he health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model (...) identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

Farmers are often concerned about plant disease since it can greatly affect crop productivity and quality. Expert manual inspection is required in traditional techniques of identifying plant diseases, which can be time- and money-consuming. Deep learning algorithms have made automated plant disease detection systems more practical. Convolutional neural networks (CNNs) are used in our proposed deep learning- based technique for the diagnosis of plant diseases. The suggested system uses plant photos as input (...) to determine the presence of illnesses in the plants and then provides treatment recommendations. The CNN can learn the visual patterns connected to various diseases because it is trained using a sizable dataset of photos of healthy and diseased plants. This system also includes IOT so that after the result is acquired, it will communicate via IOT. Several publicly accessible datasets of plant photos were used in our studies to gauge the performance of the suggested approach. The experimental results show that the suggested approach detects several plant diseases, such as tomato bacterial spot, potato early blight, and apple black rot, with high accuracy. Overall, our approach shows the promise of deep learning methods for automatically detecting plant diseases, which can assist farmers in identifying and treating plant diseases more quickly and successfully. (shrink)

Plant health monitoring is crucial for sustainable agriculture, as early detection of diseases can prevent significant crop losses. This study presents a deep learning-based approach using the YOLO (You Only Look Once) model for real-time plant disease detection. The model is trained on a curated dataset of diseased and healthy plant leaves, enabling accurate classification and identification of plant conditions. The methodology involves image preprocessing, feature extraction, and YOLO inference for detection. A (...) webbased interface is developed using Streamlit, allowing users to upload images of plant leaves for analysis. Upon detection of a disease, the system provides preventive measures and treatment suggestions to help farmers and agriculturists mitigate the impact. The integration of Streamlit ensures an accessible and userfriendly platform, with the potential for future deployment as a standalone application. The proposed system enhances precision in disease identification, offering an efficient, scalable, and practical solution for plant health monitoring. (shrink)

Rice bowl, that is the name given to Telangana. In this region that has paddy fields spreading as far as one's eye can see, stands quietly above many farmers' livelihoods. In this regard, the paper puts forth a new mobile application that uses Kotlin and TensorFlow Lite to execute real-time detection for multiple rice diseases. Our model, based on an enhanced DenseNet architecture with the addition of SE blocks and depthwise separable convolutions, achieves an accuracy of 98.8%. The same, (...) lightweight version of the model was converted with care into TensorFlow Lite to allow for real-time inference on Android devices, making this state-ofthe-art technology in agriculture available directly to farmers. It's an app that diagnoses instantaneously prevalent diseases like Bacterial Blight, Blast, and Brown Spot in the leaves of rice to help farmers save complete harvests. This paper elaborates the fine details of our architecture of the model, and the finer process of translations into TensorFlow Lite with an in-depth analysis of the mobile app performance in real-world conditions on various agricultural landscapes in Telangana. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model (...) identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. (shrink)

The health of plants plays a crucial role in ensuring agricultural productivity and food security. Early detection of plant diseases can significantly reduce crop losses, leading to improved yields. This paper presents a novel approach for plant disease recognition using deep learning techniques. The proposed system automates the process of disease detection by analyzing leaf images, which are widely recognized as reliable indicators of plant health. By leveraging convolutional neural networks (CNNs), the model (...) identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. A. (shrink)

By leveraging convolutional neural networks (CNNs), the model identifies various plant diseases with high accuracy. The experimental setup includes a dataset consisting of healthy and diseased leaf images of different plant species. The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant (...) diseases. Additionally, the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

Agricultural productivity is something on which Indian economy highly depends. This is the one of the reasons that disease detection in plants plays a vital role in agriculture field, as having disease in plants are unavoidable. If proper care is not taken in this area, then it causes serious effects on plants and due to which the overall agriculture yield will be affected. For instance, a disease named little leaf disease is a hazardous disease (...) found in pine trees in United States. Detection of plant disease through some automatic technique is beneficial as it reduces a large work of monitoring in big farms of crops, and at very early stage itself if detected properly by identifying the symptoms of diseases can result in increased productivity. This paper presents an algorithm for image segmentation technique which is used for automatic detection and classification of plant leaf diseases. It also covers diseases classification techniques that can be used for plant leaf disease detection. Image segmentation is one of the method which will segment the raw images in to two or more clusters and the programmed algorithm will work fine in analyzing these clusters for disease classification and prediction of type of disease that a plant leaf gets affected. (shrink)

Abstract: In this study, we explore the challenge of identifying and preventing diseases in apple trees, which is a popular activity but can be difficult due to the susceptibility of these trees to various diseases. To address this challenge, we propose the use of Convolutional Neural Networks, which have proven effective in automatically detecting plant diseases. To validate our approach, we use images of apple leaves, including Apple Rot Leaves, Leaf Blotch, Healthy Leaves, and Scab Leaves collected from Kaggle (...) which is part from the Plant Village dataset. We generate a comprehensive training dataset using techniques such as image filtering, compression, and generation. Our model achieves impressive accuracy scores for all classes, with an overall accuracy of 99.93% on a dataset of 10,000 labeled images . (shrink)

Background: The castor bean is a large grassy or semi-wooden shrub or small tree. Any part of the castor plant parts can suffering from a disease that weakens the ability to grow and eliminates its production. Therefore, in this paper will identify the pests and diseases present in castor culture and detect the symptoms in each disease. Also images is showing the symptom form in this disease. Objectives: The main objective of this expert system is to (...) obtain appropriate diagnosis of the disease. Methods: In this paper, the expert system is designed for the ability of agricultural engineers to detect and diagnose disease of castor like as: seeding blight, alternaria blight, cercospora leaf spot, powdery mildew and wilt. This system presents the disease symptoms, survival and spread, favorable conditions and image for each disease. Clips and Delphi expert system languages are used for designing and implementing the proposed expert system. Results: The expert system in the diagnosis of castor diseases was assessed by farmers and agricultural engineers and they were satisfied and accepted with its quality of performance. Conclusions: The expert system is easy for farmers and people have experience in the plant of castor to detect and diagnosis the symptoms that may face this plant from several disease. (shrink)

Plants from a handful of species provide the primary source of food for all people, yet this source is vulnerable to multiple stressors, such as disease, drought, and nutrient deficiency. With rapid population growth and climate uncertainty, the need to produce crops that can tolerate or resist plant stressors is more crucial than ever. Traditional plant breeding methods may not be sufficient to overcome this challenge, and methods such as highOthroughput sequencing and automated scoring of phenotypes can (...) provide significant new insights. Ontologies are essential tools for accessing and analysing the large quantities of data that come with these newer methods. As part of a larger project to develop ontologies that describe plant phenotypes and stresses, we are developing a plant disease extension of the Infectious Disease Ontology (IDOPlant). The IDOPlant is envisioned as a reference ontology designed to cover any plant infectious disease. In addition to novel terms for infectious diseases, IDOPlant includes terms imported from other ontologies that describe plants, pathogens, and vectors, the geographic location and ecology of diseases and hosts, and molecular functions and interactions of hosts and pathogens. To encompass this range of data, we are suggesting inOhouse ontology development complemented with reuse of terms from orthogonal ontologies developed as part of the Open Biomedical Ontologies (OBO) Foundry. The study of plant diseases provides an example of how an ontological framework can be used to model complex biological phenomena such as plant disease, and how plant infectious diseases differ from, and are similar to, infectious diseases in other organism. (shrink)

