Artificial Intelligence (AI) is revolutionizing the agricultural industry, offering tools to optimize crop yield predictions with high accuracy and efficiency. By leveraging machine learning (ML), deep learning (DL), remote sensing, and data analytics, farmers can make informed decisions that enhance productivity and resource use. This paper explores the integration of AI techniques in crop yield prediction, evaluates current methodologies, and proposes a hybrid model combining remote sensing data with real-time sensor inputs (...) to enhance predictive accuracy. (shrink)

Waste management and recycling are pressing global challenges due to rapid urbanization and industrialization. Traditional waste handling methods are often inefficient and unsustainable. The integration of Artificial Intelligence (AI) into waste management systems has the potential to revolutionize how waste is collected, sorted, processed, and recycled. This paper discusses the development and implementation of AI algorithms aimed at improving the efficiency of waste management and recycling processes. Emphasis is placed on image recognition for waste sorting, route optimization for collection, (...) and predictive analytics for waste generation forecasting. The study also explores real-world applications and evaluates the performance and impact of AI-driven systems. (shrink)

Abstract: Artificial Intelligence (AI) is revolutionizing the agricultural sector by enhancing productivity and sustainability. This paper explores the transformative impact of AI technologies on agriculture, focusing on their applications in precision farming, predictive analytics, and automation. AI-driven tools enable more efficient management of crops and resources, leading to improved yields and reduced environmental impact. The paper examines key AI technologies, including machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics for (...) optimizing resource use. Additionally, it addresses challenges such as data privacy, technology adoption barriers, and the ethical implications of AI in farming. By integrating AI into agricultural practices, the industry can achieve greater efficiency and sustainability, paving the way for future advancements. (shrink)

Crop diseases pose several challenges in the agricultural industry. Plant diseases can have a devastating impact on both yield and quality loss. This project presents a deep-learning based sugarcane disease classification and alert system to facilitate machine detection of disease, as well as actions to take in response to diagnosis. A dataset of images of sugarcane leaves, was modified through advanced pre-processing techniques such as cropping, rotating, image enhancement, detections edges, and adjusting for wavy images. The (...) techniques used for data augmentation helped address class imbalance as well as overfitting. The classification model implemented for this project is the EfficientNet architectures to produce better feature extraction and predictions. The classification model was trained on the pre-processed and augmented dataset and reached 97 percent accuracy on the test data after 150 epochs. The explainability of the classifying model was provided through LIME (Local Interpretable Model-Agnostic Explanations), which allowed users to see the explanation for classification for an individual image. The smart classifying system was developed as a web application (UI) and allows users to input their image to be diagnosed. Once diagnosed, the model classifies the disease and detail actions, including: recommended treatments and precautionary actions. This system shows the power of artificial intelligence to enhance management of diseases in agriculture, while providing a scalable user-friendly environment to help faciliate on farm decision-making regarding crop health and yield. (shrink)

Crop diseases pose several challenges in the agricultural industry. Plant diseases can have a devastating impact on both yield and quality loss. This project presents a deep-learning based sugarcane disease classification and alert system to facilitate machine detection of disease, as well as actions to take in response to diagnosis. A dataset of images of sugarcane leaves, was modified through advanced pre-processing techniques such as cropping, rotating, image enhancement, detections edges, and adjusting for wavy images. The (...) techniques used for data augmentation helped address class imbalance as well as overfitting. The classification model implemented for this project is the EfficientNet architectures to produce better feature extraction and predictions. The classification model was trained on the pre-processed and augmented dataset and reached 97 percent accuracy on the test data after 150 epochs. The explainability of the classifying model was provided through LIME (Local Interpretable Model-Agnostic Explanations), which allowed users to see the explanation for classification for an individual image. The smart classifying system was developed as a web application (UI) and allows users to input their image to be diagnosed. Once diagnosed, the model classifies the disease and detail actions, including: recommended treatments and precautionary actions. This system shows the power of artificial intelligence to enhance management of diseases in agriculture, while providing a scalable user-friendly environment to help faciliate on farm decision-making regarding crop health and yield. (shrink)

Abstract: Artificial Intelligence (AI) is transforming the agricultural sector, enhancing both productivity and sustainability. This paper delves into the impact of AI technologies on agriculture, emphasizing their application in precision farming, predictive analytics, and automation. AI-driven tools facilitate more efficient crop and resource management, leading to higher yields and a reduced environmental footprint. The paper explores key AI technologies, such as machine learning algorithms for crop monitoring, robotics for automated planting and harvesting, and data analytics (...) for optimizing resource use. Additionally, it discusses challenges like data privacy, barriers to technology adoption, and the ethical implications of AI in farming. Integrating AI into agricultural practices holds the promise of greater efficiency and sustainability, paving the way for future innovations. (shrink)

FarmSmart is an integrated digital agriculture platform that seeks to empower farmers with data-driven, intelligent decision-making. It brings together six must-have modules such as crop recommendation, fertilizer recommendation, crop disease forecasting, farmer-to-farmer marketplace, live commodity price tracking, and multilingual conversational chatbot into one integrated, easy-to-use solution, specifically designed for rural environments. With the combined strength of machine learning, computer vision, natural language processing, and realtime APIs of government data, FarmSmart is a holistic end-to-end solution for enabling farmers (...) right through the farming process. It includes suggesting the optimal crop in terms of environment and soil, suggesting the application of certain fertilizers, detection of crop disease using leaf image analysis through deep learning algorithms (ResNet-9), and enabling informed decisions regarding the time and place of sale of crops through live mandi price comparison. FarmSmart seeks to maximize productivity and profitability. In addition, the integrated e-commerce platform supports direct farmer-to-farmer transactions for farm produce, equipment, and produce sales and purchases, essentially doing away with intermediary functions. The platform has a multilingual AI chatbot, created by GROQ, that provides conversational assistance and voice assistance, thus improving usability for farmers from different linguistic backgrounds. Lastly, FarmSmart bridges the critical information gap in rural farming communities, increasing precision agriculture, reducing wastage of resources, and improving livelihoods through the integration of modern technology into traditional farming practices. (shrink)

