Journal of Artificial Intelligence and Capsule Networks
type: channel
description: 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.
publisher: Inventive Research Organization
language: en
publication name: Journal of Artificial Intelligence and Capsule Networks
issn: 2582-2012
items: Reversible Data Hiding in Encrypted Images
items: A Heterogeneous Graph Attention Network with Pre-trained Language Models for Multi-Modal Fake News Detection
items: AI-Driven Early Detection and Classification of Skin Diseases Using Deep Learning and Mobile Edge Computing
items: YOLOv8-Assisted Knee Osteoarthritis Severity Assessment from X-ray Images
items: AUREL:AI-Driven Blockchain Platform for Authentic Product Verification and Trusted Recommendations
items: Non-Invasive Disease Prediction Using Tongue Images
items: Natural Language Driven Leave Management Using Lang Chain Agents and Few-Shot Learning
items: Hierarchical Sparse Vision Transformers for Real-Time Drone-Based Object Detection
items: AI Based Early Childhood Glaucoma Risk Screening System Using Eye Image Analysis
items: Smart Tourist Safety and Emergency Response System Using Rule-Based Risk Detection
items: AI-Driven Climate Based Control and Energy Analytics for Smart Buildings
items: Blinkora: A Smart Digital Wellbeing App for Eye Health and Screen-Time Management
items: Glacier Growth Monitoring Using YOLOv8 and Feature-Based Classification
items: LiBile: Mobile-Based Library Management System
items: A Hybrid Deep Learning Framework for Air Quality Index Forecasting
items: An Intelligent Shopping Cart Chatbot Using Amazon Lex
items: Quantum Machine Learning Approaches for High-Dimensional Optimization Problems: A Survey
items: Ethics-Aware Personalization: A Dual-Objective AI Framework for Engagement Optimization
items: An In-Browser Proctoring System Using YOLO-Based Object Detection and Gaze Analysis
items: Comparative Analysis of Sports-Inspired Optimization Algorithms in Neural Network Training: A Benchmark Study
items: Integrating Automatic Speech Recognition and Emotion Detection: A Conformer-XGBoost Framework for Human-Centered Speech Systems
items: Adaptive Metaheuristic Optimization of AraBERT for Arabic NER: A Comparative Study of PSO and TLBO
items: Generative Adversarial Networks (GANs): A Comprehensive Review of Architectures, Training Challenges, and Advancements
items: Hybrid Bi-LSTM Framework for Aspect-Based Sentiment Analysis in E-Commerce Reviews
items: Comparative Analysis of Supervised Learning Algorithms for Clinical Diagnosis Heart Attack Prediction
items: Efficient Ensemble of Pre-Trained CNN Models for Improving Classification of Maize Diseases
items: Enhanced Sales Forecasting in Corporate Cafeterias: An LSTM Approach
items: Pulmonary Nodule Segmentation
items: Role of Artificial Intelligence in Enhancing School Digital Twins
items: Intelligent Neuroimage-based Stroke Diagnosis Using Deep Learning and Dual Web Interface for Clinical Use

Reversible Data Hiding in Encrypted Images
type: item
description: The growing demand for secure digital communication has led to the necessity of developing strong schemes that can provide confidentiality, integrity, and information recovery. In general, traditional image encryption and steganography methods suffer from limitations due to the reduction in either embedding capacity or visual quality. In this paper, a novel scheme for secure reversible data hiding of an encrypted image based on the combination of Password-Based Key Derivation Function 2 (PBKDF2), Advanced Encryption Standard in Galois/Counter Mode (AES-GCM), and Most Significant Bit (MSB) embedding is introduced. In the first place, PBKDF2 uses a password chosen by the user to derive a secret key, which is used in order to encrypt the secret message using AES-GCM. Then, the generated ciphertext is embedded into the cover image via MSB substitution method. The experimental analysis performed with the help of USC-SIPI image database shows that the suggested scheme provides 52.36 dB PSNR, 0.45 MSE, 0.99 SSIM and 2.84 bpp embedding capacity.
subject: Articles
  topic: section
subject: Reversible Data Hiding in Encrypted Images (RDH-EI)
  topic: keywords
subject: Authenticated Image Encryption
  topic: keywords
subject: Password-Based Key Derivation Function (PBKDF2)
  topic: keywords
subject: AES-GCM Cryptographic Security
  topic: keywords
subject: Imperceptible Data Embedding
  topic: keywords
creator: Raagul P.
creator: Subash P R.
creator: Deepak Indirajith M C.
creator: Sathya T.
rights: Copyright (c)
date: 2026-07-29
publication date: 2026-07-29
starting page: 256
ending page: 271
doi: 10.36548/jaicn.2026.3.007