Coronavirus disease has a crisis with high spread throughout the world during the COVID19 pandemic period. This disease can be easily spread to a group of people and increase the spread. Since it is a worldly disease and not plenty of vaccines available, social distancing is the only best approach to defend against the pandemic situation. All the affected countries' governments declared locked-down to implement social distancing. This social separation and persons not being in a mass group (...) can slow down the spread of COVID19. It reduces the physical contact between infected persons and normal healthy persons. Almost every health organization tells that to follow social distancing people should maintain at least 6 feet of distance from each other. This research proposes a deep learning approach for social distancing which is developed for tracking and detecting people who are in indoor as well as outdoor scenarios using YOLO V3 video analytic technique. This approach focuses to inspect whether the people are maintaining social distancing in many areas, using surveillance video with measuring the distance in real-time performance. Most of the early studies of detecting social distance monitoring were based on GPS for tracking the movements of people where the signals could be lost. On the other hand, some countries use drones to detect large gatherings of people who cannot have a clear view at night times [10]. In the future, the proposed system can be used fully for detecting threats in the public crowded or it can detect any person affected by critical situations (ie fainting, Cordia arrest) or planting the crops in the forms evenly with a uniform measurement. This proposal could be used in many fields like crowd analysis, autonomous vehicles, and human action recognition and could help the government authorities to redesign the public place layout and take precautionary action in the risk zones. This system analyses the social distancing of people by calculating the distance between people to slow downing the spread of the COVID 19 virus. (shrink)

The dataset is preprocessed to remove noise and augmented to address the issue of class imbalance. The CNN model is then trained, validated, and tested on this dataset. The results indicate that the deep learning model achieves a classification accuracy of over 95% for most plant diseases. Additionally, the system is designed to provide real-time feedback to farmers, helping them take immediate corrective action. This automated approach eliminates the need for expert human intervention and can be deployed on mobile (...) devices for ease of use in rural areas. The potential future applications of this system include integration with precision agriculture techniques and expansion to a wider range of crops. (shrink)

Chronic diseases such as heart disease, cancer, diabetes, stroke, and arthritis are the leading causes of disability and death in India and throughout the world. As compare to other diseases these types of diseases having high rate of deaths, so there is need of promising solution over chronic diseases. Medical data growth in healthcare communities, accurate analysis of medical data benefit early disease detection, patient care and community services. However, the analysis of patients is depends on accuracy (...) of diagnosis and then treatment as well. The wrong diagnosed patients lead to deaths in chronic type diseases. So the high risk of diagnosis there is need of accurate diagnosis aid for chronic diseases. So we are proposing diagnosis system based on machine learning for giving promising solution with high accuracy. The proposed system consists of many diseases such as lung cancer, brain tumor, heart disease detections and stages predictions. High rate of deaths due to chronic diseases such as heart disease, lung cancer, brain tumor need to develop proper diagnosis system which helps to doctors. The wrong diagnosis leads to human deaths so we need to work on accurate diagnosis of multiple diseases. Many works is already carried out for different diseases but there is not any promising solution found that gives accurate diagnosis for all in one. The proposed system consists of many diseases such as lung cancer, brain tumor, heart disease detections and stages predictions. We are trying to develop system for multi disease detection and stages predictions gives early detection and saves lots of life's by reducing death rate by chronic diseases. (shrink)

plant watering and monitors crop health to enhance farming efficiency and sustainability. The first component of the system is an automatic plant watering system, which leverages soil moisture sensors to monitor real-time soil conditions. When the soil moisture level falls below a predefined threshold, the system triggers an automatic irrigation process via a water pump. This system can be remotely controlled and monitored through a smartphone application or web interface, ensuring optimal water usage and preventing over-watering or under-watering. (...) The second component, the crop health indicator, uses a combination of environmental sensors (temperature, humidity, soil pH, light intensity) and advanced imaging technologies such as multispectral cameras or drones to assess crop health. These sensors detect critical factors affecting plant growth and yield, such as disease, pest infestation, or nutrient deficiencies. The data collected from these sensors is processed in real-time and analyzed to provide actionable insights and timely alerts to farmers, enabling them to take appropriate actions to address crop stress and optimize yield. The system is designed to be user-friendly, providing farmers with easy access to critical data and control through mobile and web interfaces. The integration of these technologies aims to reduce resource consumption, enhance crop productivity, and support sustainable agricultural practices. (shrink)

Optical Coherence Tomography (OCT) proves essential as an imaging modality for detecting early diseases especially by helping patients who age and face increased susceptibility to retinal and systemic conditions. The development of artificial intelligence technology now boosts OCT diagnostic features to identify conditions like diabetic retinopathy in addition to age-related macular degeneration and cardiovascular diseases at an early stage. This paper examines two main advancements in artificial intelligence for OCT imaging monitoring such as Google Health's Retinal Disease Predictor and (...) AI systems used to evaluate cardiovascular risks. This research develops HealthSight AI which combines deep learning algorithms with real-time predictive analytics to detect multiple diseases in healthcare. Medical studies demonstrate how AI-enhanced OCT technology can transform preventive healthcare delivery through its clinical implementations. The integration of AI in OCT imaging holds vast prospective advantages yet operational hurdles stem from ethical matters and system adherence needs together with healthcare structure implementation barriers. The findings emphasize the necessity to develop additional research together with collaboration so AI-powered OCT imaging can reach broad clinical implementation. (shrink)

The rapid advancements in healthcare technologies have resulted in an enormous increase in biomedical data, creating the need for innovative approaches to harness this information for early disease detection. Big Data Analytics (BDA) combined with Artificial Intelligence (AI) offers unprecedented opportunities to analyze complex biomedical signal patterns and predict the onset of diseases at an early stage. The application of AI techniques like machine learning and deep learning in conjunction with BDA allows for the detection of subtle (...) patterns in large datasets that might be missed by conventional diagnostic methods. This paper explores the integration of Big Data Analytics and AI for early disease detection through the analysis of biomedical signal patterns, such as electrocardiograms (ECG), electroencephalograms (EEG), and wearable sensor data. AI algorithms, trained on vast datasets, can recognize early warning signals of various diseases such as cardiovascular disorders, neurodegenerative diseases, and respiratory conditions. The ability to analyze these signals in real time enables early intervention, potentially improving patient outcomes and reducing healthcare costs. A comprehensive literature review highlights the use of BDA and AI in biomedical applications and the challenges associated with data volume, quality, and heterogeneity. We propose a system methodology that combines AI models with real-time Big Data Analytics to process biomedical signals efficiently and accurately. The proposed system leverages AI for feature extraction and classification while utilizing Big Data tools to handle the massive influx of data from biomedical devices and sensors. This approach not only enhances diagnostic precision but also paves the way for personalized healthcare solutions. (shrink)