Agriculture contributes a significant amount to the economy of India due to the dependence on humanbeings for their survival. The main obstacle to food security is population expansion leading to rising demand for food. Farmers must produce more on the same land to boost the supply. Through crop yield prediction, technology can assist farmers in producing more. This paper’s primary goal is to predict crop yield utilizing the variables of rainfall, crop, meteorological conditions, area, production, (...) and yield that have posed a serious threat to the long-term viability of agriculture. Crop yield prediction is a decision-support tool that uses machine learning and deep learning that can be used to make decisions about which crops to produce and what to do in the crop’s growing season. It can decide which crops to produce and what to do in the crop’s growing season. Regardless of the distracting environment, machine learning and deep learning algorithms are utilized in crop selection to reduce agricultural yield output losses. To estimate the agricultural yield, machine learning techniques: decision tree, random forest, and XGBoost regression; deep learning techniques- convolutional neural network and long-short term memory network have been used. Accuracy, root mean square error, mean square error, mean absolute error, standard deviation, and losses are compared. Other machine learning and deep learning methods fall short compared to the random forest and convolutional neural network. The random forest has a maximum accuracy of 98.96%, mean absolute error of 1.97, root mean square error of 2.45, and standard deviation of 1.23. The convolutional neural network has been evaluated with a minimum loss of 0.00060. Consequently, a model is developed that, compared to other algorithms, predicts the yield quite well. The findings are then analyzed using the root mean square error metric to understand better how the model’s errors compare to those of the other methods. (shrink)

Plant diseases pose a severe threat to global agriculture, causing up to 40% annual crop losses due to delayed and inaccurate diagnostics. The AI Plant Doctor: Leaf Disease Scanner is an innovative Android-based solution that transforms plant health management using Artificial Intelligence. By integrating Convolutional Neural Networks (CNNs) for precise disease classification, TensorFlow Lite for efficient on-device processing, and OpenCV for robust image preprocessing, the system delivers real-time diagnostics with 92% accuracy. Hosted on a user-friendly Streamlit interface, it (...) offers offline functionality, cloud-based scalability, and seamless access to curated treatment resources via Google API. This platform empowers farmers to detect diseases swiftly and adopt sustainable practices, significantly enhancing crop yields and food security. (shrink)

By Agriculture plays a major role in developing countries like India, however the food security still remains a vital issue. Most of the crops get wasted due to lack of storage facility, transportation, and plant diseases. More than 15% of the crops get wasted in India due to diseases and hence it has become one of the major concern to be resolved. There is a need of automatic system that can identify these diseases and help farmers to take appropriate steps (...) to get rid of crop loss. Farmers have followed the conventional method of identifying the plant disease with their naked eyes, and it not possible for all the farmers to identify these diseases the same way. With the advance in Artificial Intelligence, there is a need to incorporate the facilities of the computer vision in the field of agriculture. Deep Learning rich libraries and user as well as developer friendly environment to work with, all these qualities make Deep Learning as the favorable method to get started with this problem. In this paper we have used Deep Learning because of the advantages it offers to work with images especially in image classification to get improvised results. The methodology includes taking leaves of infected crops and label them as per the disease pattern. The images of infected leaves are processed pixel based operations are applied to improve the information from the image. As a next step feature extraction is done followed by image segmentation and at the last classification of crop diseases based on the patterns extracted from the diseased leaves. The CNN (Convolutional Neural Network) is used for the classification of diseases, for the demonstration purpose the public dataset is used consisting of around 87 K images (RGB type images) including healthy as well as diseased leaves. (shrink)

Agriculture is the field that assumes a significant part in improving our nation’s economy. Farming is the one that brought forth human advancement. India is an agrarian country and its economy generally dependent on crop productivity. Agriculture is the spine of all business in our country. Choosing a crop is vital in agriculture planning. The determination of crops will rely on various boundaries, for example, market value, production rate and distinctive government policies. Numerous progressions are needed in the (...) agriculture field to improve changes in our Indian economy. Improvements in agriculture can be done utilizing machine learning techniques which are applied effectively on cultivating area. Alongside all advances in the machines and innovations used in cultivating, valuable and exact data about various matters likewise assumes a huge part in it. The aim of the proposed system is to carry out the yield determination technique using Decision Tree Regressor, Random Forest with the goal that this strategy helps in taking care of numerous agriculture and farmers issues. This improves our Indian economy by expanding the yield rate of crop production. (shrink)

The Agricultural Portal is an innovative platform designed to improve crop production by providing farmers with easy access to agricultural information, resources, and tools. The portal offers a wide range of features including weather forecasts, crop shopping, crop prediction, yield prediction, crop stock and purchase History. This technical paper outlines the development and implementation of the Agricultural Portal, highlighting its features and functionalities. The paper also explores the benefits of the portal for farmers, including increased (...) productivity, improved decision-making, and enhanced profitability. Advanced machine learning models are integrated into the portal to improve its predictive capabilities. The portal is built on a robust technology platform that is scalable and adaptable to the needs of farmers of different sizes and geographies. The Agricultural Portal represents a significant step forward in the use of technology in agriculture. By providing farmers with easy access to information and resources, it has the potential to transform the way they farm and improve crop production across the globe by using different Machine Learning Algorithms. In the existing system There is only one feature but we are trying to add extra features and making into a single portal in the proposed system. (shrink)

Agriculture is the backbone of our country. Agriculture Plays an important role in economy of the country. The demand of agricultural products continuously increases with increase in population. Farmers need to think of increase in crop yield with the limited amount of land. The suicide rate is increasing with every passing year because the farmers aren’t able to get the desired price for their crops and farmer need to predict the crop before cultivating into agricultural land. Farmers (...) are not getting the proper price for which they have cultivated. Yield of the crops depends on soil parameters, rainfall, and soil moisture. Price prediction is agriculture commodity has been a major problem for the farmers. The main aim is to provide new framework and develop a system with more efficient price prediction. Using machine learning techniques, the price prediction and crop analysis can be done which reduces the human efforts. (shrink)

The healthcare industry has experienced substantive change because artificial intelligence (AI) technology enhances diagnosis procedures and treatment plans while improving patient outcomes. The research examines AI's role in precision medicine as well as other healthcare functionalities before explaining the benefits and challenges. AI technology brings substantial advancement to early disease detection and drug development and medical imaging but privacy risks and algorithm inaccuracies as well as regulatory matters remain ongoing concerns. The study examines the identified challenges while offering (...) solutions by improving data protection methods and creating bias prevention techniques with updated government criteria. Before focusing on AI system improvements in healthcare delivery and patient outreach this study demonstrates AI healthcare model performance versus standard medical intervention methods. Eventually AI technology will introduce robotic surgeries and virtual healthcare tools along with improved machine intelligence capabilities according to the research findings. Multiple regulations along with ethical guidelines require further development to enable effective implementation of AI technologies. Some lasting and ethical healthcare advances will result from implementing artificial intelligence alongside healthcare professionals within future development strategies. (shrink)

