Journal of Artificial Intelligence and Capsule Networks
In this data driven world, Artificial Intelligence [AI] technologies is widely accepted as the major contributor of the digital transformation in diverse application areas of our society. The journal brings together the state-of-the-art research results in all aspects of Artificial Intelligence [AI] with a particular emphasis on image processing and computer vision models. Artificial Intelligence and Capsule Networks [AICN] Journal serves as a platform for showcasing the research efforts of academicians, scholar and application developers in these domains.
Hierarchical Sparse Vision Transformers for Real-Time Drone-Based Object Detection
Object detectors based on transformers offer high contextual modeling but have a quadratic complexity of attention, which has restricted their application to real-time in aerial settings. The proposal presented in this paper is a Scalable Adaptive Hierarchical Attention Transformer (SAHAT-Det) that is proposed to be effective at detecting objects and objects in drone imagery. The framework presents the concept of dynamic relevance-based token scoring, top K sparse attention calculation, and adaptive token pruning to lower the computational cost. A multi-scale hierarchy fusion module retains small-scale spatial details especially of objects that are small and far away. On the VisDrone dataset, experimental results show a higher mAP of 0.5:0.95 and small-object detection accuracy than the latest CNN and transformer-based baselines that are run under the same configuration. Although there is less attention computation, the suggested model still retains close real-time inference speed. The qualitative analysis also proves enhanced localization stability in high density urbanized scenes. The obtained results show that adaptive sparse attention offers an efficient compromise between the accuracy of detection and processing cost in real-time aerial object detection.
Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
AI Based Early Childhood Glaucoma Risk Screening System Using Eye Image Analysis
This study describes an artificial intelligence-inspired, rule-based framework for early detection of risk of developing childhood glaucoma through analysis of the child's retina using colour fundus images. The system will first employ a series of pre-processing techniques on the input fundus image including conversion to greyscale format, noise reduction and contrast enhancement prior to finding the optic disc and optic cup in the images through use of a variety of image processing methods. From these two areas, the vertical Cup-to-disc ratio (CDR), which has been scientifically shown to be an important clinical measurement parameter for assessing the potential presence or future development of glaucoma, is calculated. The CDR calculation can be used to classify an individual as being at low, moderate or high risk of developing childhood glaucoma. The final implementation of the system will be as a web-based application where users can take a digital retinal image using a scanner or upload an existing image for analysis. Experimental results indicate that the designed system will yield quality results that are reliable and efficient for use as an initial screening procedure for childhood glaucoma. The system is also designed to be low-cost and readily accessible so that it can be used in a variety of possible clinical settings such as schools for children, rural healthcare clinics and within telemedicine environments to facilitate early detection of childhood glaucoma.
Smart Tourist Safety and Emergency Response System Using Rule-Based Risk Detection
In this research paper, the increasing traveler safety issues due to the growth of tourism have been analyzed, and limitations in current safety systems used to detect traveler risks have been highlighted. For addressing these traveler safety issues, this research proposes Rule-Based Smart Tourist Safety and Emergency Response System, which will ensure monitoring of user safety through real-time data. This system estimates a risk score dynamically depending on multiple parameters like time spent on travel, distance from risk areas, nearby emergency cases, and user's movement pattern from their GPS data. According to the calculated risk score, there is a risk classification module based on rules which categorize the risk scores into Low, Medium, and High levels. In case of any emergency situations, the system can rapidly send the user's location, time stamp of incident along with any visual information related to the situation to a centralized monitoring system. There is also an Incident Visualization Module in the proposed system that ensures proactive decisions by identifying potential hazardous locations in real time. Experimental results indicate a successful detection rate of 90%-92% in negligible time intervals.
AI-Driven Climate Based Control and Energy Analytics for Smart Buildings
With the ever-increasing demands on energy usage in modern buildings, there has emerged the need for intelligent and efficient energy management systems that will be able to optimize resource use without compromising occupant comfort. This study introduces a novel framework for climate-based control and energy analytics in smart buildings using Internet of Things sensors and AI-based predictive analytics for adaptive energy management. Real-time environmental data collection and intelligent decision support were used to optimize lighting, ventilation, and climate control systems' operations in dynamic environments. In this regard, the proposed framework applied a hybrid control approach that integrated automatic control based on sensor readings and predictive analytics to minimize unnecessary operation of appliances, hence promoting energy efficiency. Performance evaluation using traditional Full Load Mode and AI-Based Mode revealed that the proposed solution was more efficient in energy utilization compared to the existing systems. It was able to conserve about 30% of energy consumption, reducing power usage from 4311 mW to 2874 mW. Additionally, the proposed predictive analytics model showed an estimated operational efficiency of 85.51%.