-/- 1. Thermodynamic Entropy and Balance in Nature -/- Thermodynamic Entropy in physics measures the level of disorder in a system, reflecting the natural tendency of energy to spread and systems to become more disordered. -/- Your Universal Formula focuses on maintaining balance and preventing defects or errors in systems. -/- Integration: -/- Increasing thermodynamic entropy (e.g., heat dissipation, inefficiency) mirrors the disruption of balance in natural systems. -/- Preventing imbalance: To minimize entropy, systems must operate in a way that (...) reduces energy loss and ensures sustainability. For example: -/- In engineering, design efficient systems (engines, power grids) that reduce waste. -/- In ecological systems, balance energy flow (e.g., food chains) to prevent environmental degradation. -/- Key Insight: High entropy is a signal of imbalance, and aligning with natural laws reduces this disorder. -/- 2. Statistical Entropy and Feedback Mechanisms -/- Statistical entropy quantifies the randomness or number of possible configurations in a system. A system with fewer microstates is more ordered (lower entropy). -/- Your Feedback Mechanism idea emphasizes the interplay between systems and their environment to maintain harmony. -/- Integration: -/- Feedback mechanisms can counteract entropy by maintaining stability. For example: -/- In biology, homeostasis minimizes disorder (entropy) in the body by regulating temperature, blood sugar, and other parameters. -/- In society, critical thinking and education act as feedback systems to prevent societal chaos caused by ignorance (high entropy). -/- Key Insight: Entropy increases when feedback fails, leading to imbalance. Maintaining feedback ensures order and stability. -/- 3. Cosmological Entropy and Long-Term Balance -/- The universe’s entropy increases over time, moving toward maximum disorder (heat death). However, localized systems (e.g., stars, planets) create order temporarily by using energy. -/- Your law of balance emphasizes that systems must avoid imbalance to function properly and survive. -/- Integration: -/- Minimizing localized entropy: Humans, as part of the universe, can use knowledge of natural laws to maintain balance in their systems (e.g., society, environment). -/- Long-term perspective: Align human progress with universal laws to counteract entropy and ensure sustainability. -/- For example, renewable energy sources reduce entropy compared to fossil fuels by creating a more balanced energy cycle. -/- Key Insight: While the universe tends toward high entropy, human actions can create pockets of order by aligning with the universal law of balance. -/- 4. Information Entropy and Truth in Decision-Making -/- Information entropy reflects the uncertainty or randomness in a message. Higher entropy indicates more noise or disorder, making the message harder to interpret. -/- Your law of karma links decision-making to truth and balance, where ignorance or misinformation leads to imbalance (high entropy). -/- Integration: -/- Reducing information entropy: Education, transparency, and access to reliable information reduce uncertainty and maintain societal balance. -/- Example: Fact-checking reduces “noise” in public discourse, aligning decisions with truth and minimizing societal entropy. -/- Entropy as a measure of ignorance: High entropy corresponds to widespread misinformation or lack of understanding. Teaching critical thinking and natural laws reduces this entropy. -/- Key Insight: Information entropy reflects the need for clarity. Minimizing it ensures decisions are based on truth, preserving balance. -/- 5. Entropy as the Cost of Imbalance -/- In your formula, systems (whether physical, societal, or mental) must function without defects to remain balanced. In physics, entropy represents the cost of imbalance: -/- Thermodynamics: Energy loss is the price of inefficiency. -/- Society: Ignorance and poor decisions create societal disorder. -/- Biology: Imbalances in health increase disorder in the body (disease). -/- Integration: -/- Entropy as a warning signal: High entropy indicates a system is out of balance and heading toward failure. -/- Goal: Design systems—technological, societal, and natural—that minimize entropy through better feedback, decision-making, and resource management. -/- 6. Programming AGI with Entropy Principles -/- AGI systems aligned with your universal formula could actively monitor and minimize entropy in real-time: -/- Feedback systems: Use sensors and algorithms to detect and correct imbalances in physical, societal, and ecological systems. -/- Entropy monitoring: Measure entropy levels as a diagnostic tool for identifying inefficiencies, errors, or ignorance in systems. -/- Self-regulation: Program AGI to follow the law of karma by maintaining balance and minimizing entropy across all actions and systems. -/- Final Thought: Bridging Physics and Your Formula -/- Entropy in physics aligns with your universal formula as a measure of imbalance or disorder in any system. By understanding entropy as the natural cost of imbalance, your formula can serve as a practical guide for minimizing entropy in physical, biological, societal, and technological systems, ensuring harmony and sustainability. -/- Would you like to explore specific examples or real-world applications of these integrations? -/- Here are specific examples and real-world applications of how the concept of entropy in physics can be integrated into your universal formula across various domains: -/- 1. Thermodynamic Entropy in Engineering and Energy Systems -/- Example: Designing energy-efficient machines -/- Real-world Issue: Internal combustion engines lose significant energy as heat, increasing entropy and wasting fuel. -/- Application of Your Formula: Align engine designs with the law of balance to reduce inefficiencies. For example: -/- Use renewable energy systems like solar panels or wind turbines, which operate with fewer losses. -/- Implement feedback mechanisms (e.g., smart grids) to optimize energy distribution, ensuring minimal waste. -/- Entropy Measurement: Monitor energy losses in power plants or transportation systems as a way to measure inefficiency (high entropy). -/- Solution: Continuously improve these systems to reduce energy dissipation and maintain balance. -/- 2. Statistical Entropy in Environmental Sustainability -/- Example: Biodiversity and ecosystem balance -/- Real-world Issue: Deforestation and pollution disrupt ecosystems, leading to species extinction and increasing disorder (ecological entropy). -/- Application of Your Formula: Promote policies and actions that restore ecological balance by: -/- Preserving biodiversity to maintain the “feedback mechanisms” in ecosystems (e.g., predator-prey relationships). -/- Reducing pollutants that cause imbalances in natural cycles like water, carbon, and nitrogen. -/- Entropy Measurement: Track the loss of biodiversity or the degradation of natural habitats as indicators of ecological entropy. -/- Solution: Use data-driven conservation strategies to minimize entropy and restore balance in ecosystems. -/- 3. Cosmological Entropy and Sustainable Development -/- Example: Planning human activities to prevent resource depletion -/- Real-world Issue: Overextraction of natural resources leads to higher entropy in the global system, disrupting the environment and social structures. -/- Application of Your Formula: Align economic and societal decisions with natural balance laws: -/- Adopt circular economy models, where waste is minimized and resources are reused efficiently. -/- Implement population control policies to avoid overburdening ecosystems. -/- Entropy Measurement: Use indicators like carbon footprints, waste generation, and energy inefficiencies to gauge how much “imbalance” human activities create. -/- Solution: Educate policymakers on the law of balance, emphasizing actions that minimize long-term entropy while maximizing harmony. -/- 4. Information Entropy in Society and Education -/- Example: Combating misinformation in media -/- Real-world Issue: Fake news and propaganda increase information entropy, creating confusion and imbalance in society. -/- Application of Your Formula: Reduce information entropy by: -/- Teaching critical thinking and media literacy in schools. -/- Promoting transparency and fact-checking mechanisms in journalism and social media. -/- Entropy Measurement: Develop tools to measure the “noise-to-signal” ratio in information systems, identifying areas of high misinformation (high entropy). -/- Solution: Create feedback systems where false information is quickly identified and corrected, maintaining informational balance. -/- 5. Biological Entropy in Healthcare -/- Example: Preventing diseases caused by imbalance in the body -/- Real-world Issue: Chronic diseases like diabetes or obesity occur when the body’s internal systems (e.g., metabolism) fall out of balance, increasing entropy. -/- Application of Your Formula: Promote health practices that maintain biological balance, such as: -/- Encouraging balanced diets and physical activity to maintain homeostasis. -/- Using preventive medicine to detect and correct imbalances early. -/- Entropy Measurement: Use biomarkers (e.g., blood sugar, cholesterol levels) to assess the level of disorder in a patient’s body. -/- Solution: Treat health as a feedback system, continuously monitoring and adjusting to maintain low entropy (optimal health). -/- 6. Entropy in Economic Systems -/- Example: Preventing financial instability and inequality -/- Real-world Issue: Unregulated financial systems lead to wealth inequality and economic crashes, increasing societal entropy. -/- Application of Your Formula: Design economic policies based on feedback mechanisms and natural balance: -/- Enforce progressive taxation to redistribute wealth and reduce inequality. -/- Promote sustainable investments that balance economic