Wireless Sensors Network is a novel field shows tremendous application potential. To monitor the environmental parameters of high-tech polyhouse the Wireless Sensors Network (WSN) is developed. The heart of this ubiquitous field is the Wireless Sensor Node. Moreover, the field of microcontroller based embedded technology is innovative and more reliable. Therefore, based on an embedded technology and the RF module Zigbee a wireless senor node is designed about highly promising AVR ATmega8L microcontroller and implemented for WSN development. Recently, the modern (...) agriculturists are demanding sophisticated instrumentation for measurement and control of environmental parameters of the polyhouse. To enhance crop yield one has to provide controlled environment to the crop. The humidity is important parameter, which plays vital role on the crop yield. Therefore, a Wireless Sensors Network (WSN) is designed and implemented for monitoring of humidity of polyhouse and the results of implementation are interpreted in this paper. (shrink)

Abstract: In this research, an Artificial Neural Network (ANN) model was developed and tested to predict temperature in the surrounding environment. A number of factors were identified that may affect temperature or humidity. Factors such as the nature of the surrounding place, proximity or distance from water surfaces, the influence of vegetation, and the level of rise or fall below sea level, among others, as input variables for the ANN model. A model based on multi-layer concept topology was developed (...) and trained using data from several regions in the surrounding environment. The evaluation of testing the dataset shows that the ANN model is capable of correctly predicting the temperature with 100% accuracy. (shrink)

Agriculture is critical to the growth of an agricultural country. Many industries, including agricultural, have been altered as a result of the fast rise of Internet of Things-based technology. In India, farming employs over 70% of the people and generates one-third of the nation's capital. Agriculture-related issues have traditionally hampered the country's progress. The only answer to this challenge is smart agriculture, which involves upgrading conventional agricultural processes. Solutions based on IoT are being developed to autonomously manage and monitor agricultural (...) crops with minimum human intervention. Many elements of technology engaged in the realm of IoT in agriculture are covered in this article. It describes the essential components of IoT-based smart farming. Farmers benefit from IoTbased Agriculture by collecting live knowledge (Temperature, Soil Moisture, and Humidity) of agricultural data. These real-time measurements enable farmers to experiment with new agricultural techniques in order to boost average crop yields as well as plant quality. All of these actions will be controlled by any remote smart device or computer linked to the Internet, and they will be carried out by connecting sensors, Wi-Fi or ZigBee modules, cameras, and actuators with a Raspberry Pi and a microcontroller. (shrink)

There is an undeniable link between overpopulation and climate change. Overpopulation is dangerous in its own right, and the facts about the current state of affairs are truly terrifying, yet heavily overlooked and underreported. Overpopulation has led to—and continues to accelerate—deforestation. The scale of deforestation is also overlooked and heavily underreported, as is the critical role of forests in ecosystems, including their influence on healthy rain patterns. Changes in rain patterns are among the most dangerous consequences of climate change, which (...) will lead to shortages of drinking water in the foreseeable future all over the world. This, too, will be caused by deforestation, as the facts confirm. This article will explain how deforestation has caused shifts in humidity, directly contributing to the severity and frequency of droughts and floods. Additionally, changes in relative humidity are responsible for higher temperatures and the shrinking of glaciers and polar ice caps. These water reservoirs are diminishing not only because they are melting but also because they are drying out—all as a consequence of altered humidity. This shift in humidity has led to a seemingly absurd paradox: on one hand, the air is becoming drier; on the other hand, the total amount of water in the atmosphere is increasing. Scientists have been ignoring the causes and consequences of this paradox for many years. All of this has been directly influenced by deforestation, which, in turn, has been driven by the growing number of humans on this planet. We have far exceeded sustainable population levels. Another problem that exacerbates all issues related to climate change is human intervention in the climate through various weather engineering methods, often referred to as geoengineering. These methods include cloud seeding, CO2 capture, and others. They all worsen the situation, and humans fail to realize this, largely because we have ignored the enormous role of humidity levels in our ecosystems—particularly their impact on rain patterns. All of this is a consequence of deforestation. The solutions to climate problems lie in afforestation and global depopulation. These measures are relatively easier and faster to implement. Unfortunately, for them to succeed, humankind must first acknowledge these issues as the real causes of climate change and act swiftly, especially since deforestation continues unabated. We are in danger of having wasted time meant to save our planet by barking up the wrong tree—CO2 emissions. We are losing essential time. We cannot afford to be a society that "doesn’t look up!" The facts in this article are undeniable. The problem is deforestation, which is caused by overpopulation. Let the facts speak! (shrink)