A Heterogeneous Graph Attention Network with Pre-trained Language Models for Multi-Modal Fake News Detection
type: item
description: The spreading of fake news via online social media has emerged as one of the major issues in the modern information systems. The existing fake news detection techniques mainly rely on text-based classification methods and are ineffective in incorporating multimodal features, which can be used for fake news detection and classification. This study proposes HGAT-BERT, where a pretrained BERT-Large encoder is coupled with a Heterogeneous Graph Attention Network (HGAT) to learn joint representations for textual, social network and external knowledge graph features. Cross-modal attention and gated residual connections facilitate the integration of these heterogeneous feature streams into a unified representation. On the FakeNewsNet dataset split on PolitiFact and GossipCop data, the research reports an accuracy of 93.7% and an F1 score of 93.2%, an improvement of around 3.5% over the existing methods. The ablation analysis shows that all components represent meaningful contributions to the overall model performance, with cross-modal attention being responsible for the largest marginal contribution.
subject: Articles
  topic: section
subject: Fake News Detection
  topic: keywords
subject: Graph Attention Networks
  topic: keywords
subject: BERT
  topic: keywords
subject: Heterogeneous Graphs
  topic: keywords
subject: Multi-Modal Learning
  topic: keywords
subject: Misinformation
  topic: keywords
subject: Social Network Analysis
  topic: keywords
subject: Natural Language Processing
  topic: keywords
creator: Akash Garg
creator: Sachin Pachauri
rights: Copyright (c)
date: 2026-07-29
publication date: 2026-07-29
starting page: 243
ending page: 255
doi: 10.36548/jaicn.2026.3.006

AI-Driven Early Detection and Classification of Skin Diseases Using Deep Learning and Mobile Edge Computing
type: item
description: Skin disorders are experienced by millions of people across the globe, thus, calling for timely and accurate diagnosis in order to ensure proper treatment and good prognosis of the disease. Nevertheless, lack of access to dermatologists in some parts of the world, especially in rural areas and poor regions, often hinders proper and timely diagnosis of the condition. This work proposes an AI-based framework for automatic detection of skin disorders via deep learning and edge computing technology. Grad-CAM powered explainable artificial intelligence makes the model more transparent through region of interest identification in clinical images, whereas the lightweight web application makes predictions along with disease prediction confidence and visualization of diagnosis. Evaluation on HAM10000 and ISIC datasets attained 94.8% accuracy, 93.6% precision, 94.1% recall, 93.8% F1-score, and 95.4% mAP. The proposed solution presents an effective and scalable solution for AI-assisted dermatological diagnosis.
subject: Articles
  topic: section
subject: Skin Disease Classification
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: EfficientNet
  topic: keywords
subject: MobileNet
  topic: keywords
subject: Mobile Edge Computing
  topic: keywords
subject: Explainable Artificial Intelligence
  topic: keywords
subject: TensorFlow Lite
  topic: keywords
subject: Dermatological Image Analysis
  topic: keywords
subject: Telemedicine
  topic: keywords
creator: Manikandaraja G.
creator: Hemanth R S.
creator: Nihal Bin Anwar
rights: Copyright (c)
date: 2026-07-27
publication date: 2026-07-27
starting page: 224
ending page: 242
doi: 10.36548/jaicn.2026.3.005