Message Queuing Telemetry Transport (MQTT)
Blinkora: A Smart Digital Wellbeing App for Eye Health and Screen-Time Management
Screen time is among the major factors leading to the increasing cases of eye strain and other health problems in the world today. In this regard, there is the need to develop solutions to help address this problem. Hence, this research introduces Blinkora, an android-based smart system that helps to analyze and control excessive screen usage. The proposed system combines application usage analytics and face detection capabilities on-device for the effective tracking of user interaction. Using the built-in device camera, the system is capable of confirming the active participation of the user(s) on the screen. The Blinkora system uses Android Usage Stats APIs and simple image processing algorithms to perform application-level analytics and face detection respectively. Moreover, it calculates measures of exposure levels and addiction scores that measure dependency on screens. The entire process occurs on the device itself to achieve efficiency and guarantee privacy. Experiments conducted show that Blinkora efficiently merges the processes of monitoring, analyzing, and providing feedback to help increase user awareness. The presented method provides an effective solution that is both feasible and efficient for controlling screen usage and protecting one’s eyes from harm.
Glacier Growth Monitoring Using YOLOv8 and Feature-Based Classification
The rising threat of climate change has raised the need for constant glacier surveillance, since their fast growth may pose an immediate danger to the environment and cause disasters. However, conventional approaches to glaciers' monitoring are dependent on challenging and irregular manual analysis of satellite images. In this paper, a new combined methodology for automated glacier growth monitoring and risk assessment based on satellite imagery is introduced. The system combines a glacier segmentation model based on deep learning with a classification algorithm that utilizes ensemble machine learning to predict a risk level. The YOLOv8 segmentation network is used to identify the glacier outlines from a set of satellite images and generate masks to be further analyzed. The masks are used to extract features representing the glacier area, shape, and growth pattern, which are later used by a random forest and an extreme gradient boosting algorithm to determine the risk level by applying the soft-voting method. A real glacier lake dataset is used to test the system and demonstrate its performance and accuracy in identifying risky and non-risky growth.
LiBile: Mobile-Based Library Management System
The growing dependence on digital technology has considerably affected the modernization of traditional systems. This paper discusses the development of a mobile-based library information system named LiBile. The system aims to improve the accessibility, efficiency, and involvement of users in library services. Traditional library information systems usually employ manual approaches and desktop applications. This has limited their accessibility and increased the risk of errors and time consumption. LiBile aims to overcome these limitations by offering a mobile-based system that allows users to access library services anywhere and at any time. The system allows users to search for books, check availability, borrow and return books, and receive timely updates on due dates and other information. Additionally, it offers efficient services to librarians for managing books, transactions, and users. The system employs a cloud-based database to ensure real-time data synchronization. It also includes a user-friendly interface to facilitate both users and librarians. The process of developing LiBile involves a systematic approach to system development, which includes requirement analysis, system design, implementation, and testing. The results have shown improvements in terms of efficiency, human effort, and user satisfaction compared to traditional systems. LiBile offers a viable solution for modern libraries through the use of mobile technology and real-time data management. The system not only provides quality service to users but also meets the growing trend of digitalization within institutions.
A Hybrid Deep Learning Framework for Air Quality Index Forecasting
The Air Quality Index (AQI) must be accurately and highly evaluated to limit the effects when exposed to air pollution on individual health and the ecosystem. The traditional techniques are used to predict air quality ineffective in managing the nature of the environmental data being analyzed and minimizing the effects of the most significant features of large numbers of dimensions. The purpose of this work is to address these issues using the creation of a hybrid deep learning algorithm implementing Feature Selection Techniques, Recurrent Neural Networks and a Quantum-Inspired Genetic Algorithm (QIGA). The eXtreme Gradient Boosting (XGBoost) will be applied to the environmental dataset to evaluate importance of features. Additionally, the Principal Component Analysis (PCA) will reduce the dataset's dimensionality using most significant features from the original dataset as inputs for the prediction model. Recurrent Neural Networks (RNNs) are able to detect time-variant patterns of air quality (i.e., the pattern changes over time) based on the use of controlled memory. The use of quantum-based methodologies will allow rapid searches over high-dimensional datasets resulting higher performance than traditional optimization methods. This innovative methodology will lead to improved accuracy, computational efficiency and interpretability of AQI predictions. This method will be the basis for smart environmental monitoring systems used by researchers, policymakers and urban planners to make innovative decisions. Finally, the flexibility of the system makes it possible for users to apply it in any other forecasting-based environmental issues showing the scalability and efficiency.
