DMPedia Lecture Notes in Computer Science & Engineering
DMP-LNCSE (DMPedia Lecture Notes in Computer Science and Engineering) is a peer-reviewed conference proceedings series published by Digital Manuscriptpedia. The series focuses on high-quality research presented at national and international conferences in computer science, information technology, electronics, electrical engineering, and related interdisciplinary fields. Each volume undergoes a rigorous double-blind peer review process, adhering to the highest standards of publication ethics as defined by the Committee on Publication Ethics (COPE). Each volume includes original papers that undergo peer review, plagiarism screening, and editorial quality checks prior to publication. DMP-LNCSE provides a reliable platform for publishing cutting-edge work in areas such as artificial intelligence, machine learning, cybersecurity, data science, networks, IoT, software engineering, and emerging technologies. The series aims to support conference organizers by offering a professional, standards-based publication outlet with ISBN assignment, DOI allocation (where required), online hosting, and indexing support. All published volumes are made available through Digital Manuscriptpedia’s digital library and are accessible for citation, academic reference, and scholarly use.
Cybersecurity threats are increasing rapidly as organizations rely more on digital infrastructure and online services. Security Operations Centres (SOCs) are responsible for monitoring and responding to these threats, but many still depend on manual investigation and static rule-based systems. These traditional approaches struggle to handle the large volume of alerts generated by modern networks. As a result, security analysts often face issues such as alert fatigue, delayed responses, and limited contextual information when analysing incidents, which can reduce the efficiency of security operations and increase the chances of missing critical threats. To address these challenges, this paper proposes an AI-Powered SOC Assistant that helps analysts detect and analyse potential security threats more effectively. The system combines machine learning techniques, natural language processing, and threat intelligence sources to analyse real-time security data collected from SIEM logs, OSINT sources, and external threat feeds. By correlating and analysing this information, the system can identify suspicious patterns and highlight possible attack indicators. The proposed solution also includes a risk-based alert-scoring mechanism to prioritise incidents by severity. An interactive dashboard and conversational interface allow analysts to explore alerts with natural-language queries and receive clear explanations and recommended response actions, improving investigation efficiency and overall SOC performance.
Security Operations Center (SOC), Cyber Threat Intelligence (CTI), Machine Learning, Anomaly Detection, Isolation Forest, Generative AI, Large Language Models (LLMs), Explainable AI (XAI), SIEM, Threat Hunting, Log Analysis, Risk-Based Alert Scoring.
Copyright (c) 2026 DMPedia Lecture Notes in Computer Science & Engineering https://creativecommons.org/licenses/by/4.0
Medicare.AI: Design, Implementation, and Safety Evaluation of an Intelligent Healthcare Management Platform
Healthcare software prototypes usually have conversational help, scheduling, role-specific dashboards, and real-time data. Their evaluations focus more on feature lists than on failure modes. This paper reports a reproducible evaluation of safety and access control for Medicare. React, Node.js/Express, MongoDB, and socket.io. AI.IO educational health care platform. We froze a 20-prompt safety benchmark covering emergencies, first aid, medication use, mental health and general questions. We defined eight source-level security checks, including JSON Web Token middleware, role guards, appointment ownership, patient-only creation, password hashing, secret handling and Socket.IO room access. The baseline deterministic chatbot passed 7 out of 20 prompts (35%) and 5 out of 8 security checks. The dynamic loopback test also showed that an unauthenticated client could join the doctor telemetry room. Remediation introduced specific intent-matching, explicit high-risk routes, authenticated socket.io middleware, room-level role restrictions, and appointment ownership enforcement. Our final benchmark passed all 20 prompts and all eight source-level checks. The study does not claim clinical efficacy: no patient records, clinician participants, diagnostic model, or clinical outcomes were assessed. The Atlas network allowlist blocked database-backed exploitation and post-remediation dynamic socket tests, which were reported as unexecuted. Thus, the contribution is a transparent case study of how an ambitious student prototype can be transformed into a more testable, safety-aware system.
Healthcare chatbot, Web application security, Access control, Socket IO, Safety Assessment, reproducibility, Failure analysis
A Comprehensive WebRTC-Based Platform for Remote Assessment and Synchronous Technical Collaboration
With the rapidly growing technology, communication and collaboration have become much simpler and more efficient. Web Real-Time Communication (WEBRTC) enables secure, peer-to-peer (P2P) communication between two IP addresses. Common vulnerabilities include exposure of private IP addresses, which may lead to Man-in-the-Middle (MitM) attacks, and a lack of end-to-end encryption (E2EE), which makes data visible or vulnerable to interception, resulting in reduced security and privacy. Most video conferencing apps use P2P mesh, which helps transmit video directly between IP addresses. It also has the drawback of instability when the stream reaches more than 4 users. Selective Forwarding Unit (SFU) architectures are widely used for forwarding individual streams of data; however, they lack decoding on the client side, which leads to high CPU consumption. By addressing these identified gaps, our Project aims to contribute to the development of NextGen video conferencing applications that not only allow authenticated participants to attend video conferences assigned by the host or admin, but also enhance security and privacy.
Web Real Time Communication (WebRTC), peer-to-peer (P2P), Man-in-the-Middle (MitM) attacks, Selective Forwarding Unit (SFU).