YOLOv8-Assisted Knee Osteoarthritis Severity Assessment from X-ray Images
type: item
description: Knee osteoarthritis is a progressive degenerative joint disorder characterized by cartilage deterioration, leading to pain, stiffness, reduced mobility, and functional impairment. Early detection and accurate grading of disease severity play a crucial role in effective clinical decision-making and prevention of further joint damage. Most existing deep learning approaches focus mainly on image-level classification using conventional CNN architectures and do not provide precise localization of the affected knee region, which is essential for clinical interpretation. In addition, limited research has explored unified object detection frameworks for simultaneous localization and severity grading of knee osteoarthritis. To address these limitations, this study proposes a YOLOv8-based deep learning framework for automated knee osteoarthritis detection and severity classification using radiographic X-ray images collected from a publicly available Kaggle dataset. The dataset is carefully annotated using Label Studio by marking the knee joint region and assigning five severity grades: Healthy, Doubtful, Minimal, Moderate, and Severe. The proposed model is trained using GPU acceleration to enhance computational efficiency and improve detection performance. Experimental evaluation is performed using standard metrics and the proposed system achieves a mAP@0.5 of 85.2%, demonstrating strong capability in both accurate localization and classification of knee osteoarthritis severity. It can be deployed in clinical decision support systems, radiology departments, and telemedicine platforms to enable rapid knee X-ray screening, providing radiologists, orthopaedic specialists, and physicians with an automated second opinion that reduces diagnostic time and human variability.
subject: Articles
  topic: section
subject: Knee Osteoarthritis
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: X-ray Image Analysis
  topic: keywords
subject: Medical Image Classification
  topic: keywords
creator: Dhanya K.R.
rights: Copyright (c)
date: 2026-07-21
publication date: 2026-07-21
starting page: 210
ending page: 223
doi: 10.36548/jaicn.2026.3.004

AUREL:AI-Driven Blockchain Platform for Authentic Product Verification and Trusted Recommendations
type: item
description: The widespread use of fake cosmetic and personal care products has become a threat to consumer safety and product authenticity. Traditional verification methods such as QR codes, holograms, and centralized database are vulnerable to duplication and manipulation. To overcome these issues, this paper proposes AUREL, an AI-enabled blockchain platform for authentic product verification and recommendations. The proposed AUREL framework consists of product registration with blockchain, AI-based product visual verification, behavioral scan analytics, and recommendation service. In the developed application framework, first, manufacturers register the product information with a hash code generated by cryptographic technique. Second, consumers scan QR codes and take pictures of packages of the products through a mobile application for verification. Third, the system validates the blockchain and analyses the authenticity of the product to identify the unusual verification activities. A decision fusion algorithm is used to combine the outputs of each verification layer to classify products as genuine, suspicious, or fake. Finally, when fake products are found, the recommendation system suggests verified alternative products to the consumers. The proposed solution has been experimentally evaluated with a product verification success rate of 98.4%.
subject: Articles
  topic: section
subject: Product Authenticity Verification
  topic: keywords
subject: Fake Detection
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Blockchain
  topic: keywords
subject: Behavioral Scan Analytics
  topic: keywords
subject: Recommendation System
  topic: keywords
creator: Sugantha Lakshmi R.
creator: Abirami K.
creator: Harini P.
creator: Jeya Keerthana M.
rights: Copyright (c)
date: 2026-07-10
publication date: 2026-07-10
starting page: 193
ending page: 209
doi: 10.36548/jaicn.2026.3.003

Non-Invasive Disease Prediction Using Tongue Images
type: item
description: Disease diagnosis at an early stage is extremely important for enhancing the treatment efficacy and decreasing costs of medical treatment. The tongue appearance is one of the non-invasive ways of detecting diseases as its appearance reflects some health disorders. This study proposes a deep learning framework for non-invasive disease prediction based on tongue images. The system uses the YOLOv8 model for detecting tongue regions, as well as the CNN model for feature extraction and disease classification. Tongue images from TMC-Tongue dataset are preprocessed by performing image resizing, normalization, and noise filtering to improve their quality and model performance. YOLOv8 detects the tongue region, whereas CNN extracts features related to different tongue diseases. The developed framework provides users with an application allowing uploading images, analyzing them in real time, and generating reports. Experimental evaluation achieved a classification accuracy of 84.2%, demonstrating the effectiveness of the proposed framework.
subject: Articles
  topic: section
subject: Tongue Image Analysis
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Convolutional Neural Network (CNN)
  topic: keywords
subject: Disease Prediction
  topic: keywords
subject: Medical Image Processing
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Non-Invasive Diagnosis
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
creator: Suriya L.
creator: Suganthi R.
creator: Meraklin B.
creator: Muthu Kumari C.
rights: Copyright (c)
date: 2026-06-25
publication date: 2026-06-25
starting page: 180
ending page: 192
doi: 10.36548/jaicn.2026.3.002