An Intelligent Shopping Cart Chatbot Using Amazon Lex
The advanced Artificial Intelligence (AI) technology has smart agents to improve the customer satisfaction and efficient operation for e-commerce applications. The chatbots facilitate the communication between the customers that saves the human intervention, increase the speed of response and quality of services automatically. This proposed work explains the development and implementation of an AI-based customer service chatbot with the help of Amazon Lex for online shopping. This work also discusses the improved quality of response. When the data has been received, the system will detect the customer objective and guide them using a series of questions based on products, delivery information, payment methods and general shopping support. The chatbot will evaluate the user input, receive key parameters and provide customized responses with the help of Amazon Lex's Natural Language Understanding (NLU) and the slot-mapping functionalities. This system recognizes the purpose of the response and generates effectively for customers can receive the relevant data about the details of products and their services rapidly. This paper also shows the AI-based conversational interfaces improve the customer experience, automatic communication and support customer service in online shopping. When compared to the rule-based and proposed chatbot model improves the classification accuracy as 92% with accurate response metrics.
Quantum Machine Learning Approaches for High-Dimensional Optimization Problems: A Survey
Recent advances in Quantum Machine Learning (QML) bring together Quantum Computing and Machine Learning with the aim of enhancing the advantages of Machine Learning to solve problems efficiently. The properties of quantum systems (superposition, entanglement, etc.) to represent multiple states of information at once, QML can potentially offer significant advantages when solving complex high-dimensional (multidimensional) computational problems. This paper reviews the current state-of-art regarding QML in general through an organized compilation and assessment of selected studies recently published in scientific literature that were found via large electronic library catalogs based on peer-reviewed sources according to algorithms used, optimization approaches employed, and areas of applied study. This review provides comparative analyses of each selected studies for purposes of identifying major trends in QML research/practice as well as opportunities for methodological improvements and challenges still existing within QML. Finally, this review presents an overview of Quantum Computing and Quantum Data Encoding Principles utilized to translate Classical Data into Quantum States as a high-level evaluation of several popular Quantum Learning Algorithms (e.g., Quantum Neural Networks, Quantum Support Machines, and Variational Quantum Circuit). The survey describes the optimization strategies such as gradient based techniques, Bayesian Optimization and evolutionary techniques being used to improve the stability of the quantum machine learning (QML) training process. Then, it provides examples of application areas for such QML, where healthcare analytics, environmental modelling, manufacturing optimization and analysis of biologic data are included. The comparative review of the recent academic research has identified several limitations and challenges regarding the use of QML that researchers. Although QML is believed to be capable of solving complex computational problems, issues such as noise in hardware, limited numbers of qubits and scalability present significant challenges for researchers working with this type of machine learning application. Overall, it appears that this survey provides a broad overview of contemporary developments and future trends for developing high-performance quantum learning systems.
Ethics-Aware Personalization: A Dual-Objective AI Framework for Engagement Optimization
AI-powered personalization systems have enhanced digital services by increasing user engagement, retention, and conversions. However, performance-maximizing personalization approaches compromise privacy, increase bias, and decrease transparency. This article uses a multipurpose personalization system that can simultaneously increase engagement and uphold ethics using Festinger's Social Comparison Theory and Skinner's Reinforcement Theory. The system consists of social comparison-based personalization modules, reinforcement learning, and an ethics-focused system layer for re-ranking, explainability, and privacy-preserving re-learning assumptions. The system's performance is validated using Python simulations for 1.2 million customer-level interaction data, showing improved proxy transparency and reduced equality in exposure bias with unchanged engagement performance. The research explains and creates the Ethical Experience Index (EEI), a measure of both engagement and ethics performance experiences for a systematic evaluation and comparison of performance. The results show the potential of integrating ethics systems for personalization that provide a repeatable, theory-based approach to ethics and AI-driven personalization based on simulations.