Multimodal Brain Tumour Classification and Segmentation Using a Dual-Attention Swin-UNet with Quantum-Inspired Optimisation for MRI-Based Diagnostics
One of the most significant and challenging challenges in medical image processing is the use of magnetic resonance imaging (MRI) to diagnose brain cancers. Significant tumour heterogeneity, irregular morphologies, blurred boundaries, and differences between imaging modalities are the root causes of the problem. Early and precise disease detection is critical for better treatment planning and patient outcomes. Automated brain tumour analysis has greatly improved because of deep learning, particularly convolutional neural networks (CNNs). However, with high-resolution medical images, traditional CNN-based models frequently struggle to identify long-range contextual linkages. This limitation may make it more difficult to obtain precise segmentation and reduce the overall reliability of the diagnosis. Transformer-based designs have recently shown great potential in visualising global relationships. However, because they are hard to train, require a lot of memory, and are rather complex to utilise, their practical application in medical imaging is challenging. We provide a Multimodal Brain Tumour Classification and Segmentation framework based on a Dual-Attention Swin-UNet architecture, enhanced with a Quantum-Inspired Optimisation (QIO) technique, to overcome these limitations. The proposed system successfully integrates fine-grained spatial localisation and global contextual awareness by combining a U-Net-style decoder with an encoder based on Swin Transformer. Dual attention mechanisms spatial and channel attention are added to enhance feature representation and facilitate visualisation of tumour edges, thereby further accelerating feature learning. These attention modules filter away irrelevant background noise, allowing the network to concentrate on clinically significant areas. Additionally, we introduce a quantum-inspired optimisation+ technique to reduce training oscillations and enhance convergence stability, which are frequently observed in transformer-based medical imaging models. A pre-processed version of the BraTS 2020 dataset and additional brain MRI images recorded in HDF5 format are used to train and evaluate the framework. This enables more effective data handling and large-scale training. Due to computational power constraints, GPU acceleration is used for full-scale model training. A real-time, user-friendly web-based diagnostic interface demonstrates a lightweight and deployable inference pipeline. The experimental outcomes demonstrate the strength and clinical practicability of the proposed method, which offers superior tumour segmentation accuracy and reliable classification. The possible development of scalable and intelligent clinical decision support systems for brain tumour diagnosis through the integration of transformer-based architectures, the geography of attention processes, and quantum-inspired optimisation strategies is identified in the study.
Brain Tumour, MRI, Swin-UNet, Dual Attention, Quantum-Inspired Optimisation, Medical Image Segmentation, Deep Learning.
Medication overdose continues to be a major global health crisis, leading to avoidable emergencies, bad drug reactions, and more hospital visits. The rising complexity of modern prescriptions, especially when patients take multiple drugs, makes it easier to misread doses, miss dangerous interactions, and cause accidental harm. This research introduces a smart automated tool designed to predict overdose risks by analysing prescriptions. Our framework combines Optical Character Recognition (OCR) with machine learning to improve prescription reading accuracy and keep patients safer. The process starts by using OCR to pull text from images of handwritten or printed prescriptions. Key details such as drug names, strengths, timing, patient age, and history are organised in a digital format. Then, machine learning models such as Random Forests, Decision Trees, and Neural Networks analyse the data to identify risks and calculate safety scores. Tests show that the OCR reaches 92% accuracy in reading text, while the prediction model hits 89% accuracy, with 87% precision and 91% recall for high-risk flags. This method greatly reduces human reading errors and makes dosage checks much clearer. Future goals include linking the tool to Electronic Health Records (EHRs), adding support for multiple languages, expanding the drug interaction database, and developing a mobile app for instant alerts. This system shows great promise as a helpful clinical support tool, promoting safer medication habits and lowering the number of overdose cases worldwide.
Medicine Overdose Prediction, Optical Character Recognition (OCR), Machine Learning, Drug Interaction Analysis, Healthcare Automation, Clinical Decision Support System
Crop Yield Prediction Using Remote Sensing Space Convolutional Attention Machine Learning with Potter Optimisation Algorithm for Independent Variable Selection
Accurate crop yield prediction is crucial for food security planning, resource allocation, and agricultural policy formulation. This paper proposes a novel hybrid deep learning framework, the Space Convolutional Attention Machine Learning (SCAML) model, that integrates multi-source remote sensing imagery with a biologically-inspired Potter Optimisation Algorithm (POA) for principled independent variable selection. The proposed model combines a dual-branch spatial-spectral Convolutional Neural Network (CNN) for local feature extraction with a multi-head self-attention transformer for temporal dependency modelling, culminating in a Space Convolutional Attention Module (SCAM) that fuses both representations. The Potter Optimisation Algorithm, inspired by the nest-building behaviour of potter wasps (Eumenes spp.), performs wrapper-based feature selection to identify the most informative spectral indices and ancillary variables, significantly reducing dimensionality while preserving predictive power. Experiments conducted on multi-season rice, wheat, and maize datasets across five agroclimatic zones demonstrate that SCAML-POA achieves a Root Mean Square Error (RMSE) of 0.312 t/ha, a Mean Absolute Percentage Error (MAPE) of 3.47%, and an R² of 0.964, outperforming baseline CNN, LSTM, and Random Forest regressors. The results affirm that the synergy between optimised variable selection and spatiotemporal attention learning can substantially improve crop yield forecasting accuracy.