Breast cancer is the leading cause of death among women with cancer. Computer-aided diagnosis is an efficient method for assisting medical experts in early diagnosis, improving the chance of recovery. Employing artificial intelligence (AI) in the medical area is very crucial due to the sensitivity of this field. This means that the low accuracy of the classification methods used for cancer detection is a critical issue. This problem is accentuated when it comes to blurry mammogram images. In this paper, (...) convolutional neural networks (CNNs) are employed to present the traditional convolutional neural network (TCNN) and supported convolutional neural network (SCNN) approaches. The TCNN and SCNN approaches contribute by overcoming the shift and scaling problems included in blurry mammogram images. In addition, the flipped rotation-based approach (FRbA) is proposed to enhance the accuracy of the prediction process (classification of the type of cancerous mass) by taking into account the different directions of the cancerous mass to extract effective features to form the map of the tumour. The proposed approaches are implemented on the MIAS medical dataset using 200 mammogram breast images. Compared to similar approaches based on KNN and RF, the proposed approaches show better performance in terms of accuracy, sensitivity, spasticity, precision, recall, time of performance, and quality of image metrics. (shrink)

Artificial Psychology (AP) is a highly multidisciplinary field of study in psychology. AP tries to solve problems which occur when psychologists do research and need a robust analysis method. Conventional statistical approaches have deep rooted limitations. These approaches are excellent on paper but often fail to model the real world. Mind researchers have been trying to overcome this by simplifying the models being studied. This stance has not received much practical attention recently. Promoting and improving artificial intelligence (...) helps mind researchers to find a holistic model of mental models. This development achieves this goal by using multiple perspectives and multiple data sets together with interactive, and realistic models. In this book, the methodology of approximate inference in psychological research from a theoretical and practical perspective has been considered. Quantitative variable-oriented methodology and qualitative case-oriented methods are both used to explain the set-oriented methodology and this book combines the precision of quantitative methods with information from qualitative methods. This is a book that many researchers can use to expand and deepen their psychological research and is a book which can be useful to postgraduate students. The reader does not need an in-depth knowledge of mathematics or statistics because statistical and mathematical intuitions are key here and they will be learned through practice. What is important is to understand and use the new application of the methods for finding new, dynamic and realistic interpretations. This book incorporates theoretical fuzzy inference and deep machine learning algorithms in practice. This is the kind of book that we wished we had had when we were students. This book covers at least some of the most important issues in mind research including uncertainty, fuzziness, continuity, complexity and high dimensionality which are inherent to mind data. These are elements of artificial psychology. This book implements models using R software. (shrink)

The growing use of smart technology in personal grooming has resulted in novel uses such as smart mirrors to analyze the skin health and offer skincare advice. Here is a machine learning-based smart mirror framework that inspects facial skin conditions and gives individualized skincare advice. The framework employs computer vision and deep learning methods to identify and evaluate typical skin issues like acne, wrinkles, pigmentation, dryness, oiliness, and dark circles. By taking facial images with high resolution, the smart (...) mirror derives dermatological attributes and identifies skin conditions based on a CNN-driven model. The analysis is then tuned using image processing techniques and predictive analytics powered by artificial intelligence, providing high accuracy in the assessment of skin. Depending on the identified condition of the skin, the system recommends individualized skincare regimens involving appropriate products, lifestyle changes, and home remedies. The suggestions are based on a knowledge base learned on dermatological databases, skin care literature, and user-dependent considerations like age, skin type, climatic conditions, and past skin care. The intelligent mirror includes IoT sensors that sense environmental parameters like humidity, temperature, and UV index that impact skin well-being. Users interact with the intelligent mirror through voice recognition, touchpad controls, or gesture sensors, so it can be a seamless and convenient user interface. Progress tracking can be provided by the system too, where users get to track changes in the skin over a period of time and accordingly adjust their regimens. Besides, it ties in with cloud platforms and mobile apps, allowing consumers to access their skincare information online and receive professional advice. (shrink)

Cultivation Advisor may be the single most significant feature of precision farming. Prescription farming attempts to establish these parameters on a case-by-case basis in an attempt to solve the crop selection problems. Although the "site-specific" approach has enhanced performance, there remains the necessity to oversee the results of the system. Precision farming systems are not equal to one another. But in agriculture, it is important that the suggestions given are accurate and precise because a single mistake can (...) lead to a lot of material and capital loss. This application will help farmers boost agricultural productivity. A crop requires sufficient soil and atmospheric conditions for the crop to yield well. So, if we provide the values that define soil and atmospheric conditions to this application it informs us about the crop that can be cultivated for the specified conditions. This online application will assist the farmers by determining the kind of disease the crop is undergoing with the help of the machine learning algorithm and even recommending to the farmers the kind of medicine that needs to be utilized to rectify the situation in both text and image of the medicine and pesticides that need to be applied. (shrink)

Customer Relationship Management has become one of the most valuable tools in business, with the help of predictive analyzes organizations can identify their customer’s needs and improve business results. This research focuses on using of predictive analytics in Salesforce, the prominent CRM system that helps to gain insights from large volumes of data with the help of deep learning approaches. Using current advanced models like Recurrent Neural Networks and the transformer connections, companies can gain deep insights into such (...) patterns and trends as customer buying behavior, sales anticipation, and customer lifetime value. It is not only effective in improving decision making but also useful in the customization of the marketing strategy involving the delivery of messages that the customer might be interested. Integrating deep learning into Salesforce’s analytics is a revolution in the CRM system function. Former approaches in data analysis could not deal with unstructured data or analyses customer paths, as mentioned before. However, thanks to deep learning, large-scale customer data and aspects of previously hidden patterns have become changer for business interpretation. This research demonstrates the potential of using deep learning for predictive analytics to inform customer-oriented initiatives and increase the proportion of customers amongst leads and stability of client base. Thus, by focusing on Salesforce as an example, this study gives a clear vision of how artificial intelligence revolutionizes the approach to CRM, serves as the reference point for data-driven businesses in the digital environment. (shrink)

This research presents an automated system for predicting crop yield using the Random Forest Regression algorithm. The model leverages agricultural parameters such as soil composition, rainfall, temperature, and fertilizer usage to provide real-time and accurate yield predictions. A user-friendly web application developed with Python Flask allows for easy interaction, enabling farmers and agricultural professionals to input data and receive yield estimates. The results demonstrate the model’s reliability, achieving over 99% accuracy on test data. The system's modularity (...) supports future expansions, including real-time data integration and mobile deployment for widespread agricultural use. This project presents an AI-driven crop yield prediction system utilizing the Random Forest Regression algorithm. The system is designed to forecast agricultural yield based on various factors such as soil quality, rainfall, temperature, and historical crop data. Implemented using Python and deployed via a user-friendly Flask web interface, the application enables real-time yield prediction, empowering farmers and policymakers to make informed decisions. The model, trained on a diverse dataset, demonstrates high accuracy and robustness, handling non-linear data patterns effectively. By combining machine learning and intuitive design, this solution enhances agricultural planning, resource management, and overall productivity. (shrink)

The global agricultural sector faces increasing pressure to feed a growing population while preserving environmental integrity. Artificial Intelligence (AI) offers promising solutions to enhance sustainability from the field to the consumer’s plate. This paper explores how AI is revolutionizing agriculture through precision farming, crop monitoring, yield prediction, supply chain optimization, and food waste reduction. By reviewing recent literature, current technologies, and real-world applications, the study provides a comprehensive overview of AI’s transformative role in promoting sustainable agriculture.