This paper presents a machine learning-based framework for real-time short-term snowfall forecasting by integrating atmospheric and topographic data. The model uses real-time meteorological data such as temperature, humidity, and pressure, along with terrain data like elevation and land cover, to predict snowfall occurrence within a 12-hour forecast window. Random Forest (RF) and Support Vector Machine (SVM) models are employed to process these multi-source inputs, demonstrating a significant improvement in prediction accuracy over traditional methods. Experimental results show that the RF (...) model achieved a root mean square error (RMSE) of 3.2 cm, while traditional regression models achieved an RMSE of 5.1 cm. The proposed framework provides a more reliable and accurate tool for real-time snowfall prediction, especially in mountainous regions, and can be deployed for operational weather forecasting applications. (shrink)

The incorporation of thermal insulation materials into building walls is a novel strategy for reducing heating and cooling energy consumption. Nowadays, the issues of energy production, consumption, and energy storage have become global problems. Furthermore, the thermal insulation of buildings increases the thermal comfort of residential premises in order to save energy. The use of several kinds of thermal insulation materials is required in the construction sector. This paper compares the thermal and moisture performance of two different types of walls. (...) The buildings taken under consideration are located in two different Tirana neighborhoods, close to each other sharing similar climate parameters. Depending on the presence of thermal insulation, two types of walls were taken into consideration. A wall in which thermal insulation is not present and a wall with a ventilated façade system, are considered. Thermal insulation in the second case is made with rock mineral cotton. In order to obtain the most accurate results, pertinent temperature and humidity, measurements were made at several spots along the walls. The comparative analysis was performed using special measuring instruments. The temperature and moisture level in the internal walls and the temperatures of the external walls, at various spots on the walls, were taken into consideration, along with the layers of the relevant walls in both cases. The aim of this paper is to experimentally study the thermal performance of walls with various types of thermal insulation, including the ventilated façade system, as well as walls without thermal insulation. The purpose of the study is also to show how the technical and architectural methods utilized during construction affect the moisture and temperature fluctuations of the perimeter walls. (shrink)