Crop Yield Prediction, Remote Sensing, Space Convolutional Attention, Potter Optimisation Algorithm, Feature Selection, Deep Learning, NDVI, Transformer, Precision Agriculture
Personalised Adaptive E-Learning System with Competency Graph and Machine Learning
Adaptive e-learning systems are designed to tailor learning materials based on a learner's performance and mastery progression. However, existing systems use static unlocking mechanisms based on rules and do not provide a structured representation of topic dependencies. In this paper, we propose a personalised adaptive e-learning system that incorporates a competency graph with machine learning-based mastery prediction to dynamically shift quiz difficulty and recommend the best learning paths. The system represents domain knowledge as a directed graph of prerequisite-order-based competencies and monitors learner interaction data, such as quiz scores, attempts, and time spent. A model is trained to predict the correct mastery and difficulty, which it can dynamically adjust. We also compare our approaches to existing adaptive models and demonstrate improved structural personalisation and mastery-driven progression compared to traditional rule-based systems. The framework presented features such as scalability, enforced structured learning, and improved learner engagement compared to traditional e-learning systems.
Adaptive e-learning, competency graph, mastery prediction, personalised learning, machine learning, learning path recommendation
Academic Records Validator Using Blockchain Technology
Fraud in academic certificates has become a significant issue in employment, admissions to institutions, and institutional certification. The traditional methods of manual checks are tedious, inefficient and subject to fraud. To provide credible certificate validation, the present study suggests a secure and automatic Academic Records Validator, which can be viewed as a hybrid of blockchain technology, optical character recognition (OCR) and artificial intelligence (AI)-based forgery detector. The system retrieves structured information from 10th, intermediate, and degree certificates using OCR, including the candidate's name, roll number, certificate number, the name of the educational institution, year of graduation, and course information. The retrieved data should be standardised and converted to a single format to obtain a SHA-256 cryptographic hash. To ensure data privacy and guarantee immutability, integrity, and tamper-resistance, the hash value is stored only in the blockchain. To determine legitimacy, the uploaded certificate is OCR'd, compared against blockchains and recreated hashes, and the similarity is assessed by AI using cosine similarity algorithms. Experimental evaluation shows high forgery-detection capacity and secure verification with minimal processing time. The proposed system provides a secure, privacy-preserving, and tamper-resistant method for academic certificate verification in real-world scenarios.
Academic Certificate verification, Blockchain Technology, Zero-Shot learning, MobileNetV2, Visual similarity engine, Optical Character Recognition (OCR), SHA-256 hashing, Smart contract, Cosine similarity, Forgery detection, Decentralised trust, Cryptographic integrity.
NeuroNimbus: A Cloud-Powered AI System for Memory Reconstruction
NeuroNimbus is an intelligent cloud-assisted system designed to help individuals with cognitive impairments perform digital memory reconstruction. Memory disorders make it difficult for individuals to remember people and events; as a result, they are forced to rely on caregivers and manual memory aids. To address these issues, the proposed system uses dual interfaces for caregivers and patients. Users upload images with accompanying captions through a web portal. Natural Language Processing (NLP) is used to analyse the uploaded captions to extract contextual keywords. TF-IDF is used to represent features and perform cosine similarity analysis to ensure consistency of memories stored by detecting redundant memories. Memories are organised into person and event categories based on assigned labels. NeuroNimbus provides an organised digital memory support system by integrating semantic processing, redundancy control, and security mechanisms with role-based access control. Experimental validation demonstrated an overall functional accuracy of 90.2%, indicating stable and reliable system performance in structured memory reconstruction tasks.
Digital Memory Reconstruction, Natural Language Processing (NLP), TF-IDF, Cognitive Support Systems, Cloud Computing, Role-Based Access, Memory Organisation, Duplicate Detection, Healthcare Informatics
AI-Driven Polypharmacy Risk Prediction Using Graph Neural Networks
Polypharmacy, which is the usage of several drugs simultaneously, is becoming a common phenomenon, especially among elderly patients and those with long-term illnesses. Although it may be essential to the successful treatment, polypharmacy is a leading contributor to the occurrence of adverse drug reactions (ADRs), drug-drug interactions (DDIs), medication errors, and decreased therapeutic efficacy. Classical rule-based clinical decision support systems are primarily concerned with pairwise drug interactions and are based on predefined medical knowledge. But in treatment settings where a combination of drugs is available, these methods tend to fail to represent the complicated dynamics that arise when multiple drugs interact at the same time. In this project, we propose an artificial intelligence-based system to predict polypharmacy risk using Graph Neural Networks (GNNs). The model proposed is that each drug is a node in a graph, and drug-drug interactions and pharmacological relationships are edges between nodes. This multi-drug interaction is an effective way in which this graph-based representation enables the system to model the complex interaction patterns between drugs. The model is trained using deep learning algorithms on graph-structured biomedical data to predict important representations of drug interactions and the probability of an adverse reaction developing with medication combinations. The given system combines drug interaction datasets and leverages GNNs' ability to spread information across interconnected nodes to identify known and unseen interaction patterns. The ex-perimental assessments prove the given method to be more accurate in making predictions, generalizing, and being more robust in comparison with the classical machine learning and rule-based methods.