An In-Browser Proctoring System Using YOLO-Based Object Detection and Gaze Analysis
The development of remote learning has increased requirements for effective online evaluations, but the lack of physical supervision can lead to inappropriate actions such as invisible support, illegal collaboration, and the use of restricted resources. This work represents a web-based automated proctoring solution that uses a camera and a lightweight backend system to reduce the need for constant monitoring by avoiding complicated client-side implementation. The proposed system consists of three major components such as a YOLOv8s, a Dlib application and a web-based system. The YOLOv8s vision module detects human faces and restricted objects (such as mobile phones and books), a Dlib facial recognition application estimates eye and head direction to identify behavioral changes and the web-based monitor detects the behaviors like switching tabs, reducing the window size and exiting full-screen mode. A rule-based layer analyzes these signals to determine the type of violation and takes necessary action such as providing real-time notifications and expanding procedures for repeated errors. The detected activities will be saved as structured recorded files in a MySQL database for transparency and scientific evaluation with suitable supporting links shown on administrator dashboards for monitoring after the evaluation. This experimental evaluation and dashboard examples demonstrate that the system will monitor proctoring actions in real-time and provide accurate, reviewable data suitable for remote conditions.
Comparative Analysis of Sports-Inspired Optimization Algorithms in Neural Network Training: A Benchmark Study
Nine population-based optimization algorithms, six of which are sports-inspired and three classical natural-inspired, are applied in this work to neural network hyperparameter tuning. The results of the sport-inspired algorithms, namely the Most Valuable Player Algorithm, Soccer League Champions Algorithm, Golden Ball Algorithm, Tiki-Taka Algorithm, Tug of War Optimization, and World Cup Optimization, will be compared against classical metaheuristics, namely Particle Swarm Optimization, Differential Evolution, and Genetic Algorithm. Each optimizer was evaluated under identical experimental conditions on a neural network classifier using the Iris dataset to optimize four key hyperparameters: the number of hidden layers, neurons per layer, learning rate, and L2 regularization coefficient. Due to its hierarchical competition structure, MVPA had the highest classification accuracy (99.33%), followed by SLCA (98.67%), but it came at a higher computational cost. GBA and PSO showed very strong performances with efficient convergence, while DE, TOW, and WCO resulted in relatively lower accuracies of ~97–98% but achieved fast convergence, which is very suitable for time-critical applications. The results show that sports-inspired metaheuristics are robust and effective frameworks for neural network hyperparameter optimization. Their competition-driven strategic mechanisms enable efficient search behavior that is on par with, and occasionally even better than, that of more conventional evolutionary algorithms. These results provide a solid basis for hybrid, adaptive algorithms in the future related to sports.
Integrating Automatic Speech Recognition and Emotion Detection: A Conformer-XGBoost Framework for Human-Centered Speech Systems
Advanced speech technology pushes human-machine interaction to a new frontier. Most of the models address this either as a matter of speech-to-text transcription or emotion detection. By integrating an XGBoost-driven emotion classification component with a Conformer-based speech recognition system, an integrated solution has been developed. It will, therefore, strive to transcribe spoken utterances and estimate the emotional condition of the speaker with as much accuracy as possible to improve context-sensitive interaction. The transcription process combines large, multilingual speech corpora. A Conformer architecture captures both short- and long-range temporal dependencies. In this regard, an error rate of 0.322 words and 0.146 characters was achieved in transcription. For emotion recognition, several emotional speech datasets were collected, and various acoustic features were extracted under noisy conditions. Using an XGBoost model, 86.58% accuracy in emotion detection was attained. These results demonstrate the feasibility of integrating speech transcription with emotion recognition and form a basis for the further development of more human-like, empathic, and adaptive voice systems.