Solving Typhoon Turbulence Using the Universal Formula -/- By Angelito Malicse -/- Introduction -/- Typhoons are among the most destructive natural phenomena, bringing extreme winds, heavy rainfall, and turbulent ocean currents. The chaotic turbulence within a typhoon makes it difficult to predict and control, causing widespread devastation to coastal regions, infrastructure, and human lives. Despite advancements in meteorology and fluid dynamics, the turbulence inside typhoons remains a challenge for accurate forecasting and disaster mitigation. -/- By applying my universal formula, based (...) on three fundamental laws of nature, we can develop a structured and effective approach to understanding and controlling typhoon turbulence. This essay explores how the Law of Karma (causality and system integrity), the Law of Balance in Nature, and the Law of Feedback Regulation provide the ultimate solution to typhoon turbulence. -/- Understanding Typhoon Turbulence Using the Universal Formula -/- Typhoons form due to energy imbalances in the atmosphere and ocean, driven by heat, pressure differences, and moisture content. The chaotic turbulence inside a typhoon is the result of: -/- 1. Unstable air currents caused by pressure and temperature gradients. -/- 2. Vortex formation driven by Earth’s rotation and wind shear. -/- 3. Excess energy accumulation in warm ocean waters. -/- By examining typhoon turbulence through the three universal laws, we can develop a scientific strategy to predict, weaken, and potentially neutralize typhoons before they cause destruction. -/- 1. The Law of Karma: Cause, Effect, and Systemic Integrity -/- The Law of Karma states that every effect has a cause, and for a system to function properly, it must be free of defects. A typhoon is an effect caused by imbalances in atmospheric and oceanic conditions. If these imbalances are corrected, typhoon turbulence can be weakened or prevented. -/- Applying the Law of Karma to Typhoon Control -/- ✅ Eliminate the root cause: Typhoons are fueled by warm ocean waters. Artificial cooling methods, such as cloud seeding or deep-sea upwelling (bringing cold water to the surface), could reduce the heat energy supply and prevent typhoon formation. -/- ✅ Detect turbulence before it intensifies: AI-driven weather models can predict early-stage typhoon turbulence and allow preemptive interventions. AI can identify defective atmospheric conditions (such as extreme heat buildup in ocean currents) and trigger mitigation efforts. -/- ✅ Disrupt vortex formation: Since typhoons rely on rotational forces, deploying strategically placed atmospheric pressure disturbances (via drones or cloud-seeding techniques) could disrupt the vortex before it gains full strength. -/- By identifying and neutralizing the causes of typhoon turbulence, we ensure that the system remains balanced, preventing large-scale destruction. -/- 2. The Law of Balance in Nature -/- The Law of Balance states that all systems naturally seek equilibrium. When a typhoon forms, it represents an extreme imbalance in the Earth’s energy system—excess heat and moisture create an unstable feedback loop of wind and turbulence. -/- Restoring Balance to Weaken Typhoon Turbulence -/- ✅ Regulating ocean surface temperature: Large-scale ocean-based temperature regulation systems (such as reflective films or artificial cooling platforms) can prevent typhoon formation by reducing the excess heat energy that fuels storms. -/- ✅ Breaking the pressure imbalance: Typhoons depend on a low-pressure core that intensifies wind speeds. By artificially increasing local air pressure using ionization or controlled cloud seeding, the extreme wind turbulence can be diffused before it reaches destructive levels. -/- ✅ Balancing atmospheric moisture: AI-assisted weather control could help regulate humidity levels through strategic rainfall induction in high-risk areas, ensuring that water vapor does not accumulate excessively to fuel a typhoon. -/- Since all systems follow natural equilibrium, strategically rebalancing temperature, pressure, and humidity will neutralize extreme typhoon turbulence. -/- 3. The Law of Feedback Regulation -/- The Law of Feedback Regulation states that all systems operate through feedback loops, where continuous monitoring and adjustment are necessary for stability. Typhoon turbulence follows feedback patterns, which means it can be tracked, analyzed, and counteracted using real-time data. -/- Developing an AI-Driven Typhoon Suppression System -/- ✅ AI-powered real-time monitoring: Advanced satellites, ocean buoys, and drones can provide instant feedback on typhoon development, allowing for proactive interventions rather than reactive disaster response. -/- ✅ Adaptive wind and pressure control: Deploying AI-controlled drones or weather balloons to release cooling agents, modify air pressure, or disperse cloud formations could interrupt the storm's feedback loop, reducing its intensity. -/- ✅ AI-driven weather forecasting for early response: Using deep learning algorithms, AI can accurately predict turbulence patterns weeks in advance, allowing governments to take preventive action instead of just preparing for disaster relief. -/- By implementing real-time monitoring and intervention, we can use feedback regulation to weaken or redirect typhoons before they cause catastrophic damage. -/- The Future of Typhoon Control with the Universal Formula -/- By integrating my universal formula into meteorology and disaster prevention, we can develop a comprehensive AI-driven typhoon suppression system that follows the three fundamental laws of nature: -/- 1. Law of Karma (Causality and Systemic Integrity): Address the root causes of typhoon formation by eliminating atmospheric and oceanic imbalances. -/- 2. Law of Balance in Nature: Restore equilibrium by regulating ocean temperatures, air pressure, and humidity. -/- 3. Law of Feedback Regulation: Use AI-driven real-time monitoring and intervention to weaken or prevent typhoons. -/- Through advanced AI, environmental engineering, and a deep understanding of nature’s laws, we can move beyond simply predicting typhoons and start actively controlling their development. -/- Conclusion -/- Typhoon turbulence, long considered an unpredictable force of nature, is fundamentally a problem of imbalance, energy accumulation, and feedback-driven intensification. By applying my universal formula, we can shift from passive disaster response to active typhoon control, using AI, environmental intervention, and natural equilibrium principles to prevent devastation. -/- This solution is not just theoretical—it aligns with the fundamental laws governing all natural systems. Once implemented, it will mark the beginning of a new era where humanity can harness nature’s own principles to create balance, stability, and protection from extreme weather. -/- . (shrink)

Agriculture plays a crucial role in the economic stability of many nations, and optimizing crop selection is essential for enhancing agricultural productivity and sustainability. The "Crop Recommender System Using Machine Learning Approach" aims to leverage machine learning techniques to provide precise crop recommendations based on various environmental and soil conditions. By incorporating factors such as soil composition, pH level, temperature, humidity, rainfall, and geographic location, this system suggests the most suitable crops for a given area. The system utilizes machine (...) learning models, particularly Random Forest and Decision Trees, to analyze historical agricultural data, predict optimal crops, and improve the decision-making process for farmers. By training the model on large datasets, it ensures accurate predictions that align with real-world agricultural practices. The application of this system can lead to higher crop yields, sustainable farming practices, and reduced risks associated with poor crop choices. Through rigorous evaluation using standard classification metrics, the model's performance demonstrates its potential to revolutionize farming practices by aiding farmers in making informed decisions. The system has the potential to be an invaluable tool for agricultural consultants, farmers, and policymakers, ensuring long-term sustainability and improved productivity. (shrink)