Natural Language Driven Leave Management Using Lang Chain Agents and Few-Shot Learning
type: item
description: The traditional approaches for managing leaves involve form-driven procedures and processes, resulting in high levels of admin workload, delays, and low levels of flexibility for users. In this paper, we introduce a novel Natural Language Driven Leave Management System where employees are able to ask their requests about leaves through natural language. The introduced framework uses the capabilities of LangChain agents, Gemini large language models, few-shot learning, and MongoDB to translate natural language into NoSQL operations. A mechanism of validation and fallback is used to guarantee schema, data consistency, and query reliability. The framework architecture includes four layers that enable to implement automation of leave processing and notification management. An experiment was performed for testing the framework by applying a number of representative queries in leave management. Configurations include Zero-Shot, Few-Shot, and Few-Shot with Validation cases. According to the results obtained, the developed framework achieved Intent Accuracy of 91%, Field Extraction Accuracy of 88%, NoSQL Query Accuracy of 87% and Fallback Success Rate of 93%.
subject: Articles
  topic: section
subject: Leave Management
  topic: keywords
subject: Lang Chain
  topic: keywords
subject: Few-Shot Learning
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: NoSQL
  topic: keywords
subject: Fast API
  topic: keywords
subject: Gemini LLM
  topic: keywords
creator: Vijayalakshmi S.
creator: Ashwin Kumar R S.
creator: Rakshana V.
creator: Srivatsan R.
rights: Copyright (c)
date: 2026-06-17
publication date: 2026-06-17
starting page: 166
ending page: 179
doi: 10.36548/jaicn.2026.3.001

Hierarchical Sparse Vision Transformers for Real-Time Drone-Based Object Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: Scalable Attention
  topic: keywords
subject: Vision Transformer
  topic: keywords
subject: Sparse Self-Attention
  topic: keywords
subject: Aerial Object Detection
  topic: keywords
subject: Multi-Scale Fusion
  topic: keywords
subject: Real-Time Detection
  topic: keywords
creator: Vivekanandam B.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-05-26
publication date: 2026-05-26
starting page: 147
ending page: 165
doi: 10.36548/jaicn.2026.2.006

AI Based Early Childhood Glaucoma Risk Screening System Using Eye Image Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Glaucoma
  topic: keywords
subject: Eye Image Analysis
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Image Processing
  topic: keywords
subject: Cup-to-Disc Ratio
  topic: keywords
creator: Muthulakshmi I.
creator: Parameshwari V.
creator: Mubitha J.
creator: Vigneshwari K.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-05-13
publication date: 2026-05-13
starting page: 108
ending page: 120
doi: 10.36548/jaicn.2026.2.003

Smart Tourist Safety and Emergency Response System Using Rule-Based Risk Detection
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence (AI)
  topic: keywords
subject: Tourist Safety
  topic: keywords
subject: Emergency Response
  topic: keywords
subject: Risk Intelligence
  topic: keywords
subject: GPS Tracking
  topic: keywords
subject: Smart City Safety
  topic: keywords
creator: Sowmiya M.N.
creator: Sureshkumar R.
creator: Mukesh E.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-05-15
publication date: 2026-05-15
starting page: 135
ending page: 146
doi: 10.36548/jaicn.2026.2.005

AI-Driven Climate Based Control and Energy Analytics for Smart Buildings
type: item
description: 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%.
subject: Articles
  topic: section
subject: Smart Buildings
  topic: keywords
subject: Internet of Things (IoT)
  topic: keywords
subject: Energy Management Systems
  topic: keywords
subject: Energy Analytics
  topic: keywords
subject: Long Short-Term Memory (LSTM)
  topic: keywords
subject: Message Queuing Telemetry Transport (MQTT)
  topic: keywords
subject: Energy Efficiency Optimization
  topic: keywords
creator: Sumalatha M.
creator: Devi P.S.
creator: Gopikrishna J.
creator: Ajay Kumar Reddy P.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-05-13
publication date: 2026-05-13
starting page: 121
ending page: 134
doi: 10.36548/jaicn.2026.2.004