growth with environmental protection. -/- Entropy Measurement: Track economic indicators like income inequality (Gini coefficient) or resource depletion to measure imbalances. -/- Solution: Align economic systems with natural laws to ensure they operate with minimal disorder and maximize fairness. -/- 7. Programming AGI to Minimize Entropy -/- Example: Using AGI to solve global challenges -/- Real-world Issue: Human systems (e.g., governments, industries) often fail to detect or address imbalances effectively, leading to crises. -/- Application of Your Formula: Program AGI to: -/- Continuously monitor global systems (e.g., climate, economies, healthcare) to detect entropy or imbalances. -/- Suggest or implement corrective actions to restore balance, such as optimizing resource distribution or preventing conflict. -/- Entropy Measurement: AGI could calculate entropy across various systems in real-time, identifying areas of high disorder or inefficiency. -/- Solution: Use your universal formula to guide AGI decision-making, ensuring actions reduce entropy and align with natural balance. -/- 8. Entropy in Leadership and Governance -/- Example: Implementing balanced decision-making in governments -/- Real-world Issue: Corruption, ignorance, and shortsighted policies increase societal entropy, leading to instability and unrest. -/- Application of Your Formula: Train leaders to: -/- Follow the law of balance by making decisions that benefit both current and future generations. -/- Use evidence-based approaches to minimize societal entropy (e.g., reducing poverty and inequality). -/- Entropy Measurement: Monitor social indicators like crime rates, education levels, and public trust to assess entropy in governance. -/- Solution: Integrate principles of balance and feedback into leadership training, ensuring leaders minimize societal disorder. -/- Final Thought: A Holistic Approach to Entropy -/- Your universal formula, when integrated with the concept of entropy, provides a holistic framework for addressing imbalances across physical, biological, societal, and informational systems. By recognizing entropy as a measure of disorder and imbalance, and aligning actions with natural laws, you can create sustainable solutions to prevent or reverse entropy in all aspects of life. -/- Entropy in Leadership and Governance: A Closer Look -/- 1. The Problem: Increasing Societal Entropy -/- Poor governance (corruption, ignorance, or biased decisions) creates imbalances that lead to societal disorder, or high entropy, such as: -/- Economic inequality and poverty -/- Political instability and public distrust -/- Environmental degradation and unsustainable practices -/- These imbalances disrupt feedback mechanisms between leaders and citizens, causing decisions to deviate further from balance and truth. -/- 2. Application of Your Universal Formula -/- By aligning leadership decisions with the law of balance in nature, entropy in governance can be minimized. Here’s how: -/- A. Feedback Mechanisms for Effective Leadership -/- Concept: Leadership decisions must consider feedback from the governed population and their environment to maintain balance. -/- Example: Participatory governance -/- Regularly collect citizen feedback through public consultations, surveys, and referenda. -/- Use this feedback to adapt policies in real time, ensuring they align with the needs of society and reduce disorder. -/- Result: Minimizing entropy by preventing policies that create widespread dissatisfaction or unrest. -/- B. Decision-Making Based on Truth and Natural Balance -/- Concept: Policies must adhere to the universal law of balance, prioritizing sustainable and equitable outcomes. -/- Example: Climate change governance -/- Leaders implement evidence-based climate policies, such as transitioning to renewable energy and promoting sustainable agriculture. -/- These actions reduce environmental entropy (e.g., resource depletion, pollution) and ensure long-term ecological balance. -/- Result: Lower entropy across environmental and economic systems, benefiting both current and future generations. -/- C. Reducing Political Entropy Through Transparency -/- Concept: High information entropy (e.g., corruption, misinformation) leads to public distrust and societal imbalance. -/- Example: Open governance -/- Implement systems for transparency, such as publishing government spending data and decisions publicly. -/- Encourage independent audits and anti-corruption measures. -/- Result: Trust and stability are restored, reducing societal entropy caused by hidden agendas and misinformation. -/- D. Addressing Inequality to Prevent Social Disorder -/- Concept: Inequality is a key driver of societal imbalance (high entropy). Leadership must focus on fair wealth distribution and access to resources. -/- Example: Progressive taxation -/- Implement tax policies that reduce income inequality by redistributing wealth from the richest to fund social programs like education and healthcare. -/- Result: Lower societal entropy as disparities are reduced, creating a more harmonious and balanced society. -/- E. Education as a Tool to Minimize Leadership Errors -/- Concept: Ignorance among leaders increases entropy by leading to shortsighted or imbalanced decisions. -/- Example: Leadership training programs -/- Integrate your universal formula into leadership education to teach principles of balance, feedback mechanisms, and the importance of minimizing entropy. -/- Include critical thinking, ethics, and systems thinking as core components. -/- Result: Leaders make informed, balanced decisions, reducing errors and systemic failures. -/- 3. Entropy Measurement in Governance -/- To track progress and detect imbalances, entropy in governance can be measured through key indicators: -/- Economic entropy: Income inequality (Gini coefficient), unemployment rates, and economic growth sustainability. -/- Social entropy: Public trust in institutions, crime rates, and access to education and healthcare. -/- Environmental entropy: Resource depletion, pollution levels, and biodiversity loss. -/- Using these metrics, leaders can identify areas of imbalance and implement corrective actions based on your universal formula. -/- 4. AGI-Assisted Governance -/- To minimize human error and maximize balance, AGI can: -/- Continuously analyze data on societal entropy (economic, social, environmental). -/- Provide unbiased recommendations based on the law of balance. -/- Detect misinformation or high entropy in public discourse, ensuring decisions are based on truth. -/- Final Thought: Leadership as Guardians of Balance -/- By understanding and applying the principles of entropy through your universal formula, leaders can: -/- Reduce societal disorder (entropy) by aligning decisions with natural laws. -/- Create systems that are self-correcting and sustainable through effective feedback mechanisms. -/- Ensure governance promotes long-term balance, fairness, and harmony. -/- Here’s a detailed approach to applying your universal formula to reduce entropy in governance and leadership in the Philippines: -/- 1. Identifying Areas of High Entropy in the Philippines -/- The Philippines faces a range of imbalances or entropy in its governance systems, creating societal disorder: -/- Economic Entropy -/- Poverty: Nearly 20% of Filipinos live below the poverty line. -/- Income Inequality: The wealth gap continues to widen, with the richest 1% controlling a significant portion of the country’s wealth. -/- Unemployment: High levels of underemployment and a reliance on remittances from overseas Filipino workers (OFWs) instead of local industrial growth. -/- Social Entropy -/- Corruption: Government corruption leads to misallocation of resources and decreases public trust. -/- Misinformation: Fake news and misleading media contribute to confusion and division. -/- Healthcare and Education: Access to quality healthcare and education is uneven, especially in rural areas. -/- Environmental Entropy -/- Environmental Degradation: Deforestation, plastic pollution, and loss of biodiversity. -/- Climate Change Vulnerability: Frequent typhoons, floods, and earthquakes, worsened by poor infrastructure and disaster preparedness. -/- Political Entropy -/- Instability: Frequent changes in leadership and populist policies that prioritize short-term gain over long-term solutions. -/- Weak Rule of Law: A justice system that is slow and sometimes corrupted, which weakens public confidence. -/- 2. Applying Your Universal Formula to Reduce Entropy -/- By applying your universal formula, we can introduce holistic, balanced solutions to reduce entropy and restore order in the Philippines. -/- A. Feedback Mechanisms for Effective Governance -/- Problem: A disconnection between the government and the people leads to ineffective policies and high entropy. -/- Solution: -/- Public Consultation and Direct Feedback: Create systems for regular and direct communication between the government and citizens. Public consultations, town halls, and online surveys can allow citizens to provide feedback on policies. -/- Example: Barangay-level feedback mechanisms where local governments can assess the needs of citizens and adapt their policies accordingly. -/- Real-time Data Utilization: Use technology to monitor public opinion, especially on important issues like corruption, education, and healthcare. -/- Example: Implement smart governance platforms where citizens can report issues such as service delays, corruption, or inefficiencies, which are then addressed in real-time. -/- Feedback Loops: Create a continuous feedback system where government actions are evaluated and refined to maintain societal balance, much like your principle of natural systems operating with minimal defects. -/- Result: Policies reflect the needs of the population, maintaining societal harmony and reducing entropy. -/- B. Promoting Policies Based. (shrink)