Just as humans can draw conclusions responsibly or irresponsibly, so too can computers. Machine learning systems that have been trained on data sets that include irresponsible judgments are likely to yield irresponsible predictions as outputs. In this paper I focus on a particular kind of inference a computer system might make: identification of the intentions with which a person acted on the basis of photographic evidence. Such inferences are liable to be morally objectionable, because of a way in (...) which they are presumptuous. After elaborating this moral concern, I explore the possibility that carefully procuring the training data for image recognition systems could ensure that the systems avoid the problem. The lesson of this paper extends beyond just the particular case of image recognition systems and the challenge of responsibly identifying a person’s intentions. Reflection on this particular case demonstrates the importance (as well as the difficulty) of evaluating machine learning systems and their training data from the standpoint of moral considerations that are not encompassed by ordinary assessments of predictive accuracy. (shrink)

Artificial Intelligence (AI) is a technology that makes machines smart and capable of doing things that usually require human intelligence. AI works by training machines to learn from data and experiences. Such devices can recognize patterns, understand spoken language, see and understand images, and even make predictions based on their learning. Voice assistants like Siri or Alexa can understand our voice commands, answer questions, and perform tasks for us. AI-based self-driving cars can sense their surroundings, make decisions, and drive (...) safely without a human driver. AI also helps doctors diagnose diseases, detect fraud in financial transactions, and recommend movies or songs based on one’s preferences. Furthermore, AI has started to make its presence felt in Punjabi culture through various avenues. (shrink)

Generative Artificial Intelligence (AI) is rapidly emerging as one of the most innovative and transformative technologies in the field of machine learning. Unlike traditional AI, which focuses on classification and prediction tasks, generative AI is designed to create new, original content, such as images, music, text, and even software code. This paper delves into the foundational concepts of generative AI, highlighting its core models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformer models. We explore the applications (...) of generative AI in various domains such as art, healthcare, and entertainment, and examine the ethical considerations surrounding its use. Additionally, the paper discusses the future potential of generative AI in fostering creativity, automating workflows, and addressing complex real-world problems. Th. (shrink)

Artificial Intelligence (AI) is increasingly influencing the evolution of financial institutions, altering the traditional landscape of money, transactions, and customer experiences. From automated systems to predictive analytics and machine learning-driven financial advice, AI is reshaping every aspect of the financial industry. This paper explores the profound impact of AI on financial institutions, examining how it is transforming financial services, enhancing efficiency, and enabling new business models. Additionally, it investigates the associated challenges, such as data security, regulatory issues, and (...) ethical concerns. The future of money in the context of AI adoption presents both opportunities and risks, and this study aims to offer a nuanced understanding of the role AI will play in the financial sector. By analyzing current trends, technological advancements, and case studies, the paper provides a roadmap for financial institutions to navigate the rapidly evolving digital landscape. (shrink)

This work explores the potential of big data analytics, natural language processing (NLP), and machine learning (ML) techniques in predicting cyberbullying on social media. By analyzing large-scale datasets consisting of user comments, posts, and interactions, the study aims to detect harmful content patterns, abusive language, and behavioral trends that indicate cyberbullyingThe rapid proliferation of social media has transformed communication and interaction, but it has also led to an alarming rise in cyberbullying incidents. Cyberbullying, characterized by repeated and intentional harassment (...) through digital platforms, has significant psychological and social consequences for victims, often leading to anxiety, depression, and even self-harm. Traditional methods of identifying and mitigating cyberbullying are often reactive and inefficient due to the vast volume of data generated across multiple platforms. Therefore, predictive models leveraging big data and artificial intelligence (AI) offer a proactive approach to detecting and preventing online harassment. Sentiment analysis, text classification, and deep learning models such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to enhance the accuracy of cyberbullying predictions. Additionally, graph-based techniques are utilized to examine social network structures and identify potential perpetrators and victims based on interaction patterns. Despite the promising potential of big datadriven approaches, challenges such as data privacy, ethical concerns, and algorithmic bias must be addressed to ensure fair and responsible implementation. The study also highlights the importance of real-time monitoring systems that can alert platform administrators or authorities when cyberbullying behavior is detected. The findings demonstrate that integrating AI with big data analytics significantly improves the accuracy and efficiency of cyberbullying detection, enabling early intervention and fostering a safer digital environment. Future research will focus on refining detection models to handle multilingual data, cultural nuances, and evolving forms of cyberbullying across diverse social media platforms. (shrink)

What can contemporary machine learning (ML) models do? Given the proliferation of ML models in society, answering this question matters to a variety of stakeholders, both public and private. The evaluation of models' capabilities is rapidly emerging as a key subfield of modern ML, buoyed by regulatory attention and government grants. Despite this, the notion of an ML model possessing a capability has not been interrogated: what are we saying when we say that a model is able to do (...) something? And what sorts of evidence bear upon this question? -/- In this paper, we aim to answer these questions, using the capabilities of large language models (LLMs) as a running example. Drawing on the large philosophical literature on abilities, we develop an account of ML models' capabilities which can be usefully applied to the nascent science of model evaluation. Our core proposal is a conditional analysis of model abilities (CAMA): crudely, a machine learning model has a capability to X just when it would reliably succeed at doing X if it 'tried'. The main contribution of the paper is making this proposal precise in the context of ML, resulting in an operationalisation of CAMA applicable to LLMs. We then put CAMA to work, showing that it can help make sense of various features of ML model evaluation practice, as well as suggest procedures for performing fair inter-model comparisons. (shrink)