Blinkora: A Smart Digital Wellbeing App for Eye Health and Screen-Time Management
type: item
description: 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.
subject: Articles
  topic: section
subject: Screen Time Monitoring
  topic: keywords
subject: Eye Health Analysis
  topic: keywords
subject: Android Application
  topic: keywords
subject: Face Detection
  topic: keywords
subject: Usage Analytics
  topic: keywords
subject: Digital Well-Being
  topic: keywords
subject: Smart Alerts
  topic: keywords
subject: Mobile Health
  topic: keywords
creator: Sathya Priya J.
creator: Nirisha K.
creator: Shalini N.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-04-22
publication date: 2026-04-22
starting page: 97
ending page: 107
doi: 10.36548/jaicn.2026.2.002

Glacier Growth Monitoring Using YOLOv8 and Feature-Based Classification
type: item
description: 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.
subject: Articles
  topic: section
subject: Glacier Growth Monitoring
  topic: keywords
subject: Satellite Imagery
  topic: keywords
subject: Deep Learning Segmentation
  topic: keywords
subject: Ensemble Machine Learning
  topic: keywords
subject: YOLOv8
  topic: keywords
creator: Pratyush Jha
creator: Arun A.
creator: Srijan Srivastava
creator: Tanishq Gandhi
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-04-07
publication date: 2026-04-07
starting page: 84
ending page: 96
doi: 10.36548/jaicn.2026.2.001

LiBile: Mobile-Based Library Management System
type: item
description: 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.
subject: Articles
  topic: section
subject: Mobile Library Management
  topic: keywords
subject: Digital Library System
  topic: keywords
subject: Real-Time Database
  topic: keywords
subject: User-Friendly Interface
  topic: keywords
subject: Library Automation
  topic: keywords
creator: Mowsikraj M.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-03-23
publication date: 2026-03-23
starting page: 74
ending page: 83
doi: 10.36548/jaicn.2026.1.004

A Hybrid Deep Learning Framework for Air Quality Index Forecasting
type: item
description: 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.
subject: Articles
  topic: section
subject: Air Quality Index
  topic: keywords
subject: Feature Selection
  topic: keywords
subject: Recurrent Neural Networks
  topic: keywords
subject: Quantum-Inspired Genetic Algorithm
  topic: keywords
subject: XGBoost
  topic: keywords
subject: PCA
  topic: keywords
creator: Saranya K G.
creator: Akalya A.
creator: Harini G.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-03-23
publication date: 2026-03-23
starting page: 42
ending page: 73
doi: 10.36548/jaicn.2026.1.003

An Intelligent Shopping Cart Chatbot Using Amazon Lex
type: item
description: 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.
subject: Articles
  topic: section
subject: Chatbot
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Amazon Web Services (AWS)
  topic: keywords
subject: Amazon Lex
  topic: keywords
subject: AI
  topic: keywords
subject: Shopping Cart Chatbot
  topic: keywords
creator: Thilagavathi Narayanamoorthi
creator: Sridevi Sridhar
creator: Devakunchari Ramalingam
creator: Leema Nelson
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-03-18
publication date: 2026-03-18
starting page: 19
ending page: 41
doi: 10.36548/jaicn.2026.1.002

Quantum Machine Learning Approaches for High-Dimensional Optimization Problems: A Survey
type: item
description: 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 proble…
subject: Articles
  topic: section
subject: Quantum Machine Learning
  topic: keywords
subject: Quantum Computing
  topic: keywords
subject: Quantum Neural Networks
  topic: keywords
subject: Variational Quantum Circuits
  topic: keywords
subject: Quantum Data Encoding
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
creator: Joy Iong-Zong Chen
creator: Wen-Bing Wu
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-03-17
publication date: 2026-03-17
starting page: 1
ending page: 18
doi: 10.36548/jaicn.2026.1.001

Ethics-Aware Personalization: A Dual-Objective AI Framework for Engagement Optimization
type: item
description: 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.
subject: Articles
  topic: section
subject: Ethical Personalization
  topic: keywords
subject: AI
  topic: keywords
subject: Engagement Optimization
  topic: keywords
subject: Privacy
  topic: keywords
subject: Fairness
  topic: keywords
subject: Transparency
  topic: keywords
creator: Wafa Hamid Abdelrahman Mohamed Ahmed
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-01-09
publication date: 2026-01-09
starting page: 413
ending page: 427
doi: 10.36548/jaicn.2025.4.006