Adaptive Metaheuristic Optimization of AraBERT for Arabic NER: A Comparative Study of PSO and TLBO
Despite the state-of-the-art results obtained with transformer-based models like AraBERT, Arabic NER still appears to be very sensitive to the setting of hyperparameters. This usually requires a lot of manual tuning; the process is inefficient, time-consuming, and often results in suboptimal performance. In the context of this paper, an automatic framework for hyperparameter optimization was suggested using metaheuristic algorithms: PSO and TLBO. This is the first comparative research on the efficiency of metaheuristic algorithms for the optimization of Arabic NER. In this regard, this paper has applied these algorithms using the Wojood dataset and the aubmindlab/bert-base-arabertv2 to optimize the main hyperparameters: learning rate, batch size, and the number of epochs. The results revealed that PSO outperforms TLBO and a traditional approach for all test Micro-F1 scores, achieving a maximum of 0.8813, compared to 0.8755 for TLBO. Furthermore, PSO outperformed TLBO in terms of mean performance: 0.8708 versus 0.8561, with better convergence stability: a standard deviation of 0.0164 versus 0.0286. Although TLBO converged slightly faster than PSO, PSO demonstrated more robust generalization and reliability across runs. Overall, the findings confirm that metaheuristic optimization can substantially improve Arabic NER, with PSO standing out as the most effective, stable, and efficient optimizer for transformer-based Arabic NLP models.
Generative Adversarial Networks (GANs): A Comprehensive Review of Architectures, Training Challenges, and Advancements
Amongst generative models, GANs have been one of the most powerful families that have exerted an influence in domains such as artificial intelligence and deep learning. Since their discovery by Ian Goodfellow back in 2014, GANs have generated realistic data, from images and videos up to audio, in a wide range of applications. The adversarial training paradigm allows GANs to learn sophisticated data distributions without explicit supervision, including a generator and a discriminator. However, despite those benefits, training GANs is inherently problematic since instability, mode collapse, and issues of convergence naturally pop up. This contribution reviews how GAN architecture has evolved, critically discusses the main challenges regarding GAN training, revisits the most promising developments toward enhancing stability and performance, and addresses rising trends such as diffusion models and hybrid frameworks. Furthermore, this paper points to the directions in which further research should be oriented with a view to the improvement of theoretical grounds, stability, and universality of GANs for practical use.
Hybrid Bi-LSTM Framework for Aspect-Based Sentiment Analysis in E-Commerce Reviews
Aspect-Based Sentiment Classification (ABSC) is the core technology for e-commerce websites, offering business entities in-depth customer preference information through online review sentiment extraction. In this paper, a Hybrid Bi-LSTM Framework is proposed to integrate weak supervision with deep learning models to increase the accuracy of sentiment classification. The approach is based on aspect term extraction, weak labeling with Snorkel-based methods, and a multi-model sentiment analysis method known as HABSC (Hybrid Aspect-Based Sentiment Classification). It uses Amazon product review datasets to evaluate the performance difference between a Hybrid Bi-LSTM model, the HABSC algorithm, and an ABSC DeBERT model in sentiment classification for five important aspects: price, quality, usability, size, and service. Experimental results indicate that Hybrid Bi-LSTM performs better than ABSC DeBERT, achieving 93.4% accuracy, lower Hamming loss, and improved precision, recall, and F1-score. Comparative analysis also reflects the performance of an ensemble-based sentiment analysis approach employing VADER, SentiWordNet, and BERT scoring. This article contributes to enhanced automated aspect-based sentiment analysis, presenting an efficient and scalable solution for recommendation systems, business intelligence, and e-commerce websites.
Aspect Based Sentiment Classification[ABSC]
Comparative Analysis of Supervised Learning Algorithms for Clinical Diagnosis Heart Attack Prediction
This paper involves comparative research of novel deep machine learning and machine learning networks for the prediction of heart disease symptoms. Heart disease is one of the leading causes of death worldwide, and its early discovery saves a person's life. The objective of the research was to create a method of predicting heart attacks from lifestyle and clinical data through machine learning. The Machine learning algorithms that will be employed for prediction are discovered by conducting experiments that test combination of ML algorithms such as Logistic Regression, Random Forest, Support Vector Machines, and Gradient Boosting in the study. Data sets are downloaded from public sources and are processed to completion with missing value management, feature scaling, and exploratory data analysis (EDA). Validation has been performed using accuracy, precision, recall, and F1-score. The Random Forest classifier performed with an accuracy of 89%. The paper also discusses constraints and how to follow up the work, such as using larger data sets and new deep learning algorithms.