The convergence of artificial intelligence (AI) and automation is driving a paradigm shift in engineering, enabling the development of smart, adaptive, and future-ready systems. As global industries face increasing demands for efficiency, customization, and sustainability, the integration of AI and automation has become essential for maintaining competitiveness and innovation. This paper explores how these technologies are transforming traditional engineering practices into intelligent, autonomous, and highly responsive systems capable of operating in complex and dynamic environments. Smart engineering harnesses the computational (...) power of AI—such as machine learning, computer vision, and natural language processing—alongside automation technologies like robotics, control systems, and Industrial Internet of Things (IIoT) devices. Together, they enhance design optimization, predictive maintenance, real-time monitoring, and adaptive manufacturing. For instance, AI-driven generative design tools can produce optimized engineering solutions based on performance requirements, while automated systems can execute precise and repeatable tasks at scale. These advancements not only improve product quality and operational efficiency but also reduce human error and downtime. The paper also addresses the challenges of implementing smart engineering systems, including integration complexity, cybersecurity risks, ethical considerations, and the need for workforce transformation. Through case studies and industry examples, it illustrates the real-world impact and benefits of smart engineering across sectors such as automotive, aerospace, energy, and infrastructure. Ultimately, the fusion of AI and automation is not just an enhancement of engineering processes—it represents a fundamental shift toward more intelligent, adaptive, and self-improving systems. As industries prepare for the next wave of digital transformation, smart engineering will play a pivotal role in shaping sustainable and resilient technological ecosystems. (shrink)

Wildlife conservation and habitat monitoring are essential for maintaining biodiversity and ecological balance. Traditional conservation methods face challenges such as limited manpower, inaccessible terrain, and the need for continuous surveillance. Artificial Intelligence (AI) has emerged as a transformative tool, enabling more efficient, scalable, and real-time approaches to wildlife protection. This paper explores the use of AI technologies such as computer vision, machine learning, and bioacoustic analysis in monitoring animal populations, detecting poaching activity, and assessing habitat health. A structured (...) framework is proposed for integrating AI into conservation practices, highlighting benefits and future potential. (shrink)

Abstract: The integration of artificial intelligence (AI) into drug discovery is revolutionizing the pharmaceutical industry by accelerating the development of novel therapeutics. AI-powered tools enable researchers to process vast datasets, identify drug candidates, and predict their efficacy and safety with unprecedented speed and accuracy. This paper explores the transformative impact of AI on drug discovery, highlighting key advancements in machine learning algorithms, deep learning, and predictive modeling. Additionally, it addresses the challenges associated with AI implementation, including data (...) quality, regulatory hurdles, and ethical considerations. By analyzing case studies of AI-driven pharmaceutical breakthroughs, this paper underscores AI's potential to streamline drug development, reduce costs, and address unmet medical needs. The future of drug discovery is poised to shift dramatically as AI continues to advance, offering promising solutions for improving human health. (shrink)

In response to the issues of volatile climate patterns and infestations by pests, this proposal aims at creating a software application capable of assisting farmers in identifying plant diseases and detecting pests. The main aim is to equip farmers with a tool that allows for early identification, which will aid in making well-informed decisions in crop management. Through the use of machine learning algorithms and sophisticated image recognition methods, the suggested software is expected to deliver precise identification of (...) pests and diseases. This technological solution has the potential to play an important role in minimizing crop losses and reducing the dependency on chemical inputs in agriculture. The proposed software is an invaluable tool for farmers around the world, providing a functional means to increase their capacity to effectively counteract new threats to crop health. With this innovation, the agricultural community can enjoy a more sustainable and more resilient approach to agricultural practices, ensuring long-term viability and productivity under threatening environmental conditions. (shrink)

In recent years there has been an explosion of interest in Artificial Intelligence (AI) both in health care and academic philosophy. This has been due mainly to the rise of effective machine learning and deep learning algorithms, together with increases in data collection and processing power, which have made rapid progress in many areas. However, use of this technology has brought with it philosophical issues and practical problems, in particular, epistemic and ethical. In this paper the authors, (...) with backgrounds in philosophy, maternity care practice and clinical research, draw upon and extend a recent framework for shared decision-making (SDM) that identified a duty of care to the client's knowledge as a necessary condition for SDM. This duty entails the responsibility to acknowledge and overcome epistemic defeaters. This framework is applied to the use of AI in maternity care, in particular, the use of machine learning and deep learning technology to attempt to enhance electronic fetal monitoring (EFM). In doing so, various sub-kinds of epistemic defeater, namely, transparent, opaque, underdetermined, and inherited defeaters are taxonomized and discussed. The authors argue that, although effective current or future AI-enhanced EFM may impose an epistemic obligation on the part of clinicians to rely on such systems' predictions or diagnoses as input to SDM, such obligations may be overridden by inherited defeaters, caused by a form of algorithmic bias. The existence of inherited defeaters implies that the duty of care to the client's knowledge extends to any situation in which a clinician (or anyone else) is involved in producing training data for a system that will be used in SDM. Any future AI must be capable of assessing women individually, taking into account a wide range of factors including women's preferences, to provide a holistic range of evidence for clinical decision-making. (shrink)

Now-a-days selection of crop is a significant challenge for farmers it becomes more difficult, if they are having no experience or do not have knowledge to select the best possible viable crop. Introducing the Smart Crop Advisor (SCA) which aids in such recommendations to local conditions. Smart Crop Advisor is an online decision support system that adopts Gradient Boosting algorithm to improve the accuracy of agricultural decisions, and gives low-level farmers a personal crop selection advice. (...) Smart Crop Advisor is Designed using Python, HTML and CSS. Python is used for backend development and machine learning model implementation. The user interface is designed using HTML and CSS, allowing for anyone to just add their conditions easily. This application provides insights based on data by utilizing a large agricultural database. The farmers have to enter the specifications based on their soil test report like PH value, climatic conditions such as humidity, temperature. The system proposes best crops suited for particular region and situations. SCA improves crop yields and minimizes chemical inputs, reduces resource loss by selecting the right crops for a specific region and climate, farmers can improve crop yields and reduce waste. It promotes sustainable farming S which the system promotes sustainable farming practices by recommending crops that are well-suited to the local conditions. (shrink)

Abstract: Accurate identification of marine species is critical for effective fishery management, biodiversity conservation, and the aquaculture industry. Traditional methods of fish identification rely on expert knowledge and manual labor, making them time- consuming, expensive, and error-prone. In this research, we explore a machine learning-based approach to automate the classification of nine fish species using image recognition techniques. The fish species under study include Black Sea Sprat, Gilt- Head Bream, Horse Mackerel, Red Sea Bream, Shrimp, Trout, Striped Red Mullet, (...) Sea Bass, and Red Mullet. We collected 1000 images per species, yielding a total of 9000 images, with 750 used for training and 250 reserved for testing. We utilized convolutional neural networks (CNNs) pre-trained on the ImageNet dataset and fine-tuned them to perform fish species classification. Preliminary experiments showed that CNNs, particularly transfer learning models like VGG16 and ResNet50, achieved accuracy rates as high as 98.6% on the test set. This research highlights the potential of deep learning in automating marine species identification, enabling faster, more reliable monitoring. (shrink)

