Daily Archives: April 7, 2025

Abstract-Crop productivity is paramount to world food security, and precise crop yield forecasting is critical to maximizing farm operations. Classic forecasting techniques hardly consider the intricacies of interaction between climatic factors, soil properties, and crop growth stages. The rapid progress in Machine Learning (ML) and Deep Learning (DL)in recent years has transformed crop prediction by utilizing extensive data from meteorological archives, soil sensors, and remote sensing technologies. This research examines different ML methods, such as Random Forest (RF), Support Vector Machines (SVM), Artificial Neural Networks (ANN), Long Short-Term Memory (LSTM), and Hybrid AI models, to improve crop yield prediction. By incorporating major agrarian parameters like temperature, rainfall, humidity, soil moisture, and nutrient levels, AI-based models can offer more accurate and dynamic predictions, supporting farmers and policymakers in decision-making.The paper also addresses issues like data quality, model interpretability, and climate change adaptation, and possible solutions like IoT-based real-time monitoring and Explainable AI (XAI).

Abstract-Precise disease diagnosis using symptoms is of paramount importance in efficient healthcare but is frequently incomplete in conventional symptom-checking frameworks that depend upon rule-based techniques or sparse data. Pre-trained in biomedical text for transfer learning tasks, the NLP model literature, for predicting diseases through patient-reported symptoms. Through Bio BERT fine-tuning on open-source symptom-disease datasets, the system accurately maps symptoms to potential diseases, overcoming limitations like symptom variability and overlapping disease presentations. The proposed approach is compared with Naive Bayes (NB), as well as other conventional machine learning models, This includes Bayes, Random Forest, and Support Vector Machines(SVM).Experimental outcomes illustrate that the fine-tuned Bio BERT model has an accuracy rate of 89%, surpassing conventional methods by far. The system is also equipped with capabilities to improve and learn over time by incorporating user feedback to enhance its predictions. This study identifies the possibility of AI-driven symptom checkers to transform healthcare by offering real-time, accurate, and individualized disease prediction, alleviating the pressure on healthcare systems, and enhancing patient outcomes.

Abstract-Electronic trading or algorithmic trading has changed the landscape of financial markets as data is processed and analyzed, which enables instant decision making. The application of reinforcement learning (RL) in algorithmic trading has the ability of constant improvement and optimization in ever- changing environments. Autonomous, intelligent systems that can operate in the unpredictable financial market conditions are required at an ever-growing rate. Trading agents can learn market optimal decision-making strategies through reinforcement learning, which makes it a good fit for real-time usage. The primary aim of this study is to overcome the challenge posed by the traditional algorithmic trading approaches that target high market volatility and non-stationary data using pre- programmed strategies. Most of the published studies are concentrated on the theoretical aspects of the models while very little attention is given to their application, transaction cost, slippage, and market impact. In RL based trading systems, learning needs to be stable or the trader risks overfitting, setting risk parameters for exploration and exploitation can also Markov Decision Process be very difficult. We develop a custom RL framework that compensates for transaction costs at the breakeven point, where other methods fail. Rather than focusing on other reward functions, our method can actually be implemented in real-time trading situations.

Abstract-Phishing attacks, which use people’s vulnerability to trick them into disclosing personal information, continue to be the most widespread threat type, at least for the time being. These types of attacks typically involve phony emails purporting to be from reputable sources, such as banks, companies, or government buildings. Even though standard email filtering methods are somewhat helpful in the fight against phishing, they are very unlikely to detect sophisticated phishing channels like spear phishing and zero-day phishing that are used today. As a result, using machine learning (ML) and artificial intelligence (AI) to address email security is becoming more popular. They can learn from sampled volumes of emails and use that knowledge to better identify phishing and non-phishing emails. Here, we suggest developing a phishing detection system with AI/ML, which will be instrumentally essential to ensuring dependable and flexible email security. To categorize emails according to features taken from the subject, body, and links of the emails, the system uses Random Forest, Support Vec- tor Machines (SVM), and Neural Networks. We trained and evaluated these models to determine the feasibility of phishing identification using both phishing and benign email corpora. The study’s accomplishments included a higher detection accuracy in comparison to traditional methods and a further decrease in misrecognition, both of which enhance security overall. Notably, the suggested system is robust and adaptable to sophisticated phishing attacks by combining a multi-model approach with learning mechanisms.

Abstract-With the progression of machine learning methods recently, a branch of artificial intelligence was revealed for forecasting and prediction in agriculture field. That is a benefit to works that have to do with agriculture. Recent developments in agricultural practices and methods have highlighted the importance of accurate monitoring, particularly with regards to field monitoring such as paddy areas, in order to take timely control measures for food security and other supportive actions. Moreover, regular monitoring of an area, a landscape, and the entire earth is beneficial by using one of the important sources, that is satellite images, providing information through multi-temporal images. Best source of images complexity because they are indifferent of atmospheric conditions like wind, sun light etc. It combines deep learning specifically convolutional neural networks (CNNs)–and satellite imaging to model crop yield. We suggest a hybrid model that uses data from various sources and real-time integration to provide scalability, accuracy, and reliability to solve practical challenges, such as dataset diversity as well as computational efficiency.