Cities and towns are expanding and thriving as a result of urbanization, which also significantly changes the local climate. One of the most significant phenomena associated with urbanization is the Urban Heat Island (UHI) effect. This phenomenon is increasingly being studied worldwide. The paper aims to investigate the UHI phenomenon in the metropolitan area of Tirana, Albania. It analyses the impact of the UHI on four specific locations in Tirana, its causes and mitigation measures, as well as variations in surface (...) temperature, CO2 emissions, and relative humidity. The regions are measured and observed using a specific instrument such as the Testo 435. Considering the mean surface temperature variations between urban and rural regions ranging from 28.4°C to 33.7 °C, CO2 emissions from 302.9 ppm to 416.2 ppm, and relative humidity from 34.1% to 41.2%, it is found that the UHI impact in Tirana is significant. The lack of green spaces, high building density, urban patterns, building materials used, transport, and energy use are the main contributors to the UHI in central Tirana. Increasing green spaces, using reflective materials, and promoting sustainable urban design are some of the mitigation techniques suggested to reduce the UHI effect in Tirana. (shrink)

Thermal insulation is an important component in nowadays construction. Considering that before 90s Albania had been undeveloped enough, there was no needed information for thermal insulation’s importance, as well as missing materials and economic hardships to provide them. Consequently, many objects built during this time are not thermal insulated. Nowadays, its application is being more and more significant and convenient due to its benefits. In our country, a lot of attention has been paid to the application of thermal insulation, since (...) 1990s. The main reason is the energy efficiency. Energy consumption is directly affected by its preservation. Moreover, it is crucial to mention the comfort that is offered. To give solutions to these housing challenges, is important to find the appropriate materials. During the latest years, more and more materials are highly standardized in contemporary expectations. One of these materials that is widely used in thermal insulation is expanded polystyrene. The benefits that the material has in terms of insulation, durability, moister resistance, and energy efficiency are important. In construction, insulation continues to function under a variety of temperature, humidity, and other conditions. The impact of expanded polystyrene in thermal insulation of buildings is the main subject of the study. This study should take in consideration the thermal insulation (EPS) applied in a building in the city of Tirana and another building where there is no application of thermal insulation. A comparative analyse between them should be applied by using a specific instrument, which calculate the U-value taking in consideration all layers of the wall and three components of temperature: indoor temperature, wall temperature and outer temperature. (shrink)

In today's agricultural landscape, farmers face numerous challenges in selecting the right crops to plant, primarily due to varying soil conditions and unpredictable weather patterns. This often leads to suboptimal yields and inefficient use of resources. To tackle these issues, we propose a Crop Recommendation System powered by machine learning, specifically utilizing the Random Forest algorithm. This innovative system will analyze essential factors such as soil nutrients—nitrogen, phosphorus, and potassium—as well as climatic conditions like temperature, humidity, and rainfall, to (...) provide tailored crop recommendations. Our goal is to empower farmers by simplifying the decision-making process, ensuring they have access to reliable guidance for improving crop selection. By making informed decisions, farmers can boost their yields, enhance sustainability, and ultimately contribute to a more efficient agricultural sector. This project not only aims to improve individual crop management practices but also supports researchers and policymakers in developing better agricultural strategies. Through this initiative, we hope to pave the way for a future where technology and agriculture work hand in hand for a more sustainable and productive world. (shrink)

One of the main reasons of environmental pollution is energy consumption in buildings. Today, the use of renewable energy sources is increasing dramatically. Among these sources, solar energy has favorable costs for various applications. This study examined a commercial building in a hot and humid climate. The findings showed that choosing the optimal angle of solar panels with the goal of optimized energy consumption would yield reduced costs and less environmental pollutants with the least cost and maximum energy absorption. In (...) this study, to calculate the energy requirements of the building, DesignBuilder software was used. To study the solar angles and estimate the energy produced by the solar panels, Polysun software was used after simulating the building energy. Energy simulation results showed that the whole building energy consumption was 26604 kWh/year. Finally, the evaluation results of solar panels showed that the energy produced by photovoltaic modules at an optimal angle of 31° would be equal to 26978 kWh/year, which is more than the energy required by the building. This system can prevent 14471 kg of carbon dioxide emissions annually. Sustainable energy criteria showed that for the studied building, photovoltaic modules could be used in energy production to reach a zero-energy system connected to the grid with an annual energy balance. (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)