Efficient Ensemble of Pre-Trained CNN Models for Improving Classification of Maize Diseases
Maize is one of the world's leading staples crops, whose yields are compromised by foliar diseases such as rust, blight, and gray leaf spot. Speedy and correct diagnosis must be employed to reduce loss and maintain food security. Deep learning or Convolutional Neural Networks (CNNs) has been promising for auto-classifying diseases, but one-model systems are not robust and generalizable. Moreover, previous research did not address issues of dataset imbalance leaf orientation variation in the out-worldly setting, and field deployability for precision farming. In this research, a feature ensemble-fusion classifier model for maize disease identification is presented. Pre-processing and augmentation were performed on a 4,188-leaf image dataset divided into four classes (Common Rust, Gray Leaf Spot, Blight, and Healthy). Six pre-trained CNNs (EfficientNetB0, DenseNet201, ResNet50V2, NasNetMobile, MobileNetV2, and VGG16) were tested in frozen and partially fine-tuned states. EfficientNetB0, DenseNet201, and ResNet50V2 were the top three models averaged using an ensemble approach. The proposed system achieved a total average of 98% accuracy across all diseases, with 97% precision, 96% recall, and 96.5% F1-score, and at least 92% precision, 96% recall, and 96% F1-score on Gray Leaf Spot disease thus performing better than single CNNs. With improved class imbalance handling and environmental robustness, the scheme offers an efficient and adaptive solution to real-world maize disease diagnosis and precision agriculture.
Enhanced Sales Forecasting in Corporate Cafeterias: An LSTM Approach
A key metric for improving business cafeteria operations, inventory control, and customer satisfaction is accurate sales forecasting. k-Nearest Neighbors (k-NN) and other standard machine learning algorithms usually fail to identify temporal patterns in sales data, which leads to predictions that are not accurate. We provide a better forecasting technique that overcomes this limitation by utilizing Long Short-Term Memory (LSTM) networks, a specific kind of recurrent neural network that can recognize long-term dependencies in sequential data. The performances of LSTM and k-NN were compared and evaluated using a Kaggle corporate cafeteria sales dataset. Based on experimental results, the LSTM model outperformed k-NN with a mean prediction accuracy of 87.57% compared to 83.99%. The significance of the improvement (p = 0.038) was validated by statistical testing with an independent samples t-test.
Lung cancer remains one of the leading causes of death worldwide, mainly due to delayed diagnosis and treatment. Lung nodules must be properly and quickly recognized as benign or malignant to improve survival. This work presents a deep learning approach for segmentation using the Residual U-Net (ResiU-Net) architecture improved with Multiscale Aggregation (MSA) focusing on accurately identifying lung nodules from CT scans. A total of 1,190 labeled CT scan slices from the publicly available IQ-OTHNCCD dataset, including the benign, normal, and malignant classes, are used to test the model. To enhance feature representation and data balance, advanced preprocessing methods such as data augmentation and standardization were used. The new ResiU-Net architecture makes use of residual connections to overcome vanishing gradients and achieve contextual depth, while the MSA block enables the collection of global and fine-grained information required for the identification of small nodules (as small as 3 mm). A rigorous 5-fold cross-validation technique was used to test the model's performance. The strength of segmentation was evaluated using metrics such as F1-Score, Accuracy, Dice Score, and Intersection over Union (IoU). The method demonstrated better detection accuracy and excellent generalization compared to baseline U-Net models with an F1-score of 0.8385 and a best IoU of 0.7971 and validation IoU of 0.7300. The model's ability to accurately recognize nodules of varying sizes and shapes has been established by the testing results. The goal of the project is to help design a clinical decision-support system that is accurate, automated, and affordable for monitoring and early-stage lung cancer diagnosis.
Role of Artificial Intelligence in Enhancing School Digital Twins
A School Digital Twin system powered by AI has been deployed to make educational spaces more responsive and intelligent through real-time monitoring, self-governing decision-making, and decentralized participation. Utilizing cameras, IoT sensors, artificial intelligence, and blockchain-powered smart contracts, the system monitors and interprets real-time data from classrooms to build a dynamic virtual replica of every learning environment. This virtual twin enables timely, data-driven interventions intended to maximize classroom conditions. While AI oversees environmental adaptations, blockchain guarantees transparency and verifiability of decision-making processes. By coupling advanced technological infrastructure with pedagogical goals, the system aims to facilitate more adaptive, resource-effective, and learner-centred education practices. Finally, it aims to enhance learning conditions, promote evidence-informed governance, and increase academic achievement through personalization and intelligent adaptation.