Polypharmacy, Graph Neural Networks, Drug-Drug Interactions, Adverse Drug Reactions, Artificial Intelligence in Healthcare, Clinical Decision Support Systems, Deep Learning, Biomedical Data Analysis
Explainable Vision Transformer for Diabetic Retinopathy Detection
Diabetic Retinopathy (DR) is a major cause of vision loss all over the world, and if diagnosed early, it can be prevented. In this paper, we propose an automated retinal image classifier using an Explainable Vision Transformer (ViT) model to classify Diabetic Retinopathy (DR) on the APTOS 2019 retinal image dataset. It leverages the transformer's attention mechanism to focus on clinically relevant regions in retinal fundus images, demonstrating high interpretability and strong predictive performance. Pre-processing techniques to enhance model generalisation include image resizing, image normalisation, and image augmentation. ViT models have been trained to achieve about 95 per cent accuracy on the validation set for predicting five levels of DR severity. Moreover, attention maps visually illustrate regions that significantly impact the model's decision-making process and, in addition, provide interpretable outputs for medical experts. The proposed strategy highlights the opportunities afforded by transformer-based architectures for medical imaging, with their high-performance enabling safety and reliability in DR screening.
Diabetic Retinopathy, Vision Transformer, Explainable AI, Medical Image Analysis, Deep Learning, Attention Mechanism, Fundus Imaging
A Mental Reasoning Driven Large Language Model for Smarter Human AI-Interaction
The Large Language Model (LLM) is a sophisticated artificial intelligence system designed to think and react in ways very close to how humans do in their minds. Its primary focus is on understanding users' intentions, breaking complex questions into simpler parts, and delivering accurate and insightful responses in a concise format. To improve its performance over time, it uses reasoning, memory, and learning skills. The Mental LLM is designed to deliver quick, accurate, and secure results across a range of fields, including data analysis, customer service, education, and health care support. It improves communication and problem-solving skills by mimicking human-like cognitive processes, including logical decision-making, memory, and step-by-step thinking. The increasing reliance on AI systems for routine tasks requires not only accurate but also "contextual" responses closer to human reasoning. To improve human-AI interaction, this project proposes a Mental Reasoning-Driven Large Language Model (LLM) that integrates cognitive reasoning into conventional language models. The system can interpret users' intentions, understand context, and deliver accurate responses through a combination of symbolic reasoning, neural networks, and knowledge integration. Applications in the fields of education, healthcare, customer support, and decision-making can benefit from the model's capability to adapt to user interactions, maintain multi-turn dialogues, and provide personalised interactions. This method bridges conventional AI responses with human-like comprehension. Large Language Models (LLMs) have changed the dynamics of human-AI interactions. The practicality of LLMs in real-world scenarios is limited by their inability to perform deep reasoning and learning. To create a model that mimics human thought processes, a Mental Reasoning-Driven Large Language Model is proposed.
Mental Large Language Model (LLM), Cognitive Reasoning, Natural Language Processing, Neural Networks, Contextual Understanding
From Robotic Process Automation to Agentic AI: A Systematic Review, Taxonomy, and Capability Assessment Framework for Intelligent Automation in Enterprise Accounting
Enterprise accounting is undergoing a structural transition from rule-based robotic process automation (RPA) toward agentic artificial intelligence: systems built on large language models that plan multi-step workflows, invoke tools, and adapt their execution autonomously. Research on this transition remains fragmented across information systems, accounting, and artificial intelligence venues, and no unified conceptual structure yet characterises the emerging class of agentic accounting systems. We conduct a PRISMA 2020-compliant systematic literature review, screening 2,387 retrieved records down to 60 included studies, and synthesise the technological trajectory of intelligent automation across four generations RPA, intelligent process automation (IPA), hyperautomation, and agentic AI and across core accounting subfunctions including procure-to-pay, order-to-cash, record-to-report, reconciliation, audit, and tax. Applying Nickerson et al.'s method, we derive a six-dimensional taxonomy spanning autonomy level, learning capability, process scope, human oversight, integration depth, and accounting subfunction. We extend the taxonomy into a capability assessment framework with five maturity levels (L0–L4) per dimension and ground it in four archetypal system classes, from the classic RPA bot to the autonomous multi-agent reconciliation system. The review identifies five critical research gaps: agent governance, hallucination mitigation, benchmark scarcity, multi-agent orchestration standards, and explainability and outlines a research agenda for trustworthy agentic accounting systems.