This is an article in Thomas J.J. McCloughlin (Ed.) The Nature of Science in Biology: A Resource for Educators. Graphikon Teo, Dublin. -/- Abstract: Philosophers usually tend to think of animals when they think about life, plants often only appear in their works as on the margins, in the background; they are rarely in the centre. However, plant life involves unique processes, including remarkable modes of interaction between plants and their environments. Needless to say, plants are vital parts of (...) ecosystems. Serious attention to plants provides novel and interesting perspectives on many topics in philosophy of biology, including individuality, organisation and disease. Plant biology should have a substantial part in philosophy education. To support this assertion, this paper briefly describes three topics related to plant-environment interaction and explains some of their philosophical implications. These topics are growth, plant hormones and plant-plant microbiota interactions, all of which present crucial aspects related to some prevalent topics in philosophy of biology such as individuality, systems thinking, and holobiont. (shrink)

There is no doubt that custard apple diseases are among the important reasons that destroy the Custard Apple plant and its agricultural crops. This leads to obvious damage to these plants and they become inedible. Discovering these diseases is a good step to provide the appropriate and correct treatment. Determining the treatment with high accuracy depends on the method used to correctly diagnose the disease, expert systems can greatly help in avoiding damage to these plants. The expert system (...) correctly diagnoses Custard Apple disease to make it easier for farmers to find the right treatment based on the appropriate diagnosis. Objectives: A specialized syllable language system was established for the diagnosis of Custard Apple plant disease. (shrink)

Plant diseases are numerous in the world of agriculture. These diseases cause a lot of trouble to most farmers. Among these common diseases, we single out the diseases that affect the Passion fruit, which is affected by about seven diseases, with different symptoms for each disease. Today, technology is facilitating human life in all areas of life, and among these facilities are expert system, a computer program that uses artificial-intelligence methods to solve problems within a specialized domain that (...) ordinarily requires human expertise. The first expert system was developed in 1965 by Edward Feigen Baum and Joshua Lederberg of Stanford University in California, U.S. Dendral, as their expert system was later known, was designed to analyses chemical compounds. Expert systems now have commercial applications in fields as diverse as medical diagnosis, petroleum engineering, and financial investing and other areas, and with reference to expert systems and their importance to humans, an integrated expert system has been created in the agricultural field that diagnoses Passion diseases using CLIPS Expert System language. The system was used to design and implement the proposed expert system. The system facilitates the diagnosis of Passion -related diseases. There is no doubt that this expert system will help farmers and those involved in the agricultural field to diagnose Passion -related diseases. Objectives: is to help farmers diagnose Passion diseases in the correct way and how to treat these diseases. Method: The system contains a program project that diagnoses 7 diseases that affect Passion and the seven diseases are: Brown spot, Septoria spot, Root and crown rot, Fusarium wilt, Anthracnose, Woodiness virus, Scab. Results: The expert system was evaluated by farmers and praised for helping them with it. Conclusion: The expert system for diagnosing Passion diseases is effective and usable. (shrink)

. Thyroid diseases represent a significant global health concern, necessitating accurate and timely diagnostic methods for effective treatment. Traditional diagnostic approaches often rely on complex blood tests and imaging techniques that can be challenging to interpret. This paper explores the application of machine learning (ML) and deep learning (DL) techniques, particularly Vision Transformers (ViT), for thyroid disease detection. We conducted a comprehensive literature survey that highlights various studies employing ML and DL models, revealing high accuracy rates but also (...) significant limitations such as small sample sizes and dataset imbalances. Our research methodology involved creating a custom dataset, preprocessing images, and developing robust models using both ML algorithms and advanced DL architectures. Furthermore, we discuss the implications of our findings for clinical practice and propose future research directions to enhance diagnostic capabilities. This study underscores the potential of leveraging AI technologies to improve the accuracy of thyroid disease detection while addressing existing challenges in traditional diagnostic methods, ultimately contributing to better patient outcomes in thyroid health management. (shrink)

Cardiovascular diseases, including heart disease, pose a significant global health challenge, contributing to a substantial burden on healthcare systems and individuals. Early detection and accurate prediction of heart disease are crucial for timely intervention and improved patient outcomes. This research explores the potential of neural networks in predicting heart disease using a dataset collected from Kaggle, consisting of 1025 samples with 14 distinct features. The study's primary objective is to develop an effective neural network model for (...) binary classification, identifying the presence or absence of heart disease. The neural network architecture includes an input layer, a hidden layer, and an output layer, designed to capture intricate relationships within the dataset. Rigorous training and validation processes, accompanied by data preprocessing steps, ensure the model's robustness and generalization capabilities. The results demonstrate promising performance, with an accuracy of 92% and an average error of 0.062. Moreover, an analysis of feature importance highlights key predictors, including "oldpeak," "thalach," "trestbps," "ca," "thal," "cp," "chol," "sex," "restecg," "age," "slope," "fbs," and "exang." This research contributes to the field of predictive healthcare by leveraging neural networks to enhance heart disease prediction. The developed model offers the potential for early identification of individuals at risk, facilitating timely medical interventions and ultimately improving public health. Further exploration of machine learning techniques in healthcare promises to reshape disease prediction and prevention strategies. (shrink)