Socialist buildings in the city of Kruja (Albania) date back after the Second World War between the years 1945-1990. These buildings were built during the time of the socialist Albanian dictatorship and the totalitarian communist regime. A questionnaire with 30 questions was conducted and 14 people were interviewed. The interviewed residents belong to a certain area of the city of Kruja. Based on the results obtained, diagrams have been conceived and mathematical regression models have been developed which will serve as (...) a base for drawing conclusions. The purpose of this study is to find a relationship that specifies the role of the inhabitants, the role of the dwellings, the physical-mechanical characteristics, and their energy efficiency, in order to improve the living conditions of the inhabitants. The independent variables interact with each other assuming positive or negative values depending on the probabilistic mathematical regression, giving different results based on the calculated data. The variables that co-exist among them are living space, quality of life, time spent at home, social interaction of residents, dwelling humidity level, dwelling orientation, satisfaction level of the inhabitants, dwelling improvements demand, and monthly electricity bills. (shrink)

The diversion of forest land for development requires accurate tree enumeration to assess environmental impact. Traditional methods, like manual counting and sampling, are labor-intensive, time-consuming, and prone to error. This project leverages high-resolution satellite and drone imagery, combined with advanced image processing and machine learning, to automate tree counting. Our system includes analytical tools, and provides authorities with historical environmental data (like rainfall, temperature, humidity) for informed decision-making. With a userfriendly interface and appealing data visualizations, it also integrates Google (...) Maps API for user-generated satellite imagery. This solution aims to enhance accuracy, efficiency, and scalability in tree enumeration, supporting sustainable forest management and regulatory compliance. (shrink)

Assumption: Sensory modalities are individuated by their proper objects. Hearing is the direct perception of sounds, sight the direct perception of colours, etc. Objection: There is no single type of proper sensibles in the case of touch (temperature, solidity, hardness, humidity, texture, weight, vibration...). Answer : 1. accept to distinguish the sense of pressure (touch strictly speaking) from the sense of temperature. 2. argue that pressures are the direct perceptual objects through which one perceives weight, texture, solidity.

Natural disasters such as earthquakes, floods, and wildfires have devastating consequences, necessitating efficient early warning systems. This paper presents a real-time disaster detection system leveraging IoT sensors to monitor environmental parameters, including temperature, humidity, seismic activity, and air quality. The system collects and processes sensor data using machine learning algorithms to detect anomalies and predict potential disasters. A cloud-based architecture ensures seamless data transmission and storage, enabling real-time monitoring and quick decision-making. The system issues automatic alerts to authorities and (...) residents through mobile notifications, SMS, and sirens, enhancing preparedness and minimizing losses. Experimental results demonstrate the system's high accuracy in detecting disasters, significantly reducing response time compared to traditional methods. The integration of IoT with artificial intelligence improves disaster prediction capabilities, making it a reliable solution for mitigating risks. Future enhancements will focus on refining sensor accuracy, expanding disaster coverage, and incorporating blockchain for secure data handling. (shrink)

New Delhi is known for having some of the worst air quality in the world, with fine particulate matter (PM₁) linked to over 10,000 premature deaths each year. Yet a new study suggests that the true extent of this pollution has been significantly underestimated—due to a scientific blind spot involving humidity.

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)

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)

Natural disasters such as earthquakes, floods, wildfires, and landslides pose significant threats to human life, property, and infrastructure. Early detection and timely response are critical in minimizing the damage caused by such events. This paper presents the design and implementation of an IoT-based real-time disaster detection and alert system that leverages a network of low-cost sensors, microcontrollers, and cloud connectivity to monitor environmental parameters and detect potential disasters. The proposed system integrates various IoT sensors— including temperature, humidity, water level, (...) gas, vibration, and smoke sensors—to detect abnormal conditions indicative of natural hazards. Data collected from the sensors are transmitted to a cloud server via Wi-Fi or GSM modules, where it is processed and analyzed in real time. Once a threshold is crossed or a disaster is detected, the system triggers instant alerts via SMS, email, and mobile notifications to relevant authorities and users in the affected region. The system is designed to be modular, scalable, and energy-efficient, making it suitable for deployment in both urban and remote areas. A prototype has been developed and tested under simulated disaster scenarios to validate its effectiveness, response time, and reliability. The results demonstrate that the system can serve as an efficient early warning mechanism, contributing to disaster preparedness and risk reduction through the integration of smart technology. (shrink)