An In-Browser Proctoring System Using YOLO-Based Object Detection and Gaze Analysis
type: item
description: 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.
subject: Articles
  topic: section
subject: Online Proctoring
  topic: keywords
subject: Real-Time Monitoring
  topic: keywords
subject: YOLOv8
  topic: keywords
subject: Gaze Estimation
  topic: keywords
subject: Computer Vision
  topic: keywords
subject: Academic Integrity
  topic: keywords
subject: Browser-Based System
  topic: keywords
subject: Cheating Detection
  topic: keywords
creator: Ayush Sharma Kaundinya
creator: Pramish Adhikari
creator: Mohan Bikram KC
creator: Pratikshya Shrestha
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2026-01-08
publication date: 2026-01-08
starting page: 388
ending page: 412
doi: 10.36548/jaicn.2025.4.005

Comparative Analysis of Sports-Inspired Optimization Algorithms in Neural Network Training: A Benchmark Study
type: item
description: 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.
subject: Articles
  topic: section
subject: Sport-Inspired Optimization
  topic: keywords
subject: Hyperparameter Optimization
  topic: keywords
subject: Deep Learning Optimization
  topic: keywords
subject: Population-based
  topic: keywords
subject: Algorithms
  topic: keywords
creator: Eyad J. R. Elfarra
creator: Mohammed A. Alhanjouri
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-12-05
publication date: 2025-12-05
starting page: 362
ending page: 387
doi: 10.36548/jaicn.2025.4.004

Integrating Automatic Speech Recognition and Emotion Detection: A Conformer-XGBoost Framework for Human-Centered Speech Systems
type: item
description: 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.
subject: Articles
  topic: section
subject: Automatic Speech Recognition
  topic: keywords
subject: Speech Emotion Recognition
  topic: keywords
subject: Conformer Architecture
  topic: keywords
subject: XGBoost Classifier
  topic: keywords
subject: Human Computer Interaction
  topic: keywords
subject: Multimodal Speech Processing
  topic: keywords
creator: Mohan Bikram K C.
creator: Smita Adhikari
creator: Tara Bahadur Thapa
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-12-02
publication date: 2025-12-02
starting page: 343
ending page: 361
doi: 10.36548/jaicn.2025.4.003

Adaptive Metaheuristic Optimization of AraBERT for Arabic NER: A Comparative Study of PSO and TLBO
type: item
description: 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.
subject: Articles
  topic: section
subject: Artificial Intelligence
  topic: keywords
subject: Optimization
  topic: keywords
subject: Metaheuristic
  topic: keywords
subject: Particle Swarm Optimization
  topic: keywords
subject: Teaching–Learning-Based Optimization
  topic: keywords
subject: Natural Language Processing
  topic: keywords
subject: Named Entity Recognition
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Deep Neural Network
  topic: keywords
creator: Belal K. ELFarra
creator: Mohammed Alhanjouri.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-11-12
publication date: 2025-11-12
starting page: 320
ending page: 342
doi: 10.36548/jaicn.2025.4.002

Generative Adversarial Networks (GANs): A Comprehensive Review of Architectures, Training Challenges, and Advancements
type: item
description: 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.
subject: Articles
  topic: section
subject: GANs- Generative Adversarial Networks
  topic: keywords
subject: Generative Models
  topic: keywords
subject: GAN Architectures
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: Adversarial Training
  topic: keywords
creator: Balqis Nabilah Binti Ramanizan
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-11-07
publication date: 2025-11-07
starting page: 304
ending page: 319
doi: 10.36548/jaicn.2025.4.001

Hybrid Bi-LSTM Framework for Aspect-Based Sentiment Analysis in E-Commerce Reviews
type: item
description: 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.
subject: Articles
  topic: section
subject: Aspect Based Sentiment Classification[ABSC]
  topic: keywords
subject: Bi-LSTM
  topic: keywords
subject: DeBERT
  topic: keywords
subject: Weak Supervision
  topic: keywords
subject: Snorkel
  topic: keywords
subject: Sentiment Analysis
  topic: keywords
subject: E-Commerce Reviews
  topic: keywords
creator: Manimalar S.
creator: Neavil Porus A.
creator: Bharathwaj
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-09-26
publication date: 2025-09-26
starting page: 284
ending page: 303
doi: 10.36548/jaicn.2025.3.005