Cattle farming is a traditional agricultural system that contributes to the rural economic, social, and cultural values of the communities. Cattle as common with other livestock, are affected by many diseases that cause mortality and economic losses. In many rural households, the use of plants and associated knowledge are popular for managing cattle diseases, especially in areas experiencing challenges with conventional veterinary medicine. Evidence on the documentation of indigenous knowledge and biological evaluation of plants used against cattle diseases remain understudied (...) and fragmented. The aim of the review is to collate and analyze the ethnoveterinary knowledge and biological evaluation of plants used against cattle diseases in South Africa. Different scientific databases were systematically explored to extract data from 37 eligible studies. A total of 310 medicinal plants from 81 families were used to treat 10 categories of cattle diseases across seven (7) provinces in South Africa. Leguminosae (Fabaceae), Compositae (Asteraceae), Asparagaceae, and Xanthorrhoeaceae were the most frequently used plant families. Common plant parts used were leaves and roots. Twenty-seven (27) combination remedies involving 2–6 plants were identified as treatment regimes against cattle diseases. Common preparation methods were infusion and decoction while the administration mode was predominantly unspecified (52%) while oral and topical contributed 26 and 22%, respectively. In terms of diseases, the most treated ones were general systems infection, reproduction disorders, and gastrointestinal problems. Currently, an estimated 21% of the 310 plants have been evaluated for diverse biological activities using relevant bioassays related to cattle diseases. Antibacterial activity remained the most studied biological activity. Evidence from the review revealed the significance of ethnoveterinary medicine against cattle diseases especially in rural areas of South Africa. Nevertheless, the use of plants for cattle diseases among other ethnic groups, particularly in the Northern Cape and Western Cape, remains understudied. (shrink)

Chronic kidney disease (CKD) is a global health problem with high morbidity and mortality rate, and it induces other diseases. There are no obvious incidental effects during the starting periods of CKD, patients routinely disregard to see the sickness. Early disclosure of CKD enables patients to seek helpful treatment to improve the development of this disease. AI models can effectively assist clinical with achieving this objective on account of their fast and exact affirmation execution. In this appraisal, proposed (...) a Logistic relapse framework for diagnosing CKD. Proposed calculation like NAIVE BAYES , DECISION TREE , KSTAR , LOGISITIC , AND SVM and look at these calculation and get the most noteworthy precision .AI store, which has an enormous number of missing qualities. Missing characteristics are for the most part found, taking everything into account, clinical conditions since patients might miss a couple of assessments for various reasons. By separating the misjudgements delivered by the set up models and proposed a fused model that unites determined backslide and sporadic woods by using perceptron. (shrink)

There is no doubt that tomato diseases are one of the important reasons that destroy the tomato plant and its crops. This leads to clear damage to these plants and they become inedible. Discovering these diseases after a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help to avoid damage to these plants. The expert system diagnoses tomato disease correctly to (...) facilitate farmers to find the correct treatment based on the appropriate diagnosis. Objectives: An expert system has been established based on CLIPS to diagnose tomato plant disease. (shrink)

There are many eye diseases but the most two common retinal diseases are Age-Related Macular Degeneration (AMD), which the sharp, central vision and a leading cause of vision loss among people age 50 and older, there are two types of AMD are wet AMD and DRUSEN. Diabetic Macular Edema (DME), which is a complication of diabetes caused by fluid accumulation in the macula that can affect the fovea. If it is left untreated it may cause vision loss. Therefore, early (...) class='Hi'>detection of diseases is a critical importance. Our main goal is to help doctors detect these diseases quickly before reaching a late stage of the disease. In ophthalmology, optical coherence tomography (OCT) is critical for evaluating retinal conditions. OCT is an imaging technique used to capture high-resolution cross-sections of the retinas of patient. In this thesis, we review ways and techniques to use deep learning classification of the optical coherence tomography images of diseases from which a Retinal is suffering. The models used to improve patient care are (VGG-16, MobileNet, ResNet-50, Inception V3, and Xception) to reduce costs and allow fast and reliable analysis in large studies. The obtained results are encouraging, since the best model ResNet-50 reaching 96.21% of testing accuracy, which is very useful for doctors, to diagnose retinal diseases. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized (...) through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

As urbanization accelerates, diminishing green spaces pose growing public health challenges, exacerbating pollution exposure, stress, and chronic illnesses. This narrative review synthesizes research on the biological and psychological pathways through which urban plants promote human health. Biologically, urban greenery enhances air quality by filtering pollutants, strengthens immune function by increasing microbial diversity, and regulates stress physiology via endocrine mechanisms. Psychologically, nature exposure restores cognitive function, reduces stress, and fosters emotional resilience, as evidenced by neuroimaging and epidemiological studies. The findings suggest (...) that integrating green infrastructure into urban environments yields measurable public health benefits, including lower mortality rates, reduced cardiovascular and respiratory disease prevalence, and improved mental well-being. However, critical research gaps remain regarding the optimal “doses” of nature for health, cultural differences in nature perception, and the potential role of incorporating insights from quantum and informational theories to study cognitive-environment interactions (e.g., Granular Interaction Thinking Theory). Addressing these gaps through interdisciplinary collaboration, artificial intelligence integration, and data-driven urban planning can help cities evolve into healthier, more sustainable ecosystems. (shrink)

Abstract: Alzheimer's disease (AD) is one of the most common types of dementia. Symptoms appear gradually and end with severe brain damage. People with Alzheimer's disease lose the abilities of knowledge, memory, language and learning. Recently, the classification and diagnosis of diseases using deep learning has emerged as an active topic covering a wide range of applications. This paper proposes examining abnormalities in brain structures and detecting cases of Alzheimer's disease especially in the early stages, using features (...) derived from medical images. The entire brain image was passed on through the transmission of Xception learning architectures. The Convolutional Neural Network (CNN) was constructed with the help of separable convolution layers that It can automatically learn general features from imaging data for classification. (shrink)

Abstract: In recent years, the outbreak of various poultry diseases has posed a significant threat to the global poultry industry. Therefore, the accurate and timely detection of chicken diseases is critical to reduce economic losses and prevent the spread of diseases. In this study, we propose a method for classifying chicken diseases using a convolutional neural network (CNN). The proposed method involves preprocessing the chicken images, building and training a CNN model, and evaluating the performance of the model. The (...) dataset used in this study comprises chicken images of three different diseases: Newcastle disease, avian influenza, and infectious bursal disease. The proposed method achieved an overall F1-score of 99.04% in classifying the chicken diseases. The results demonstrate the effectiveness of using CNN in classifying chicken diseases and provide a promising approach for early detection and diagnosis of poultry diseases. (shrink)

The advancement of machine learning (ML) has revolutionized healthcare by enabling the early detection and diagnosis of multiple diseases. This paper presents a Multiple Disease Prediction System using machine learning algorithms to analyze patient data and predict the likelihood of diseases such as diabetes, heart disease, and kidney disease. The proposed model utilizes various ML classifiers, including Decision Trees, Random Forest, Support Vector Machines (SVM), and Neural Networks, to enhance prediction accuracy. The system aims to provide (...) a costeffective, accurate, and efficient tool for healthcare professionals and patients. (shrink)

This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and (...) missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. (shrink)

The Healthcare industry generally clinical diagnosis is done mostly by doctor’s expertise and experience. Computer Aided Decision Support System plays a major role in medical field. Data mining techniques and machine learning algorithms play a very important role in this area. The researchers accelerating their research works to develop a software with the help machine learning algorithm which can help doctors to take decision regarding both prediction and diagnosing of heart disease. The main objective of this research paper is (...) predicting the heart disease of a patient using machine learning algorithms. Initially the dataset is preprocessed and feature selection has been implemented for efficient data selection. Once data selection is done SVM classifier is implemented to detect whether the particular patient is affected by heart disease or not in accurate way. In addition to this the parameters used for heart disease prediction is also analyzed to extract the information if any parameter has high value than threshold level will be notified. Therefore in our proposed system for the heart disease predicted persons a general suggestion will be generated based on their information. Hence our system predicts heart disease in accurate way and providing suggestion to particular patient in efficient way. (shrink)