Natural disasters such as earthquakes, floods, and wildfires pose significant threats to life and property, underscoring the need for effective early warning systems. This study introduces a real-time disaster detection system that utilizes IoT sensors to monitor critical environmental parameters such as temperature, humidity, seismic activity, and air quality. The collected data is processed using advanced machine learning algorithms to identify anomalies and predict potential disasters. A cloud-based infrastructure facilitates seamless data transmission, real-time monitoring, and efficient decision-making. The system (...) provides automated alerts to authorities and residents via mobile notifications, SMS, and sirens, enhancing disaster preparedness and minimizing damage. Experimental results confirm the system’s high accuracy and ability to significantly reduce response time compared to traditional methods. By integrating IoT and artificial intelligence, this solution offers improved disaster prediction and response capabilities. Future work will focus on enhancing sensor precision, expanding disaster scenarios, and adopting blockchain technology for secure and transparent data management. Refined Keywords . (shrink)

This project centers on developing a geo-tagging and monitoring system for plantations within the catchment area of a hydro project. Designed for the forest department, it provides real-time insights into environmental conditions to support the sustainable management of forest resources. The system comprises an Arduino Uno microcontroller that integrates GPS, DHT11 (temperature and humidity sensor), and LoRa modules to track and communicate critical data, such as the geographic location, temperature, and humidity around each plantation site. Powered by a (...) 7.4V Li-ion battery and regulated through an LM7805 voltage regulator for consistent 5V output, the system is optimized for low power consumption to ensure longevity in remote settings. Using the LoRa module, the system transmits data over long distances to a central receiver, which could be stationed at a monitoring hub. This data transmission operates independently of internet connectivity, making it ideal for isolated forest areas where network coverage is limited or non-existent. Each plant or designated plantation area can be geo-tagged and monitored through data collected by the sensors and sent through LoRa communication. The data is then processed and can be visualized on a server or application interface, giving forest officials an overview of the environmental conditions in each monitored area. This innovative system offers a scalable solution for remote environmental monitoring, allowing for expansion with additional sensors or wider deployment across forest areas. Furthermore, it lays a foundation for enhanced forest management through features like environmental alerts and trend monitoring, potentially aiding in conservation efforts by ensuring optimal conditions for plantation growth. By leveraging IoT technology in an eco-friendly, power-efficient setup, this project presents a modern approach to sustainable forestry and plantation monitoring. (shrink)

Cloudbursts pose a significant threat in India, especially during the South-West Monsoon season that commences in June. India's diverse climate regions, including the northern Himalayan region, Indo-Gangetic Plain, southern peninsula, and coastal areas, experience sporadic cloudbursts, with only 31 recorded instances, mainly in Himachal Pradesh, Uttarakhand, and Jammu and Kashmir. To address the lack of comprehensive Indian cloudburst data, we've curated a dataset, incorporating meteorological factors for cloudburst prediction. This dataset encompasses variables such as Temperature, Wind Gust, Wind Gust Speed, (...) Humidity, Monsoon patterns, Air Pressure, and Cloud Density. Our goal is to improve preparedness and mitigation strategies, safeguarding lives, and property in cloudburst prone areas. Employing optimized machine learning algorithms, our model analyzes these parameters alongside prevailing weather conditions, facilitating cloudburst event prediction. We evaluate the prediction performance of machine learning algorithms, including KNN. The KNN algorithm outperformed others with an accuracy of 86.18%. Moreover, we provide graphical insights into the correlation between humidity and cloudburst occurrence, emphasizing the importance of weather variables in prediction models. This research contributes to cloudburst forecasting, even with limited Indian data, and highlights the potential of utilizing diverse machine learning techniques for improved accuracy. (shrink)