Soil fertility is a critical factor in ensuring healthy crop growth and agricultural productivity. Traditional methods of assessing soil fertility, which rely on laboratory testing to measure essential nutrients like Nitrogen (N), Phosphorus (P), and Potassium (K), are often time-consuming, labor-intensive, and impractical for small-scale farmers. To address these challenges, this paper proposes an advanced framework that combines Internet of Things (IoT) technology with an optical transducer for efficient soil fertility analysis. The optical transducer measures the absorption of light (...) by soil, accurately detecting the presence and levels of N, P, and K. IoT-enabled sensors monitor additional parameters, such as pH, temperature, and moisture, providing comprehensive real-time data. This data is transmitted to farmers, enabling them to make timely and informed decisions regarding fertilizer application. The proposed system eliminates the need for laboratory testing, offering a faster, more cost-effective solution. By accurately identifying nutrient deficiencies and minimizing fertilizer overuse, this technology helps preserve soil health, improve crop yields, and promote sustainable farming practices. The integration of IoT and optical sensing technology represents a promising innovation in agriculture, enhancing productivity while reducing environmental impact. (shrink)

Today, artificial intelligence, especially machine learning, is structurally dependent on human participation. Technologies such as Deep Learning (DL) leverage networked media infrastructures and human-machine interaction designs to harness users to provide training and verification data. The emergence of DL is therefore based on a fundamental socio-technological transformation of the relationship between humans and machines. Rather than simulating human intelligence, DL-based AIs capture human cognitive abilities, so they are hybrid human-machine apparatuses. From a perspective of media (...) philosophy and social-theoretical critique, I differentiate five types of “media technologies of capture” in AI apparatuses and analyze them as forms of power relations between humans and machines. Finally, I argue that the current hype about AI implies a relational and distributed understanding of (human/artificial) intelligence, which I categorize under the term “cybernetic AI”. This form of AI manifests in socio-technological apparatuses that involve new modes of subjectivation, social control and discrimination of users. (shrink)

This document is based on integrating healthcare system and create a self-sustaining ecosystem that can help healthcare providers and hospitals to provide adequate as well as accurate treatment. This is now the age of smart computer. Machines have started to impersonate as human, with the advent of artificial intelligence, machine learning, and deep learning. Chatbot is classified as conversational software agents enabled by natural language processing, and is an excellent example of such system. A Chatbot is a (...) program which allows the user to start a conversation with the machine. These Chatbots can be used in health sector. Health Chatbots are capable of running one-on-one patient communications and of reviewing specific patient queries. The integration of Artificial Intelligence (AI) in healthcare is revolutionizing patient interaction through intelligent virtual health assistants. This project explores the development of an AI-powered virtual health assistant that leverages machine learning to enhance patient engagement, provide personalized health recommendations, and assist in preliminary diagnostics. The system utilizes Natural Language Processing (NLP) for understanding patient queries and predictive analytics for offering real-time responses based on medical data. By incorporating machine learning algorithms, the assistant continuously learns from user interactions to improve accuracy and efficiency. This approach aims to reduce the workload on healthcare professionals, improve accessibility to medical information, and enhance patient experience. The study evaluates the impact of AI-driven virtual health assistance on patient satisfaction, response accuracy, and healthcare delivery efficiency. The proposed solution demonstrates the potential of AI in creating a more accessible and intelligent healthcare ecosystem. (shrink)

As the adoption of cloud computing continues to increase, securing cloud environments has become an ever-growing concern. Traditional security models struggle to keep up with the evolving nature of cyber threats, making it essential for organizations to explore innovative approaches. This paper explores how artificial intelligence (AI) and machine learning (ML) can enhance cloud security by automating threat detection, response, and mitigation in real-time. Through the application of advanced ML algorithms, AI-driven security systems can identify and predict security (...) incidents, classify threats, and adapt to new attack strategies. The paper examines the various ML techniques, such as anomaly detection, supervised learning, and deep learning, used to enhance cloud security. It also explores the challenges, benefits, and potential future directions of AI-driven security in cloud computing. Case studies from industry leaders demonstrate the impact of these technologies in improving the robustness and efficiency of cloud security frameworks. (shrink)

The increasing complexity and volume of cyber threats necessitate a more advanced and proactive approach to cybersecurity. Traditional Security Operations Centers (SOCs) rely on rule-based systems and manual analysis, which are often insufficient to counter evolving attack vectors. Artificial Intelligence (AI), particularly Machine Learning (ML) and Blockchain technology, has emerged as a game-changer in enhancing SOC operations, improving threat detection, response, and mitigation capabilities. Machine Learning algorithms can analyze vast amounts of security data in real time, identifying (...) anomalies and predicting potential threats with higher accuracy than conventional methods. ML-driven threat intelligence enables SOCs to detect zero-day attacks, automate incident response, and reduce false positives, thereby increasing operational efficiency. Additionally, AI-powered automation allows security analysts to focus on strategic threat mitigation rather than being overwhelmed by repetitive tasks. Blockchain technology further strengthens cybersecurity by providing an immutable, decentralized, and transparent framework for securing digital transactions and communications. Its application in SOC operations includes secure identity management, tamper-proof logging of security events, and decentralized threat intelligence sharing. By integrating blockchain with AI, SOCs can ensure data integrity, enhance collaboration across organizations, and reduce insider threats. This paper explores the synergy between AI, ML, and Blockchain in modern SOC environments. It discusses their combined potential to transform cybersecurity operations by enabling intelligent automation, realtime threat analysis, and secure data management. Furthermore, it highlights challenges such as computational overhead, integration complexity, and privacy concerns that must be addressed to maximize the benefits of these technologies. (shrink)

Artificial intelligence (AI), deep learning, machine learning and neural networks represent extremely exciting and powerful machine learning-based techniques used to solve many real-world problems. Artificial intelligence is the branch of computer sciences that emphasizes the development of intelligent machines, thinking and working like humans. For example, recognition, problem-solving, learning, visual perception, decision-making and planning. Deep learning is a subset of machine learning in artificial intelligence that has networks capable of learning unsupervised from (...) data that is unstructured or unlabeled. Deep learning is a technique used to generate face detection and recognize it for real or fake by using profile images and determine the differences between them. In this study, we used deep learning techniques to generate models for Real and Fake face detection. The goal is determining a suitable way to detect real and fake faces. The model was designed and implemented, including both Dataset of images: Real and Fake faces detection through the use of Deep learning algorithms based on neural networks. We have trained dataset which consists of 9,000 images for total in 150 epochs, and got the ResNet50 model to be the best model of network architectures used with 100% training accuracy, 99.18% validation accuracy, training loss 0.0003, validation loss 0.0265, and testing accuracy 99%. (shrink)