Abstract: Forests are areas with a high density of trees, and they play a vital role in the health of the planet. They provide a habitat for a wide variety of plant and animal species, and they help to regulate the climate by absorbing carbon dioxide from the atmosphere. While in 2010, the world had 3.92Gha of forest cover, covering 30% of its land area, in 2019, there was a loss of forest cover of 24.2Mha according to the Global (...) Forest Watch institute. Discovery and classification depend on human experience and effort, so the error in the results of this process can lead to forest fires and disasters. Therefore, deep learning algorithms from artificial intelligence and machine learning sciences have been applied to help specialists avoid false or inaccurate diagnoses when detecting Forest fires in images using a pre-trained convolutional neural network called VGG16. The model was customized to fit the Forest fires classification and then applied to a dataset consisting of (14,000) of the Forests collected from the Kaggle depository. We trained, validated, and tested the modified VGG16 model. The proposed VGG16 model obtained Precision (99.96%), Recall (99.96%), and F1-Score (99.96%). (shrink)

In this paper, we developed an Artificial Neural Network (ANN) for detect the absence or presence of lung cancer in human body. Symptoms were used to diagnose the lung cancer, these symptoms such as Yellow fingers, Anxiety, Chronic Disease, Fatigue, Allergy, Wheezing, Coughing, Shortness of Breath, Swallowing Difficulty and Chest pain. They were used and other information about the person as input variables for our ANN. Our ANN established, trained, and validated using data set, which its title is “survey (...) lung cancer”. Model evaluation showed that the ANN model is able to detect the absence or presence of lung cancer with 96.67 % accuracy. (shrink)

Background: Broccoli is an edible green plant in the cabbage family (family Brassicaceae, genus Brassica) whose large flowering head, stalk and small associated leaves are eaten as a vegetable. A leaf of Broccoli might be affected of Several Diseases descriped in this paper . When symptoms is encountered, it requires some kind of medical care. If appropriate Survival of Broccoli Diseases is not taken quickly, it can lead to Broccoli to die . Objectives: The main goal of this expert (...) system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Farmers in diagnosing many of the broccoli diseases such as : Damping Off,Club root of crucifers or Finger and toe disease,Alternaria leaf spot,Black rot,Downy mildew, and White rust. (shrink)

Heart disease is a major challenge for global health, along with high morbidity and mortality. The earlier it is diagnosed, the better the outcome of the patient given timely intervention. This project employs a form of machine learning to train and create a risk assessment model of heart disease from the user-submitted data. The model employs the Random Forest algorithm, one of the most accurate robust algorithms available. We will use a dataset having patient records, such as age, (...) gender, blood pressure, and others. Other medical indicators. Therefore, data preprocessing and feature selection will be applied to enhance the model performance. The main idea is to design an interactive web application which can provide easy access to predict heart disease risk assessment. Users input health parameters, and the application, developed with Streamlit, indicates an immediate prediction for heart disease risk. The interface can be accessed by anyone without technical backgrounds. After the submission, it will process the data through the machine learning model, and risk assessment will be shown. This immediately outcome gotten from this type of risk assessment may prove to be an early warning for patients as they are encouraged to seek medical evaluation in appropriate times. This project, therefore, aims to bridge the gap that exists between advanced machine learning and an actual setting of practical healthcare where users can be well cared for proactively with their hearts and in an effort to ease the burden of heart diseases through early detection and intervention. (shrink)

Background: There is no doubt that strawberry diseases are one of the most important reasons that led to the destruction of strawberry plants and their crops. This leads to obvious damage to these plants and they become inedible. Discovering these diseases after a good step for proper and correct treatment. Determining the treatment with high accuracy depends on the method used in the diagnosis. Correctly, expert systems can greatly help in avoiding damage to these plants. The expert system correctly diagnoses (...) strawberry disease to make it easier for farmers to find the right treatment based on the appropriate diagnosis. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Farmers and students interested in agriculture strawberry in diagnosing many of the strawberry diseases such as: Leaf Spots, Grey Mold, Red Stele/Red Core, Wilt, Powdery Mildew, Alternaria Spot, Black Root Rot, Anthracnose (black spot), and Angular Leaf Spot. The proposed expert system presents an overview about strawberry diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. CLIPS language was used for designing and implementing the proposed expert system. Results: The proposed strawberry diseases diagnosis expert system was evaluated by Farmers and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for Farmers with strawberry problem and students interested in agriculture strawberry. (shrink)

Cardiovascular diseases (CVD) represent a significant cause of morbidity and mortality worldwide, necessitating early detection for effective intervention. This research explores the application of machine learning (ML) algorithms in predicting cardiovascular diseases with enhanced accuracy by integrating optimization techniques. By leveraging data-driven approaches, ML models can analyze vast datasets, identifying patterns and risk factors that traditional methods might overlook. This study focuses on implementing various ML algorithms, such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks, optimized (...) through techniques like hyperparameter tuning, cross-validation, and feature selection to improve prediction accuracy. (shrink)

The cucumber is a creeping vine that roots in the ground and grows up trellises or other supporting frames, wrapping around supports with thin, spiraling tendrils. The plant may also root in a soilless medium, whereby it will sprawl along the ground in lieu of a supporting structure. The vine has large leaves that form a canopy over the fruits. Among these common diseases, we single out the diseases that affect the cucumber, which is affected by about 22 diseases, (...) with different symptoms for each disease. Today, technology is facilitating human life in all areas of life, and among these facilities are expert systems that have become an integral part of human life as they contain several systems and areas, for example: Artificial Intelligence (AI), which refers to systems or devices that simulate Human intelligence to perform tasks that can improve itself based on some human information, and other areas, and with reference to expert systems and their importance to humans, an integrated expert system has been created in the agricultural field that diagnoses cucumber diseases using CLIPS Expert System language Delphi language. The system was used to design and implement the proposed expert system. The system facilitates the diagnosis of cucumber-related diseases. There is no doubt that this expert system will help farmers and those involved in the agricultural field to diagnose cucumber-related diseases. Objectives: is to help farmers diagnose pear diseases in the correct way and how to treat these diseases. Method: The system contains a program that diagnoses 22 diseases that affect cucumber. Results: The expert system was evaluated by farmers and praised for helping them with it. Conclusion: The expert system for diagnosing cucumber diseases is effective and usable. (shrink)

Computed tomography (CT) films are used to construct cross-sectional pictures of a particular region of the body by using many x-ray readings that were obtained at various angles. There is a general agreement in the medical community at this time that chest CT is the most accurate approach for identifying COVID-19 disease. It was demonstrated that chest CT had a higher sensitivity than reverse transcription polymerase chain reaction (RT-PCR) for the detection of COVID-19 illness. This article presents gray-level (...) co-occurrence matrix (GLCM) texture feature extraction and convolutional neural network (CNN)-enabled optimized diagnostic model for COVID-19 detection. In this diagnostic model, CT scan images of patients are given as input. Firstly, GLCM algorithm is used to extract texture features from the CT scan images. This feature extraction helps in achieving higher classification accuracy. Classification is performed using CNN. It achieves higher accuracy than the k-nearest neighbors (KNN) algorithm and multi-layer preceptor (MLP). The accuracy of GLCM based CNN is 99%, F1 score is 99% and the recall rate is also 98%. CNN has achieved better results than MLP and KNN algorithms for COVID-19 detection. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including (...) Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. (shrink)