The crop recommendation system employing machine learning methods will be covered in this study. For sustainable agricultural practices to be followed and to increase crop yields, crop advice is crucial. Based on several factors, including nitrogen (N), phosphorus (P), potassium (K), and humidity, we will advise the best crop for the given site. We analyzed various algorithms like KNN, Decision Tree, Random Forest, SVM etc. But based on various accuracy levels we committed to random forest implementation. Means in this (...) paper we are going to implement a crop recommendation system using random forest algorithm. The model is allowed to train upon a large dataset and the Performance of the recommendation system is measured using accuracy score. Finally, Using the trained model we are going to predict suitable crops for land according to the given parameters. Our proposed approach can be helpful for farmers, researchers, and policymakers in making informed decisions regarding crop management and planning. -/- . (shrink)

he Sensor-Integrated IoT Solution for Precision Farming is designed to optimize agricultural water usage and enhance crop productivity through real-time monitoring and automated control. The system uses an Arduino and NodeMCU to collect and process data from various sensors, including a pH sensor for water quality, a DHT11 sensor for temperature and humidity, and a soil moisture sensor to assess soil conditions. Sensor data is displayed on an LCD and uploaded to the ThingSpeak platform for remote monitoring. When the (...) pH value deviates from the optimal range, the system activates two water pumps—first, the outlet pump to remove poorquality water, followed by the inlet pump to introduce fresh water. Relays control the operation of the pumps, ensuring efficient water management. By integrating IoT technology, this system enhances water conservation, reduces manual intervention, and improves overall agricultural efficiency, making precision farming more sustainable and effective. (shrink)

Agricultural productivity is significantly impacted by wildlife intrusion and unfavourable environmental conditions, necessitating the integration of modern technology to enhance crop protection and growth monitoring. This project presents an intelligent IoT and sensor-based automation system designed to safeguard crops while ensuring optimal growth conditions. The system incorporates multiple sensors to monitor environmental parameters and detect potential threats in real time. A soil moisture sensor continuously assesses soil moisture levels, and when low moisture is detected, it automatically activates a water motor (...) to initiate irrigation, preventing water stress in crops. Additionally, a DHT sensor monitors temperature and humidity, providing farmers with real-time data updates through a mobile IoT platform, enabling proactive decision-making for maintaining ideal growing conditions. To mitigate the risks posed by wildlife intrusion, a Passive Infrared (PIR) sensor is deployed to detect motion in the field. Upon detecting movement, the system triggers an alarm buzzer to deter animals and simultaneously updates the IoT platform, alerting farmers about potential threats. Furthermore, the security aspect is enhanced using image processing techniques based on MATLAB and Convolutional Neural Networks (CNN). The system captures and processes images of intruding wildlife, accurately identifying animal species and automatically triggering an alert system for immediate intervention. By integrating IoT with machine learning-based image recognition, the system offers a dual-layered security mechanism, ensuring a proactive response to wildlife threats. (shrink)

The aim of this project is to develop a system that uses the Internet of Things (IoT) and machine learning (ML) to help farmers select the best crops for their fields. The geolocations are fetched using the GPS receiver by communicating with the satellite. The system consists of IoT sensors that collect data on soil and environmental conditions, such as pH, temperature, humidity, and rainfall. This data is sent to a cloud platform, where ML algorithms analyze it and provide (...) crop recommendations based on historical data, weather forecasts, and soil properties. The system offers a user-friendly interface that allows farmers to access the recommendations, monitor their crops, and provide feedback. The system aims to enhance agricultural productivity, resource efficiency, and sustainability by using data-driven insights. (shrink)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like (...) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (shrink)

Windows in buildings impact energy usage for temperature control through solar heat gain and enable natural light to reduce reliance on artificial lighting. Balancing solar heat gain and daylight utilization is a challenge, which can be addressed by employing automated or manual blind systems to manage daylight and enhance user comfort and energy efficiency. Additionally, accurate weather forecasts are essential for predicting energy-efficient strategies through individual building energy simulations, as weather conditions synergistically interact with occupant behavior to influence energy consumption (...) patterns. This research aims to assess the energy consumption associated with manual and automated internal window blinds in medium-sized office buildings situated within two distinct and significant climate zones in the United States, considering both present and future climate change scenarios based on the IPCC report (RCP 4.5 - 8.5). Employing a simulation-based methodology, the study unveiled varying effectiveness levels of diverse window blind configurations contingent on the specific climate zones (e.g., 4A Mixed-Humid, 2B Hot-Dry). In different climate zones, on-site energy consumption alterations for heating and cooling become evident as temperatures escalate in the forthcoming years. Using simulation as the method and comparing RCP 4.5 and 8.5 scenarios reveals that automated blinds—a more efficient choice than manual blinds—significantly reduces cooling energy consumption, particularly under RCP 8.5 in a 4A Mixed-Humid zone. Rising temperatures in Climate Zone 2B Hot-Dry are a factor in increased energy requirements for cooling and decreased energy requirements for heating due to climate change. This study provides enlightening insights into the potential benefits of diverse window-covering strategies concerning energy conservation within varied climatic contexts for the future. (shrink)