Phishing attacks are major threats to email security and pose challenges, while cyber attackers utilize increasingly sophisticated means to deceive the user and steal away important information. Well-established ways of detecting phishing attacks, such as rule-based systems or simple machine-learning models, usually cannot deal efficiently with such advanced threats. This research proposes an approach to detect phishing attacks on email systems, which deploys natural language processing and deep learning technologies. The method proposes to improve the detection accuracies and (...) efficiencies of phishing emails, which consequently enhances the protection of emails against popular cyberattack attempts and aids in securing users from such attacks. The research involves designing an active model that utilizes the strength of NLP-based text analysis for DL-oriented pattern identification. The NLP techniques used in this study include tokenization, stop-word removal, and context- based analysis to extract significant features from the email messages. Such context information greatly helps this model differentiate between actual emails and phishing ones. Deep learning algorithms exploited here are based on CNN and LSTM networks, offering sinusoidally varying parameters for optimum recognition of patterns from different perspectives in the email data's spatial and temporal domains. The experiment results validated that the hybrid model was far superior to the conventional methods in robbing phish attack detection. The model stood at an accuracy of 97.5%, much ahead of the baseline models purely having rule-based systems or traditional machine learning algorithms. The model also performed well under real-time detection conditions, with low latency and high throughput support for deployment in an active email environment. Evidence of the model's capability to sense new variants of future phishing threats also came in the automatic updating and continuous learning, thus making it applicable to newly emerging threats. The study's practical implications are numerous in providing advances in email protection. Companies can use the model to ward off phishing attacks with reduced chances of data breaches and financial loss. Due to its low cost and scalable features, this solution can be used in organizations from small- and medium-sized to large enterprise levels. The improved detection of phishing attacks brings organizations closer to compliance with data protection and cybersecurity regulations, thus minimizing their chances of noncompliance and the fines that come with it. Future research paths include exploring even newer NLP methods consisting of transformer-based models for feature extraction about contextual understanding. Exploring hybrid approaches that can merge DL with other techniques, such as reinforcement learning in ML, will create a more robust and adaptive phishing filter. Future fields for research are multimodal data, that is, a mixture of email metadata and behavior. Merely considering hybrid techniques merging artificial intelligence with machine learning approaches such as reinforcement learning can generate an even more robust and adaptive phishing filter. Coupling all these technical solutions with user awareness and education programs would greatly enhance overall security. (shrink)

The integration of Artificial Intelligence (AI) and Machine Learning (ML) into the research landscape has transforming almost every extending field, including pharmaceutical research. The idea of drug discovery itself is very conventional and has long been criticized for being overly lengthy and expensive, which sometimes may take more than 10 years and billions of dollars to develop a certain drug. AI and ML formulate the future of the drug discovery process by using big data to provide preliminary drug (...) candidates more effectively. This paper overviews the innovations defined by AI and ML in the field of drug discovery, major achievements, techniques, and use cases. Additionally, we explore how AI algorithms can enter biological data, inspect drug-target relations, determine optimal drug design, and potentially recompose famous drugs. This means through using big data with the help of AI in the process of research, previously undisclosed patterns that help in developing effective treatments for patients are found. The paper also addresses the related issues and limitations in applying AI in this domain, namely, data quality issues, the interpretability of AI solutions, and some ethical concerns. Herein, to provide a concrete foundation to the concepts mentioned so far, we discuss the different AI applications and case studies in drug discovery from a survey of the available literature. The article also provides information regarding the methodologies used in AI-enabled drug discovery like deep learning, reinforcement learning, and natural language processing. Moreover, we compare the use of conventional and artificial intelligence methods, while demonstrating what is good and what maybe in both. The results section offers a review and an integration of the most recent objectives and recommendations for subsequent research instruction. Therefore, despite these prohibitive AI and ML forecasts for drug discovery improvement, continuous interaction between computational scientists, biologists, and regulatory authorities is functional to fully unlock this potential. (shrink)

It is often thought that traditional recidivism prediction tools used in criminal sentencing, though biased in many ways, can straightforwardly avoid one particularly pernicious type of bias: direct racial discrimination. They can avoid this by excluding race from the list of variables employed to predict recidivism. A similar approach could be taken to the design of newer, machine learning-based (ML) tools for predicting recidivism: information about race could be withheld from the ML tool during its training phase, ensuring that (...) the resulting predictive model does not use race as an explicit predictor. However, if race is correlated with measured recidivism in the training data, the ML tool may ‘learn’ a perfect proxy for race. If such a proxy is found, the exclusion of race would do nothing to weaken the correlation between risk (mis)classifications and race. Is this a problem? We argue that, on some explanations of the wrongness of discrimination, it is. On these explanations, the use of an ML tool that perfectly proxies race would (likely) be more wrong than the use of a traditional tool that imperfectly proxies race. Indeed, on some views, use of a perfect proxy for race is plausibly as wrong as explicit racial profiling. We end by drawing out four implications of our arguments. (shrink)

Drought is a critical environmental issue that affects agriculture, water resources, and ecosystems. Traditional drought monitoring methods rely on ground-based meteorological observations, which have limited spatial coverage and do not provide real-time assessments. This project aims to develop a Vegetative Drought Prediction System by integrating Vegetation Condition Index (VCI) data from the ISRO VEDAS VCI Dashboard, remote sensing indices (NDVI), meteorological drought indicators (SPI, PDSI), and machine learning algorithms (Random Forest, SVM, LSTM) to accurately detect and predict drought (...) conditions. The system utilizes VCI data from the ISRO VEDAS portal, which provides high-resolution drought monitoring for India. Additionally, satellite imagery from MODIS, Sentinel-2, and Landsat-8 is used to compute NDVI (Normalized Difference Vegetation Index) to assess vegetation health. Meteorological indices, such as the Standardized Precipitation Index (SPI) and Palmer Drought Severity Index (PDSI), are used to evaluate precipitation deficits and long-term drought trends. Machine learning models further enhance drought prediction. Random Forest (RF) and Support Vector Machine (SVM) classify drought severity based on VCI, NDVI, temperature, and rainfall data. Long Short-Term Memory (LSTM) networks analyze time-series data to forecast future drought conditions. These models are trained on historical drought records and meteorological data to improve accuracy. A web-based application is developed to visualize drought conditions, enabling users to select a region, analyze VCIbased drought indices, and receive real-time predictions. The frontend is built using HTML, CSS, and JavaScript (React.js), while the backend is implemented using Flask/Django, with PostgreSQL/MongoDB for data storage. This project provides an efficient, scalable, and cost-effective solution for drought monitoring using ISRO VEDAS VCI data, benefiting farmers, policymakers, and researchers in making informed decisions and enhancing drought preparedness. (shrink)