Robotic Process Automation, Agentic AI, Intelligent Agents, LLM-based Agents, Enterprise Accounting, Systematic Literature Review, Taxonomy, Capability Assessment Framework
Artificial Intelligence-Based Parkinson’s Disease Identification: A Comparative Analysis of Recurrent Neural Networks and Machine Learning Models
The neurodegenerative disorder weakens the brain's neurological, physiological, and behavioural systems; accurate identification is challenging in the early stages due to mild variances. Parkinson's disease is a major health concern. General signs of this condition include sluggish movements called ‘bradykinesia’. Parkinson's disease, a neurological disease, causes tremors, rigidity, imbalance, and loss of motor control. Most symptoms progress over time. This study suggests using supervised algorithms like Logistic regression, SVM, Decision tree, K-NN, Random Forest, Bagging, XGBoost Classifier, ANN /NN and LSTM (part of RNN) to generate the models. to diagnose subjective diseases. The proposed method involves feature selection using filter and wrapper methods, and classification processes. Data for this research were acquired from the UCI Machine Learning Repository. The dataset comprises biological voice recordings from 31 subjects, including 23 patients affected by Parkinson’s disease and 8 healthy individuals. Based on the results of the experiment, it can be stated that XGBoost has the highest accuracy with 97%, followed by KNN with an accuracy 95%, while RNN’s model LSTM achieved 97%, along with 50 epochs. Compared with prior research, the current study's findings indicate that the proposed approach yields equivalent or even superior outcomes.
Parkinson’s Disease, Machine Learning models, RNN, Data Science, Voice Measurements
ISO/IEC 42001 - Aligned Governance Evidence for High-Stakes AI Decision Systems: A Systematic Literature Review
Artificial intelligence (AI) is increasingly deployed in high-stakes sectors such as healthcare, finance, and autonomous systems, creating a need for governance approaches that address risks related to bias, opacity, accountability, and compliance. This systematic literature review (SLR) examines how the literature aligns AI governance for high-stakes decision systems with ISO/IEC 42001:2023, with particular attention to evidence generation, auditability, and the operationalization of compliance. Using a structured search and multi-stage screening process, 39 studies were selected for analysis from an initial pool of 2,918 records. The review finds growing convergence toward risk-based, lifecycle-oriented AI governance, supported by mechanisms such as traceability, continuous monitoring, and machine-readable evidence artefacts. The literature also indicates increasing interest in automation-enabled compliance, including blockchain-based logging and agentic support for governance processes. However, evidence remains fragmented, with limited large-scale validation, inconsistent evidence formats, and uneven treatment of regional regulatory requirements. The review concludes that ISO/IEC 42001-aligned governance for high-stakes AI requires evidence-centric, auditable, and context-sensitive oversight that combines automation with human judgment.
ISO/IEC 42001, AI Governance, Governance Evidence Layer (GEL), High-Stakes Decision Systems, Automated Compliance.
Explainable Multi-Horizon Deep Learning Framework for Household Energy Forecasting
. The growing need for intelligent energy management systems has also highlighted the importance of accurate, interpretable models for household energy consumption forecasting. This paper presents an explainable multi-horizon deep learning model for forecasting household electricity consumption at hourly, daily, and weekly time horizons. The proposed model combines optimised preprocessing, appliance-level energy decomposition, and sequence-to-sequence recurrent models such as Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks. To improve the interpretability of the proposed model, SHAP feature attribution and permutation importance analysis are used to explain the effect of historical consumption patterns and appliance-level features on the forecasting results. The proposed model forecasts multi-horizon energy consumption while providing appliance-level information. The experimental results show high forecasting accuracy across various time horizons. The proposed system improves the interpretability and trustworthiness of deep learning models in energy management systems
Household Energy Forecasting, Multi-Step Prediction, LSTM, GRU, SHAP, Time-Series Modelling, Explainable AI, Appliance-Level Energy Decomposition, Multi-Horizon Forecasting
AR-based Museum App for Personalised and Immersive Learning
Walking into museums feels familiar every time. Same old signs next to silent objects behind glass. Information stays unchanged, no matter who reads it - same words for kids and scholars alike. Yet something shifts when tech steps in. A mobile app built for Android changes how things appear. It uses Unity and Vuforia to layer augmented reality over real items. Hold your device toward an artefact, wait a second - it recognizes what you’re looking at. Instantly, custom 3D models pop up alongside tailored descriptions. Everything adjusts based on who you are. At first opening, pick just one identity: Child, Student, or Adult. A kid stares at the statue, watches it come alive with voices and motion. A learner spots old customs, how people paired off back then, timelines unfolding. An older person traces origins and reads between the lines of academic notes. One object. Three views. Staff do nothing. Rules drive what shows up, not hidden calculations, so outcomes stay steady, clear. Runs smoothly on everyday phones, needs no internet tug, no server talk. Built only as much as a common Android can carry, frame by frame, without slowing down. Fits inside current displays, like adding a page to an already-open book.
Smart Museum, Augmented Reality, Personalised Learning, 3D Visualisation, Image Recognition, Visitor Engagement, Adaptive Content Delivery, Cultural Heritage Education
A Machine Learning Approach for the Diagnosis of Diabetic Retinopathy
Diabetic retinopathy (DR) continues to be a major cause of preventable blindness globally, with early diagnosis a critical factor in the prevention of loss of vision. Diabetic Retinopathy (DR) is one of the main causes of avoidable blindness globally, and its diagnosis is most crucial for intervention. In this work, we propose an automated DR classification system using the Messidor dataset of fundus retinal images. Our approach primarily uses Convolutional Neural Networks (CNNs) for feature extraction and DR stage classification, given their success in medical imaging. To address the scarcity of annotated medical data, data augmentation methods such as random flips, rotations, and brightness changes are applied to improve the model's generalisation. In addition, transfer learning using pretrained CNN models (e.g., ResNet and EfficientNet from the timm library) is employed to accelerate convergence and improve accuracy. For thorough examination, the dataset is split into training, validation, and test subsets, and model efficiency is quantified through metrics such as accuracy, precision, recall, F1-score, and AUC.