Intelligent Neuroimage-based Stroke Diagnosis Using Deep Learning and Dual Web Interface for Clinical Use
One of the main causes of death and permanent disability, stroke needs to be diagnosed as soon as possible, to guarantee early treatment as soon as possible. Traditional stroke detection techniques mostly depend on radiologists who manually interpret neuroimage, which can be laborious and subjective. Many deep learning models lack integration with a user-central design or deployed clinical equipment, despite the promise to detect strokes despite recent advances. The study introduces a novel AI-powered structure to an early stroke classification from CT and MRI neuroimage that uses a modified VGG16-based Convolutional Neural Network (CNN). Our technology, unlike traditional models, includes a dual-interface web platform, designed for patients and physicians, which is filled with AI-Interested Chatbot, automated report distribution and a safe database. The suggested model was a high precision and remembered and a classification accuracy of 94.6%. These findings support the efficacy of the framework and demonstrate their ability for practical clinical integration, especially in rural or deprived healthcare settings.
Intelligent Motion based Animal Repulsion System for Agriculture
The protection of crops from birds and animals is a significant concern for farmers worldwide. Birds and animals such as crows, sparrows, goats, and cows often cause substantial damage to crops, leading to financial losses and reduced yields. Traditional methods of crop protection, such as scarecrows or chemical repellents, have limitations in their effectiveness and sustainability. This research has developed a prototype designed for the detection and repulsion of birds and animals within agricultural environments. The prototype features a motion detection system and an object detection model, which activates a repellent that generates a certain frequency to disturb the birds and animals. This prototype consists of PIR sensors as detectors to detect movement and a camera to capture real-time footage for object detection. If and only if the detected object is a bird or an animal, a signal is sent to the Arduino. The Arduino then determines the respective frequency to be played through a frequency-generating system. The intrusion will be repelled by the emitted ultrasonic waves.
Smart Ration Tracking and FIFO Management
Conventional ration distribution systems exhibit certain limitations, including stock outages and poor management of stocks, which result in wastage of resources and unequal distribution. This paper suggests implementing an AI-based Smart Ration Tracking System enriched with blockchain technology to enhance the transparency, security, and effectiveness of public distribution. Through the use of smart contracts, the system manages the distribution of rations directly to the beneficiaries, thus reducing cases of fraud and false claims. FIFO (First In, First Out) is a stock control strategy that helps to reduce goods waste and increase the efficiency of the supply chain by using the older inventory before the new one. The AI-based analytics help in the correct identification of demand patterns to avoid shortages or overstocking situations. The decentralized and immutable ledger of blockchain increases stock accountability by providing accurate information about all transactions made. With this system enhanced by AI, the rations can be delivered to the beneficiaries properly and on time, with no possibility of interference or undue delay.
First in first out (FIFO) stock management
A Deep Learning Approach for Leaf Disease Detection
To guarantee agricultural output and stop the spread of disease, early detection and treatment of plant diseases are essential. The problem of classifying plant leaf diseases is addressed in this article using a novel approach based on the EfficientNetV2 model. The model's complex structure makes it possible to accurately diagnose a variety of diseases by effectively differentiating them based on minute variations in leaf characteristics. This study shows that deep learning approaches can accurately detect disease patterns and symptoms, which can be used as a tool to detect and provide evidence of future changes in the disease state. Our recommended method outperforms conventional disease management techniques in terms of performance effectiveness and quality by leveraging cutting-edge technologies. EfficientNetV2 easily outperforms conventional deep learning techniques on all major evaluation metrics, according to a comparative analysis with a baseline CNN model. In addition to the technology, the approach has important ramifications for the agricultural methodology framework as a whole. By empowering stakeholders to automatically and precisely identify diseases that can improve crop health and yield, our method has the potential to completely transform disease management strategies. In order to ensure food security and sustainability globally, this article demonstrates how the clever integration of new technologies with agricultural systems has the potential to revolutionize active disease management.