Comparative Analysis of Supervised Learning Algorithms for Clinical Diagnosis Heart Attack Prediction
type: item
description: 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.
subject: Articles
  topic: section
subject: Heart Attack Prediction
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Healthcare Analytics
  topic: keywords
subject: Feature Engineering
  topic: keywords
subject: Medical Diagnosis
  topic: keywords
subject: Random Forest
  topic: keywords
creator: Syed Rafiammal S.
creator: Padma Usha M.
creator: Turika N.
creator: Shaik Sharukh
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-09-23
publication date: 2025-09-23
starting page: 271
ending page: 283
doi: 10.36548/jaicn.2025.3.004

Efficient Ensemble of Pre-Trained CNN Models for Improving Classification of Maize Diseases
type: item
description: 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.
subject: Articles
  topic: section
subject: Maize Disease
  topic: keywords
subject: Convolutional Neural Networks (CNNs)
  topic: keywords
subject: Transfer Learning
  topic: keywords
subject: Ensemble Model
  topic: keywords
subject: Disease Classification
  topic: keywords
creator: Pankaj Raj Dawadi
creator: Sumanta Silwal
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-09-23
publication date: 2025-09-23
starting page: 258
ending page: 270
doi: 10.36548/jaicn.2025.3.003

Enhanced Sales Forecasting in Corporate Cafeterias: An LSTM Approach
type: item
description: 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.
subject: Articles
  topic: section
subject: Long Short-Term Memory (LSTM)
  topic: keywords
subject: k-Nearest Neighbors (k-NN)
  topic: keywords
subject: Machine Learning
  topic: keywords
subject: Sales Forecasting
  topic: keywords
subject: Enhancement
  topic: keywords
subject: Cafeteria
  topic: keywords
creator: Ruban Gladwin M.
creator: Jenifer P.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-08-26
publication date: 2025-08-26
starting page: 244
ending page: 257
doi: 10.36548/jaicn.2025.3.002

Pulmonary Nodule Segmentation
type: item
description: 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.
subject: Articles
  topic: section
subject: Pulmonary Nodule Segmentation
  topic: keywords
subject: Deep Learning
  topic: keywords
subject: U-Net
  topic: keywords
subject: Resnet-152
  topic: keywords
subject: Computed Tomography (CT) Images
  topic: keywords
subject: Medical Image Processing
  topic: keywords
subject: Convolutional Neural Networks (CNNs)
  topic: keywords
creator: Shenbagarajan A.
creator: Abinaya M.
creator: Bhuvanika S.
creator: Rajakumari S.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-08-01
publication date: 2025-08-01
starting page: 232
ending page: 243
doi: 10.36548/jaicn.2025.3.001

Role of Artificial Intelligence in Enhancing School Digital Twins
type: item
description: 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.
subject: Articles
  topic: section
subject: School Digital Twin
  topic: keywords
subject: Artificial Intelligence
  topic: keywords
subject: IOT sensors
  topic: keywords
subject: Blockchain
  topic: keywords
creator: Nalayini C M.
creator: Shailesh Arunkumar
creator: Madesh S.
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-07-15
publication date: 2025-07-15
starting page: 209
ending page: 231
doi: 10.36548/jaicn.2025.2.008

Intelligent Neuroimage-based Stroke Diagnosis Using Deep Learning and Dual Web Interface for Clinical Use
type: item
description: 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.
subject: Articles
  topic: section
subject: Stroke Detection
  topic: keywords
subject: Medical Imaging
  topic: keywords
subject: MRI
  topic: keywords
subject: and Deep Learning
  topic: keywords
creator: Vidya D. Argade
creator: Diksha Gautam
creator: Diksha Singh
creator: Raman Vishwakarma
rights: Copyright (c) 2026 Journal of Artificial Intelligence and Capsule Networks
date: 2025-07-15
publication date: 2025-07-15
starting page: 191
ending page: 208
doi: 10.36548/jaicn.2025.2.007