Diabetic Retinopathy, Convolutional Neural Networks, Transfer Learning, Fundus Retinal Images, Pre-trained
Computer-Aided Multi-Class Classification of Diabetic Retinopathy Using Fundus Images
Diabetic Retinopathy is a progressive retinal disease and a major cause of preventable vision loss in those with diabetes. For immediate and appropriate clinical interventions in DR, recognising and appropriately understanding its severity are quite important. However, manual classification of diabetic retinal images and their severity assessment are time-consuming and highly dependent on specialists. Therefore, in this research, a multi-class classification system has been presented for automated classification of retinal images from people with diabetes and the identification of their severity levels. The proposed technique uses deep learning feature generation alongside supervised learning to identify and predict distinct visual features and varying degrees of DR. Traditional image pre-processing is performed to improve image quality and reduce light intensity variations. Moreover, a large number of data augmentation techniques are employed to improve the classifier's generalisation capacity
: Diabetic Retinopathy, Fundus Images, Deep Learning, Multi-Class Classification, Severity Detection, Data Augmentation.
Blockchain-Based Decentralised Threat Intelligence Validation System
This paper presents the design and implementation of a blockchain-based, decentralised threat intelligence validation system built on the Solana blockchain. The system addresses the fundamental challenge of enabling trustworthy cybersecurity intelligence sharing among organisations without relying on a central authority. By combining Solana’s high-throughput blockchain with a token-based economic incentive model using the DTNC SPL token, the system establishes a stake-based validation mechanism that ensures data integrity, discourages malicious behaviour, and rewards accurate contributions. A hybrid storage architecture retains cryptographic hashes on-chain while keeping full report data off-chain, significantly reducing operational costs while preserving verifiability. Smart contracts developed using Python Seahorse automate the entire validation and reward lifecycle. Upon validation, the consensus-verified data is passed to a downstream AI pipeline for contextual threat intelligence analysis. The results demonstrate that decentralised, economically incentivised threat intelligence sharing is both technically feasible and practically viable using current blockchain technology
Solana, blockchain, threat intelligence, smart contracts, SPL token, decentralised validation, cybersecurity, Proof of History
A GenAI-Driven Adaptive Cybersecurity Mesh for Real-Time Threat Detection in Intelligent Communication Systems
Intelligent communication systems integrating Internet of Things (IoT), cyber-physical infrastructures, edge computing, and next-generation communication protocols have significantly expanded the attack surface of modern digital ecosystems. Traditional intrusion detection systems (IDS) remain predominantly centralised, signature-based, and insufficiently adaptive to evolving multi-vector and zero-day threats. This paper proposes a GenAI-driven adaptive cybersecurity mesh architecture designed for real-time threat detection in distributed intelligent communication environments. The proposed framework integrates zero-trust security principles with a distributed mesh of edge security nodes coordinated through a policy orchestration layer. A generative AI-based adaptive threat modelling engine continuously synthesises contextual attack patterns and enhances anomaly detection across network, application, and behavioural layers. A formal cross-layer risk-scoring model fuses heterogeneous security signals to generate dynamic threat-confidence indices. The system is evaluated in a simulated intelligent communication environment comprising heterogeneous nodes, mixed legitimate traffic, and multiple attack scenarios, including DDoS, man-in-the-middle, and protocol-exploitation attacks. Experimental results demonstrate improved detection accuracy, reduced false positives, and lower response latency compared to baseline signature-based and centralised ML-based IDS models. The proposed architecture offers a scalable and adaptive security paradigm suitable for next-generation intelligent communication infrastructures.
Cybersecurity Mesh; Generative AI; Intelligent Communication Systems; Zero-Trust Architecture; Intrusion Detection; Cross-Layer Risk Scoring; Distributed Security.
AI-Based Online Learning Recommendation System for Personalised Education
The rapid expansion of online learning platforms has created unprecedented access to educational resources; however, it has also introduced significant challenges, including contentoverload,inappropriatecourseselection,andlowlearner engagement.Learnersfrequentlystruggletoidentifycoursesthat align with their current skill levels, learning objectives, and careeraspirations,resultingininefficientlearningpathsandhigh dropout rates. To address these challenges, this paper proposes an artificial intelligence-based online learning recommendation system that personalises learning experiences through intelligent,data-driventechniques.Theproposedsystemanalyses diverse learner data, including demographic information, academic background, learning behaviour, interaction history, and assessment performance. By applying machine learning anddata analysis techniques, the system constructs comprehensive learner profiles and dynamically identifies individual skill gaps. Based on these insights, personalised course recommendations are generated to enhance learner engagement, improve learning efficiency, and support continuous skill development.