Abstract-As we know the ongoing challenges of agriculture sector necessitate the integration of advanced technology to ensure sustainable farming. Agriculture plays a crucial role in our life as food is the important part of human survival, it is necessary to look out for the ongoing problems in agriculture sector. With increasing challenges to the agricultural sector, the integration of advanced technologies into sustainable farming practices is essential to enhance productivity. Being the backbone of the global economy, agriculture faces threats from climate change, resource inefficiencies, and market access limitations. This paper introduces an AI- Powered Smart Agricultural Ecosystem that combines advanced technologies to provide holistic solutions to these issues. The system is supposed to enhance the prediction of weather through AI and allows farmers to take the right decisions in planting and harvesting. IoT-based soil health monitoring helps to assess soil in real-time to save efficient usage of resources, ensuring that crop yield is improved. Blockchain technology gives farmers direct access to the market, thereby reducing intermediaries between buyers and sellers and ensuring transactions are transparent. AI-powered financial services will give personalized credit scores and microloans to facilitate the farmers. By harnessing the most recent advances in technologies, such as machine learning, IoT, and blockchain, this system bridges existing gaps in agricultural research and practices. Unlike the typical approaches, this proposed framework combines predictive analytics, real-time monitoring, and financial inclusion into one integrated ecosystem. While it increases productivity, this will also promote environmental sustainability by improving resource use efficiency. This research is novel because it fully integrates technology to tackle the critical challenges that farmers face. This ecosystem is expected to increase profitability, reduce resource wastage, and ensure better market access, thus significantly contributing to the transformation of the agricultural sector into a more resilient and sustainable domain.

Abstract-The increasing demand for continuous, non-invasive health monitoring has spurred innovations in wearable technology. This paper presents a conceptual framework for a flexible, skin-adhering biosensor tattoo designed to monitor vital physiological parameters-heart rate, hydration levels, and glucose concentration-in real time. Leveraging photoplethysmography (PPG), bioimpedance analysis (BIA), and sweat-based electrochemical sensing, the proposed system integrates ultra-thin sensors, a low-power microcontroller, and Bluetooth Low Energy (BLE) for wireless data transmission to a mobile application. The framework emphasizes energy efficiency through a hybrid power system combining flexible micro-batteries with thermoelectric and piezoelectric energy harvesting. A user-friendly mobile interface provides live health metrics, AI-driven anomaly detection, and historical trend analysis, enhancing proactive healthcare. Unlike conventional wearables, such as smartwatches and continuous glucose monitors, this tattoo-based system offers superior comfort, non-invasiveness, and customization potential, addressing limitations like bulkiness and frequent charging. The design incorporates waterproofing via hydrophobic nano-coatings and biocompatible materials, ensuring durability and skin safety. While currently a research-based concept, the framework builds on established technologies, demonstrating feasibility through existing sensor methodologies and flexible electronics research. This paper outlines the system architecture, technical workflows, and potential applications, targeting athletes, diabetic patients, and health-conscious individuals. Future directions include prototype development, clinical validation, and expanded health metric integration. The proposed system promises to redefine wearable health technology by merging advanced biosensing with seamless, everyday wearability.

Abstract-This research document shows how deep learning helps detect emotional content in social media data. Natural language processing (NLP) department Sentiment analysis discovers text sentiment orientation within documents. Thanks to social media’s fast growth companies and researchers depend on understanding verbal reactions from content producers. Because social media texts follow informal writing styles with slang words and emojis plus frequent hashtag usage they require special handling methods. Deep learning models including CNNs RNNs and Transformers now better recognize linguistic details from text datasets than ever before. The research examines every aspect of using deep learning methods to analyse social media text sentiment including the tools, data sets used, present problems and future prospects. This study proposes new methods to create better sentiment analysis systems for social media platforms.

Abstract-This project is designed to assist in locating individuals trapped in fire emergencies by integrating two interconnected components: fire detection and human identification. The system employs the YCbCr color space standard to detect fire and flames within the environment. To identify individuals amidst the fire, it leverages the HOG combined with a SVM classifier. Motion-based feature selection techniques are utilized for human activity recognition in video sequences. To ensure seamless operation of both modules, they are systematically integrated. Fire detection is carried out using a trained model that incorporates a diverse range of human feature sets. Additionally, moving objects are identified using a combination of a color median filter and background differencing, following four distinct rules. A critical aspect of this approach is the dependency between fire detection and human identification—ensuring that if a fire is detected, the system actively searches for trapped individuals. The primary objective of this methodology is to enhance the efficiency of locating individuals in hazardous fire conditions, enabling rapid rescue operations. This system can support firefighters in strategic decision-making and identifying high-risk zones.