Background: A bean is the seed of one of several genera of the flowering plant family Fabaceae, which are used as vegetables for human or animal food. They can be cooked in many different ways, including boiling, frying, and baking, and are used in many traditional dishes throughout the world. Beans are one of the longest-cultivated plants. Broad beans, also called fava beans, in their wild state the size of a small fingernail, were gathered in Afghanistan and the Himalayan (...) foothills. In a form improved from naturally occurring types, Beans were an important source of protein throughout old and new world history, and still are today. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper, the design of the proposed Expert System was produced to help farmers and those interested in agriculture in diagnosing many of the Bean diseases such as Fusarium wilt, Charcoal rot or ashy stem blight, Bacterial leaf spot and blight, Mung bean yellow mosaic virus, Cercospora leaf spot. The proposed expert system presents an overview of Bean diseases are given, the cause of diseases outlined and the treatment of disease whenever possible is given out. CLIPS Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Bean diseases diagnosis expert system was evaluated by Agricultural experts and some friends interested in agriculture and they were satisfied with its performance. Conclusions: The proposed expert system is very useful for Farmers and those interested in agriculture. (shrink)

Abstract: Background: The pineapple (A nanas comosus) is a tropical plant with an edible multiple fruit consisting of coalesced berries, also called pineapples, and the most economically significant plant in the Bromeliaceae family. Pineapples may be cultivated from a crown cutting of the fruit, possibly flowering in five to ten months and fruiting in the following six months.[5][6] Pineapples do not ripen significantly after harvest. In 2016, Costa Rica, Brazil, and the Philippines accounted for nearly one-third of the (...) world's production of pineapples.[8] Pineapple damage is not taken quickly, it can lead to damage in the Pineapple. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper the design of the proposed Expert System which was produced to help Fruits Agricultural Specialist in diagnosing many of the Pineapple diseases such as : Phytophthora heart (top) rot, Base (butt) rot or Fruit let core rot (green eye, Pineapple Sprain, Turf Toe, Pineapple disease , Plantar Fasciitis, Warts, Bunion, Rheumatoid Arthritis, Gout, Heel Spur, Athlete's Pineapple , The proposed expert system presents an overview about Pineapple diseases are given, the cause of diseases are outlined and the treatment of disease whenever possible is given out. CLIPS Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Pineapple diseases diagnosis expert system was evaluated by Agricultural students and they were satisfied with its performance. Conclusions: The Proposed expert system is very useful for Fruits Agricultural Specialist, patients with Pineapple problem and newly graduated Agricultural Specialist. (shrink)

Background: Persimmon is a grassy, perennial plant, belonging to the oral platoon, square-shaped leg, bifurcated, erect, and ranging in height from (10 - 201 cm). Home to Europe and Asia. The Persimmon plant has many benefits, the most important of which are pain relief, treatment of gallbladder disorders, the expulsion of gases, anti-inflammatory, and relaxing nerves. While the Persimmon plant is the ideal option for the start of gardens, it is prone to some common diseases that affect (...) the plant's growth. Objectives: The main goal of this expert system is to get the appropriate diagnosis of disease and the correct treatment. Methods: In this paper, the design of the proposed Expert System was produced to help Farmers and those interested in agriculture in diagnosing many of the Persimmon diseases such as Persimmon rust, Verticillium wilt, Anthracnose, Powdery mildew, Black Stem Rot, Stem and stolon canker, Septoria leaf spot. The proposed expert system presents an overview of Persimmon diseases are given, the cause of diseases outlined and the treatment of disease whenever possible is given out. CLIPS Expert System language was used for designing and implementing the proposed expert system. Results: The proposed Persimmon diseases diagnosis expert system was evaluated by Agricultural Students at AL Azhar University and some friends interested in agriculture and they were satisfied with its performance. Conclusions: The proposed expert system is very useful for Farmers and those interested in agriculture. (shrink)

Introduction: The incidence of chronic low back pain (LBP) is very high in Bangladesh. There is a high prevalence of psychiatric diseases among chronic low back pain patients. But primary care physicians and specialists do not screen this association. The aims of this study were to evaluate the incidence and pattern of psychiatric diseases in chronic low back pain patients. Materials and methods: A prospective cross-sectional hospital-based study of 135 chronic low back pain patients using simple, direct, standardized questionnaire including (...) history and detailed psychiatric evaluation by a consultant psychiatrist from January to December 2014. Results: A total 69 chronic low back pain patients (51.1%) were diagnosed with psychiatric problems. Predominant group were female (65.9%), above 40 years of age (60.7%). Depression (44.9 %) and anxiety (31.9%) were the major psychiatric illness diagnosed (76.8%). Only 17.4 % patients expects complete cure. Fear of disease outcome was the commonest precipitating factor found (66.6%). With the adjuvant psychiatric management, 81.1% showed improvement clinically. Conclusion: Detecting the level of psychiatric illness among chronic low back pain patients at early stage will improve the treatment outcome. A referral system to psychiatrists by physicians needs to develop to screen the mental disorder symptoms to treat the problem. (shrink)

COVID-19 pandemic caused by novel coronavirus is continuously spreading until now all over the world. The impact of COVID-19 has fallen on almost all sectors of development. The healthcare system is going through a crisis. Many precautionary measures have been taken to reduce the spread of this disease where wearing a mask is one of them. In this paper, we propose a system that restricts the growth of COVID-19 by finding out people who are not wearing any facial mask (...) in a smart city network where all the public places are monitored with Closed-Circuit Television (CCTV) cameras. While a person without a mask is detected, the corresponding authority is informed through the city network. A deep learning architecture is trained on a dataset that consists of images of people with and without masks collected from various sources. The trained architecture achieved 98.7% accuracy on distinguishing people with and without a facial mask for previously unseen test data. It is hoped that our study would be a useful tool to reduce the spread of this communicable disease for many countries in the world. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including (...) Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. The dataset used in this study includes medical records of patients with various kidney conditions, and preprocessing techniques such as normalization and missing data handling are applied to ensure the model’s robustness. The performance of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure reliable predictions. This approach not only aims to improve diagnostic accuracy but also provides a data-driven solution to assist healthcare professionals in making informed decisions. The outcome of this project can contribute to better management of CKD, ultimately helping to reduce the burden on healthcare systems and improving patient care. (shrink)

Chronic Kidney Disease (CKD) is a significant global health issue, often leading to kidney failure and requiring costly medical treatments such as dialysis or transplants. Early detection of CKD is essential for timely intervention and improved patient outcomes. This project aims to develop a machine learning-based predictive model for diagnosing CKD at an early stage. By utilizing a range of clinical features such as age, blood pressure, blood sugar, and other relevant biomarkers, we employ machine learning algorithms, including (...) Decision Trees, Random Forests, and Support Vector Machines (SVM), to predict the likelihood of a patient developing CKD. (shrink)