Online learning, recommendation systems, artificial intelligence, personalised learning, machine learning
Adaptive Face Recognition and Emotion Analysis Framework (AFREAF): A Real-Time Multi-Modal Deep Learning System for Comprehensive Facial Attribute Detection
This paper presents the Adaptive Face Recognition and Emotion Analysis Framework (AFREAF), a comprehensive, real-time system for simultaneously detecting and analysing multiple facial attributes, including age, gender, and emotional states. AFREAF integrates advanced deep neural networks with adaptive preprocessing techniques, including face alignment via MediaPipe landmarks and histogram equalisation to enhance feature extraction. The framework employs OpenCV's DNN module for robust face detection, specialised convolutional neural networks for age and gender classification, and the FER+ model for emotion recognition. A novel temporal smoothing algorithm ensures prediction stability across video frames. Experimental evaluation demonstrates that AFREAF achieves real-time performance at 28.4 FPS while maintaining competitive accuracy rates of 68.5% for age estimation, 94.2% for gender classification, and 71.3% for emotion recognition. The modular architecture facilitates easy integration into diverse applications, including human-computer interaction, security systems, and behavioural analytics.
Facial analysis, emotion recognition, age estimation, gender classification, real-time processing, deep learning, temporal smoothing.
Deep Learning Framework for Indian Heritage Site Classification and Virtual Tour Generation using Transfer Learning
The Indian heritage sites represent several centuries of history, architecture, and diversity of various cultures. Creative approaches are required to secure accessibility and awareness, and to maintain and promote these places. Traditional methods of heritage interpretation often fail to deliver a detailed, scalable, and automated experience because they rely on human guides, fixed displays, or limited mobile applications. This paper proposes a deep learning-based system that integrates a virtual tour system and heritage site classification. We predict the 55 Indian monuments using the datasets (Kaggle + GitHub) with transfer learning using EfficientNetB0 and ResNet50. To improve generalisation, a unified dataset of 1,091 images was processed and augmented. The trained models achieved good classification performance and high validation accuracy (EfficientNetB0: 99.39%; ResNet50: 99.08%). To enhance cultural interaction, we combine the classifier with a virtual tour module that captures the inside and outside views of the architectural site, along with textual descriptions in JSON files, with each landmark having its own. Users can upload an architectural image, label the monument, and automatically navigate its interior via a structured virtual tour using a web-based GUI (React frontend + FastAPI backend). The proposed system addresses gaps in existing systems by being automated, scalable, and accessible. Future developments for this system include multilingual support, AR/VR support, and implementation as an offline mobile/web application to increase digital heritage preservation.
Classification, Transfer Learning, EfficientNetB0, ResNet50, Indian Heritage, Virtual Tour.
Light-SustainFormer: A Lightweight Temporal Fusion Transformer-Based CNN–BiGRU Framework for Sustainable Smart Building Energy Forecasting
Energy consumption forecasting is central to the design of smart, sustainable, and cost-efficient buildings. Recent deep learning and Transformer-based models have improved forecasting accuracy, yet most such models prioritise predictive performance over computational cost and model size. Full Transformer and Temporal Fusion Transformer architectures capture long-range temporal patterns, but their large dimensions and multiple attention blocks require high computational cost. This paper introduces Light-SustainFormer, a lightweight deep learning framework for short-term energy consumption forecasting in smart buildings. We propose a Lite-Temporal Fusion Transformer attention module that streamlines the conventional TFT by using fewer attention heads and a single attention block with a lightweight feed-forward layer. Instead of applying an attention block directly to raw time-series data, the proposed model first passes the inputs through CNN layers to capture short-term energy fluctuations, followed by Bi-GRU layers to produce compact temporal representations. This CNN–BiGRU-based compression reduces computational cost without sacrificing the ability to model long-range energy usage patterns. Light-SustainFormer learns energy consumption behaviour across weather, occupancy, time of day, and seasonal variations. On experimental benchmarks, the Light-SustainFormer achieves an MAE of 0.118 kWh, an RMSE of 0.164 kWh, a MAPE of 4.82%, and an R² of 0.961. These results demonstrate that a lightweight, attention-based architecture can deliver strong forecasting accuracy at low computational cost, providing a practical solution for energy management in smart buildings.
Smart Buildings, Energy Consumption Forecasting, Sustainable Energy Management, Light-SustainFormer, Temporal Fusion Transformer, CNN-BiGRU, Lightweight Attention.
Cultivate: A Configurable AI Agent for Scaling Agricultural Extension Services in Ghana
Ghana's agricultural extension system operates at a farmer-to-extension officer ratio of 1:1,500 three times the FAO-recommended standard of 1:500. Agronomists cannot serve the farmers who need them, and no existing system lets them train and deploy their own configurable AI agents. This paper presents Cultivate, an AgroAgent-as-a-Service (AaaS) platform that enables agronomists to upload domain knowledge, configure agent behaviour, and deploy AI agents that answer farmer queries at any time, unlike Farmer. Chat and FarmerAI, which use centrally curated knowledge bases, Cultivate gives each agronomist direct control over their agent's knowledge, communication style, and escalation threshold. The platform is built on Next.js, Mastra AI, Claude Sonnet 4.5, and Supabase with pgvector for retrieval-augmented generation (RAG). A pilot with Farmitecture a Ghana-based urban farming startup with 70+ customers and two agronomists validated the system with one agronomist and one farmer. The agronomist set up a fully configured agent in under ten minutes without technical guidance and estimated that the platform could handle 50-60% of his query load. The farmer said responses felt specific and grounded. The pilot demonstrated that decentralised, agronomist-trained AI agents are technically feasible and well-received by their intended users. Limitations include a small pilot sample, heuristic confidence scoring, and no WhatsApp integration yet.