We investigate the application of Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and a hybrid CNN-LSTM model for forecasting air pollution levels based on historical data. Our experimental setup uses real-world air quality datasets from multiple regions, containing measurements of pollutants like PM2.5, PM10, CO, NO2, and SO2, alongside meteorological data such as temperature, humidity, and wind speed. The models are trained, validated, and tested using a split dataset, and their accuracy is evaluated using performance metrics like (...) Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R-squared. (shrink)

Premature birth or low birth weight (LBW) is predictor of infant morbidity and mortality. Generally, infant with premature birth will be treated on incubator device. Incubator must provide stable levels of temperature, relative humidity and oxygen concentration. Temperature on infant incubator must be maintaned around 36°C-38°C. In order to control that, adaptive Fuzzy- PI controller is proposed and implemented on infant incubator’s prototype. This research use DHT11 sensor as sensing element and the control method are actuated by lightbulbs. The (...) controller used Microcontroller Atmega16 as processor which output determines the amount of power supplied to the lightbulb by employing pulse width modulation and MOSFET triggered circuit. Tests were carried out on temperature references that correspond to the standard incubator temperature range of 36°C to 38°C. References changes (fluctuatif reference) test and disturbances test were also carried out to prove system performance. The tests result on fixed reference temperatures were able to reach a stable setpoint with time rise (tr) around the 200 second with the highest overshoot maximum steady state error below 5%. References change tests and disturbances test successfully proved the controller was able to follow changes in references and overcome the disturbances. (shrink)

This study presents a neural network-based model for predicting smoke potential in a specific area using a Kaggle-derived dataset with 15 environmental features and 62,631 samples. Our five-layer neural network achieved an accuracy of 89.14% and an average error of 0.000715, demonstrating its effectiveness. Key influential features, including temperature, humidity, crude ethanol, pressure, NC1.0, NC2.5, SCNT, and PM2.5, were identified, providing insights into smoke occurrence. This research aids in proactive smoke mitigation and public health protection. The model's accuracy and (...) feature analysis empower decision-makers, with potential applications in real-time smoke event monitoring and preparedness strategies. This work contributes to the field of air quality forecasting and environmental stewardship, offering a data-driven approach to address smoke-related challenges and enhance community well-being. (shrink)

Now, the climate of Bangladesh has subtropical monsoon weather with extensive seasonal variations concerning humidity, temperature, and rainfall. Furthermore, it has altitudes from 600 to 1000 meters above sea level, whereas, at 1063 m(3,488 ft.) altitude, Saka Haphong at Mowdok range is the highest height in the southern part the hill there (Wasson, R., 2003). Bangladesh is an emerging market as a developing nation where approximately 30% of its GDP comes from agriculture to tell more about the country. However, (...) Bangladesh got its Independence from Pakistan in 1971, after the partition of British India in 1947 by a glorious liberation war. Before Independence, it had its map within a red circle surrounded by a rectangular green shape as the national flag. After Independence, the map got removed. The red circle signifies the blood sacrifice of the martyrs during the liberation war, while the green rectangle is the symbol of the country's green nature. (shrink)

Protopolybia sedula is a social swarming wasp, widely spread throughout many countries in the Americas, including most of Brazil. Despite its distribution, studies of its behavioral ecology are scarce. This study aimed to describe its foraging activity and relation to climatic variables in the city of Juiz de Fora in southeastern Brazil. Three colonies were under observation between 07:00 and 18:00 during April 2012, January 2013, and March 2013. Every 30 minutes, the number of foragers leaving and returning to the (...) colony was registered along with air temperature and relative humidity. Activity began around 07:30¸ increased between 10:30 and 14:30, and ended around 18:30. A mean of 52.7 exits and 54 returns were measured every 30 minutes. The daily mean values were 1,107 ± 510.6 exits and 1,135 ± 854.8 returns. Only one colony showed a significant correlation between forager exits and temperature (rs = 0.8055; P < 0.0001) and between exits and relative humidity (rs = -0.7441; P = 0.0001). This paper shows that climatic variables are likely to have little control on the foraging rhythm of P. sedula when compared to other species, suggesting the interaction of other external and internal factors as stimuli of species foraging behavior. (shrink)