As the financial services industry advances, managing the inherent complexities of annuities requires sophisticated risk management in software testing. Traditional methodologies are insufficient to address the multi-dimensional challenges posed by evolving regulatory landscapes, intricate financial models, and system integration. This paper investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) to enhance risk mitigation across critical testing domains, including compliance automation, financial accuracy, data security, and performance optimization. AI/ML technologies introduce advanced automation, predictive analytics, and anomaly (...) detection, elevating the precision and efficiency of the testing lifecycle. Through continuous learning models and adaptive testing frameworks, AI/ML streamlines legacy system integrations and dynamically scales performance testing. This article establishes the strategic imperative for insurers to integrate AI/ML into software testing frameworks, ensuring a proactive, data-driven approach to risk management and future-proofing their technological ecosystems. (shrink)

Abstract: Sarcasm is commonly used in news and detecting sarcasm in headline news is challenging for humans and thus for computers. The media regularly seem to engage sarcasm in their news headline to get the attention of people. However, people find it tough to detect the sarcasm in the headline news, hence receiving a mistaken idea about that specific news and additionally spreading it to their friends, colleagues, etc. Consequently, an intelligent system that is able to distinguish between can sarcasm (...) none sarcasm automatically is very important. The aim of the study is to build a sarcasm model that detect headline news using machine and deep learning and attempt to understand how a computer learns the patterns of sarcasm. The dataset used in this study was collected from Kaggle depository. We examined 21 algorithms of machine learning and one deep learning algorithm for detecting sarcasm in headline news. The evaluation metric used in this study are Accuracy, F1-measure, Recall, Precision, and Time needed for training and evaluation. The deep learning model achieved accuracy (95.27%), recall (96.62%), precision (94.15%), F1-score (95.37%) and time needed to train the mode (400 seconds), with loss of around 0.3398. However, the algorithm of machine learning that achieved the highest F1-Score is Passive Aggressive Classifier. It was the top classier for sarcasm detection among the machine learning algorithms with accuracy (95.50%), recall (96.09 %), precision (94.30%), F1-score (95.19%) and time needed to train the mode (0.31 seconds). (shrink)

Does the human mind resemble the machines that can behave like it? Biologically inspired machine-learning systems approach “human-level” accuracy in an astounding variety of domains, and even predict human brain activity—raising the exciting possibility that such systems represent the world like we do. However, even seemingly intelligent machines fail in strange and “unhumanlike” ways, threatening their status as models of our minds. How can we know when human–machine behavioral differences reflect deep disparities in their underlying capacities, vs. (...) when such failures are only superficial or peripheral? This article draws on a foundational insight from cognitive science—the distinction between performance and competence—to encourage “species-fair” comparisons between humans and machines. The performance/competence distinction urges us to consider whether the failure of a system to behave as ideally hypothesized, or the failure of one creature to behave like another, arises not because the system lacks the relevant knowledge or internal capacities (“competence”), but instead because of superficial constraints on demonstrating that knowledge (“performance”). I argue that this distinction has been neglected by research comparing human and machine behavior, and that it should be essential to any such comparison. Focusing on the domain of image classification, I identify three factors contributing to the species-fairness of human–machine comparisons, extracted from recent work that equates such constraints. Species-fair comparisons level the playing field between natural and artificial intelligence, so that we can separate more superficial differences from those that may be deep and enduring. (shrink)

In this paper, we focus on the most efficacious AI applications for life extension and anti-aging at three expected stages of AI development: narrow AI, AGI and superintelligence. First, we overview the existing research and commercial work performed by a select number of startups and academic projects. We find that at the current stage of “narrow” AI, the most promising areas for life extension are geroprotector-combination discovery, detection of aging biomarkers, and personalized anti-aging therapy. These advances could help currently living (...) people reach longevity escape velocity and survive until more advanced AI appears. When AI comes close to human level, the main contribution to life extension will come from AI integration with humans through brain-computer interfaces, integrated AI assistants capable of autonomously diagnosing and treating health issues, and cyber systems embedded into human bodies. Lastly, we speculate about the more remote future, when AI reaches the level of superintelligence and such life-extension methods as uploading human minds and creating nanotechnological bodies may become possible, thus lowering the probability of human death close to zero. We suggest that medical AI based superintelligence could be safer than, say, military AI, as it may help humans to evolve into part of the future superintelligence via brain augmentation, uploading, and a network of self-improving humans. Medical AI’s value system is focused on human benefit. (shrink)

Railway asset change detection through Artificial Intelligence (AI) technology has transformed infrastructure monitoring by providing better efficiency combined with predictive maintenance functions and improved accuracy. The paper studies the development of railway asset monitoring throughout history while it moved from traditional manual inspections to AI-powered solutions. The study recognizes three main barriers which include irregular data acquisition practices along with restricted sensor abilities and imprecise AI model precision and difficulties applying them to current railway control platforms. The research (...) investigates novel data collection approaches besides studying hybrid AI model deployment and finding optimal solutions for railway operational AI integration. The research findings establish critical requirements for railway administrators and policy makers and developers of AI systems regarding standardized regulatory structures and enhanced AI interpretation capabilities and collaborative advancements. Music research will further AI railway asset monitoring by integrating edge computing technology and 5G connectivity to process real-time data streams. The presented study enhances railway infrastructure management systems through its advances in AI methodology development along with solutions to implementation hurdles. (shrink)

According to the literature review, there is much room for improvement of college student retention. The aim of this research is to evaluate the possibility of using deep and machine learning algorithms to predict whether students continue to attend college or will stop attending college. In this research a feature assessment is done on the dataset available from Kaggle depository. The performance of 20 learning supervised machine learning algorithms and one deep learning algorithm is evaluated. The (...) algorithms are trained using 11 features from 1000 records of previous student registrations that have been enrolled in college. The best performing classifier after tuning the parameters was NuSVC. It achieved Accuracy (91.00%), Precision (91.00%), Recall (91.00%), F1-score (91.00%), and time required for training and testing (0.04 second). Additionally, the proposed DL algorithm scored: Accuracy (93.00%), Precision (93.00%), Recall (93.00%), F1-score (93.00%), time required for training and testing (0.66 second) for predicting whether student will continue to attend college or not. (shrink)