agricultural extension services, configurable AI agents, retrieval-augmented generation, Agent-as-a-Service, urban farming, human-in-the-loop, Ghana, knowledge transfer
Hiding Patient Data in Medical Images in DICOM Metadata
Keeping medical images and patient records in separate systems creates a huge security hole. Central databases are basically magnets for hackers. And if technicians strip out metadata to share a scan safely, doctors lose the clinical context they need. We fixed this structural flaw by locking patient data directly inside the DICOM file. We never touch the image pixels. Even minor pixel changes ruin a scan's diagnostic value. Metadata steganography bypasses that issue entirely. The security setup operates in layers. Patient text undergoes AES-256 encryption. We apply DNA cryptographic mapping next, and then hide the ciphertext inside reserved DICOM tags. The medical scan stays completely intact. During testing, the implementation hit an infinite Peak Signal-to-Noise Ratio (PSNR). That means zero visual distortion. Data extraction works perfectly. We also built in a SHA-256 hash check to catch any tampering instantly. Tying clinical identity straight to the imaging file lets the system ignore vulnerable central storage. It creates a highly secure, self-contained medical record.
DICOM, DNA Cryptography, AES Encryption, Steganography, Healthcare Security, Medical image Steganography
Modernising On-Prem Government Systems to Cloud-Native Architectures: A U.S. Public Sector Case
U.S. public‑sector agencies continue to rely on legacy on‑premises systems designed for fixed workloads, limited concurrency, and infrequent reporting. As service expectations shift toward mobile access, real‑time data availability, and uninterrupted operations, these environments increasingly drive high maintenance costs, limited scalability, and challenges in meeting evolving security and audit requirements. This case study examines the cloud‑native modernisation of a state‑level workforce services platform operated by a U.S. labour agency. The modernisation replaced a monolithic on-premises labour-exchange system with a Microsoft Azure–based architecture utilising serverless computing, managed data services, and infrastructure-as-code. The transformation achieved 78% less manual reconciliation, 82% higher anomaly‑detection accuracy, an 11.7% improvement in data quality, and an 81% reduction in business costs. The findings demonstrate that phased, policy‑aligned cloud‑native adoption can deliver substantial operational, scalability, and accountability benefits for public‑sector institutions.
IT modernisation; Cloud-native architecture; Government cloud migration; Legacy system modernisation; Microsoft Azure; Serverless computing; Infrastructure as Code (IaC); Public-sector compliance; Digital government services; Workforce systems modernisation; Data governance and analytics; High-reliability government systems.
Role-Based Secure Access and Searchable Encryption Model for Medical Records
The rapid adoption of cloud computing in healthcare systems has enabled efficient storage and sharing of electronic medical records. However, medical data contains highly sensitive information, making security, privacy, and access control critical challenges. Traditional encryption techniques ensure confidentiality but do not support searching over encrypted data. Moreover, healthcare systems involve multiple users such as doctors, nurses, patients, and insurance agents, each requiring different access privileges. This paper proposes RB-SEMR, a Role-Based Secure Access and Searchable Encryption Model for Medical Records. The proposed system integrates role-based access control with searchable encryption to enable secure storage, fine-grained access control, and efficient keyword search over encrypted medical records. The RB-SEMR model improves data confidentiality, access accuracy, and system efficiency, making it suitable for cloud-based healthcare environments.
Role-based access control, Medical Records, Cloud Security, Data Privacy
Data-Driven Climate Change Impact Analysis Using Cross-Validation and Cybersecurity
The effects of climate change are becoming more evident in the form of temperature variations, rainfall patterns, and air quality, and require stable, real-time, and uncompromising data-driven evaluation methods. In this paper, the researcher has suggested and demonstrated a unified system of using Internet of Things (IoT)-based environmental monitoring, cybersecurity, and statistical data analytics to understand the effects of climate change. The IoT sensors were used to collect real-time environmental data (temperature, humidity, rainfall, and air quality) and to securely transmit and store the data, using encryption and authentication protocols to maintain data integrity and reliability. The gathered data were analysed and pre-processed using descriptive statistics, time-series analysis, correlation, and regression to identify climate trends, interrelationships, and main impact drivers. The findings showed that there was considerable change in climate parameters, with increases in temperature and worsening air quality as the major contributors to environmental stress, though rainfall showed a moderating effect. The IoT-based data were verified against secondary meteorological data to ensure their accuracy and authenticity. The authors found that secure, data-driven IoT frameworks offer an effective and scalable approach to climate change impact analysis and may support informed environmental planning, risk assessment, and adaptive decision-making.
Climate Change Impact Analysis, Internet of Things (IoT), Data-Driven Analytics, Cybersecurity, Environmental Monitoring, Climate Variability