Emotion Recognition System from Facial Expressions Using Machine Learning
Facial Emotion Recognition (FER) enables automatic classification detection of human emotions from facial expressions using deep learning (DL) and computer vision techniques. In this paper, a hybrid real-time emotion recognition system using Convolutional Neural Networks (CNNs), OpenCV, and DeepFace is proposed to achieve accurate and dynamic emotion analysis. The system aims to identify emotions such as happiness, sadness, anger, surprise, and fear with a high level of accuracy to enable enhanced Human-Computer Interaction (HCI), mental health monitoring, and smart safety systems. The method used includes offline training of the model and real-time emotion recognition using video. The technique employs continuous learning and optimization strategies to maximize recognition rates and resilience in practical environments.
Deep Autoencoder-Based Anomaly Detection for Intelligent Network Slice Monitoring in B5G Networks
Anomaly detection in 5G network slicing is critical for ensuring the security and reliability of next-generation telecommunications infrastructure. This study presents a deep autoencoder-based framework for unsupervised anomaly detection in network traffic, leveraging a comprehensive dataset of 210,786 samples with a realistic anomaly rate of 4.5%. The proposed approach incorporates advanced preprocessing techniques, including normalization, interpolation, and oversampling, and prioritizes key network features identified through correlation analysis. The autoencoder model is trained exclusively on normal traffic to learn baseline behaviour, with anomalies detected via reconstruction error analysis. Experimental results demonstrate that the model achieves robust separation between normal and anomalous samples, identifying 1,904 anomalies with a clear margin in reconstruction error statistics. Comparative evaluation with traditional unsupervised methods, such as Isolation Forest, PCA, and One-Class SVM, highlights the superior sensitivity and adaptability of the deep learning approach. The findings underscore the potential of autoencoder-based models for real-time, interpretable anomaly monitoring in highly dynamic and imbalanced 5G network environments, paving the way for more resilient and intelligent network management solutions.
Meta-Heuristic Enhanced Deep Learning Model for Mango Blossom and Stem Disease Classification
This study presents an innovative classification framework for detecting diseases affecting mango blossoms and stems by integrating deep learning techniques with a meta-heuristic optimization strategy. A dataset consisting of 3,500 images, collected directly from mango orchards across the Konkan region in Maharashtra, is used for training and evaluation. To enhance image clarity and enable effective feature extraction, Contrast-Limited Adaptive Histogram Equalization (CLAHE) is applied during preprocessing. Deep features are subsequently extracted using Convolutional Neural Networks (CNNs). To optimize the extracted feature space, a novel hybrid algorithm, Adaptive Squirrel-Grey Wolf Search Optimization (AS-GWSO) is introduced, boosting both processing efficiency and classification precision. For the final classification stage, an Enhanced Long Short-Term Memory (E-LSTM) model is employed, which utilizes the optimized features to improve generalization and minimize overfitting. Experimental results demonstrate that the proposed AS-GWSO-LSTM model outperforms existing classifiers and meta-heuristic-based models, achieving a high accuracy rate of 97.2%. The findings highlight the model’s strong applicability for real-time agricultural disease monitoring systems.
Intelligent FAQ Chatbot: A User-Centric Approach using Large Language Models
The use of increasingly automated systems for tasks like customer support and information retrieval is considerably changing industries such as education, healthcare, and e-commerce. Typical FAQ chatbots often rely on rule-based or keyword-matching algorithms, which limit their ability to address complex questions, adapt to personalized contexts, and learn from interactions. This undertaking introduces a novel chatbot for frequently asked questions, which uses advanced LLMs to tackle these issues. Gemini is an advanced LLM, and the LangChain allows for advanced context management. This system improves comprehension of user intent and also generates natural, contextually relevant responses, progressing beyond static, database-dependent answers. It uses user-specific data, such as interaction history and preferences, to customize different interactions and generally increase user satisfaction. This framework directly addresses scalability challenges as well as delivers remarkably smart, user-centric automation. It does so for contemporary customer support along with information retrieval applications through thoughtfully combining contextual understanding, in addition to personalization. This approach illustrates the transition from prescriptive, rule-driven systems to more comprehensive, learning-oriented driving systems. It enhances the system’s effectiveness and user experience while encouraging the growth of advanced, user-friendly automation help systems.