Volume 14 | Issue 4 |

 Abstract

Healthcare professionals now frequently use medical documents due to the expansion of the Internet. To facilitate collaboration while safeguarding private patient data, secure medical image transmission and management are crucial. This study examines several approaches to securely exchanging medical data, emphasising both their benefits and drawbacks. We divide these strategies into two categories: distributed strategies like blockchain and federated learning, and centralised strategies like encryption and watermarking. This study also looks at how medical image watermarking has changed over time, from conventional approaches to sophisticated AI-based systems. Deep learning models are regarded as "black boxes," providing more resilience and flexibility than conventional methods known as "white boxes," which are straightforward and easy to understand. In order to handle the increasing complexity of threats while maintaining the diagnostic integrity of medical images, this analysis highlights the necessity of integrating contemporary technology. Additionally, our work contributes to the ongoing discussion on improving data security in medical imaging by offering a thorough classification of watermarking techniques and outlining future research directions.

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 Keywords

Medical Image Security, Digital Watermarking, Blockchain-Based Traceability, Federated Learning, AI-Driven Tamper Detection

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

This study examines the correlation between the Latin American Artificial Intelligence Index (ILIA) and the Web Accessibility Index (WAIN) across 19 Latin American nations to assess the alignment of technological innovation with digital inclusivity. The ILIA gives a broad picture of how ready AI is by looking at things like infrastructure, data capacity, talent development, research and adoption, and governance structures. This index shows that some regions are better at digital transformation than others. For example, Uruguay, Chile, and Brazil are still ahead of the game when it comes to AI adoption because they have strong infrastructure and governance frameworks.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Artificial Intelligence Readiness (ILIA), Web Accessibility Index (WAIN), WCAG 2.2 Compliance, Digital Inclusivity, Sustainable Digital Transformation

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

As more and more autonomous and smart vehicles are used in ground transportation systems, new security problems arise. This change from operations run by people to those run by computers makes it easier for bad people to attack. As the Internet of Things (IoT) becomes more common in cars, they are always making and sharing a lot of data. Attackers can take advantage of this trend's weaknesses in complicated ways, like with Advanced Persistent Threats (APT). It is very important to be able to find APTs in vehicles that have IoT technology. To find these threats, we need better ways to do so. The urgent requirement for vehicle data privacy constrains conventional centralised Machine Learning (ML) methodologies. Also, there aren't many APT datasets available to the public in the vehicle field, which makes it harder to develop and test models. This is a big problem for cybersecurity in this area that is always changing. This study introduces an innovative Federated Deep Neural Network (FDNN) framework incorporating a privacy-preserving technique to address these challenges. The research emphasises the primary obstacles in APT detection and delineates its distinctive contributions to the domain. It talks about the research questions that are guiding the study. The UNSW-NB15, Edge-IIoTset, and CSE-CIC-IDS2018 datasets each represent a different stage of an APT attack. We use these datasets to look at and judge the framework that was made. For these datasets, the framework without the privacy-preserving technique gets APT detection accuracies of 97.32%, 96.81%, and 98.06%, respectively. But when the privacy-preserving technique is used, the framework's accuracies are 95.62%, 96.11%, and 95.63%, respectively. There are tables that show all of the results, as well as other evaluation metrics like Precision, False Positive Rate, and F1 Score. We use "Shapley Additive Explanations (SHAP)" analysis on the framework we made to find the most important features for finding APTs. This study validates the efficacy of a novel framework for identifying APTs in distributed vehicular contexts. The framework works well because it cuts down on the amount of data and the number of features. This was shown by extensive testing with several benchmark datasets. Future work will look into how well the framework can find APTs in different areas.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Federated Learning, Advanced Persistent Threats (APT), Connected Vehicles Security, Explainable Artificial Intelligence (XAI), Privacy-Preserving Deep Learning.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Tool condition monitoring (TCM) is very important in today's manufacturing because it makes sure that products are of high quality, cuts down on unplanned downtime, and makes production as efficient as possible. Over time, mechanical stress, heat, and friction cause cutting tools like milling cutters, drills, and lathes to wear down. Tool wear not only affects the size and surface quality of parts made, but it also raises production costs and can put workers' safety at risk. Most traditional TCM systems use manual inspection or supervised machine learning methods, which need a lot of labelled datasets. However, labelling data from industrial sensors takes a lot of time and work, and it is often not possible in real-world manufacturing settings, especially for machines that run on a continuous production schedule.

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 Keywords

Tool Wear Recognition, Unsupervised Learning, Semi-Supervised Learning, Sensor Fusion, Predictive Maintenance.

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

A significant amount of web text data, including news articles, blogs, social network status updates, online reports, and more, has been gathered along with the growth of the internet. Accurately classifying these web texts automatically is a crucial task in the fields of artificial intelligence and natural language processing. Numerous applications, including sentiment analysis, web filtering, automatic recommendation, and web information retrieval, are made possible by it. The issue is that it is challenging to accurately classify web text because of its complex semantic structures and distant relationships. Current word representations, like Word2Vec and GloVe, generate fixed word embeddings for each and every word. This paper presents a more advanced deep learning structure called BERT, BGCA, which combines BERT (Bidirectional Encoder Representations from Transformers), BiGRU (Bidirectional Gated Recurrent Unit), CNN (Convolutional Neural Network), and Attention in a web text classification problem to address the aforementioned problems. In order to help the model learn the syntax and semantics between words through bidirectional transformer architecture, BERT is used to obtain the contextual encoding of words in the web text. Contexts can be investigated both forward and backward using the BiGRU layer. The CNN layer is in charge of extracting important details and gram features from the text. and the focus is on capturing the importation of key terms. To confirm the effectiveness of the suggested model, BERT, BGCA, experiments were carried out on a sizable enough news dataset called THUCNews. Conventional embedding techniques (Word2Vec and GloVe) and BERT-based embedding were tested. According to the comparison results, the BERT significantly outperformed the static embedding techniques. On the 20 categories THUCNews news dataset, the high accuracy and F1 score (95.21%, 94.36%) and F, measure of 95.21% and 94.36%, respectively, demonstrate that the BERT, BGCA can achieve better classification results and be favourable to semantic representation when compared to other deep learning models like TextCNN. Therefore, it can be said that the BERT, BGCA method for large-scale web text classification is practical, scalable, and effective.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Web Text Classification, BERT (Bidirectional Encoder Representations from Transformers), BiGRU (Bidirectional Gated Recurrent Unit), Convolutional Neural Network (CNN), Attention Mechanism, Natural Language Processing,

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Creative Commons Attribution 4.0 and The Open Definition

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Working capital management is a very important part of financial management, which involves the management of short-term assets and liabilities of an organisation. This includes maintaining sufficient liquidity to conduct organisational activities in an efficient manner. In addition to this, this involves improving the profitability of the organisation. Working capital management plays an important role, especially in the manufacturing sector, because in such industries most of the funds are invested in inventories and receivables. In this study, working capital management strategies followed by JALARAM DOORS & HARDWARES, a manufacturing and trading organization involved in wooden doors manufacturing and hardware supply, have been studied. The study relies upon secondary data collected from financial statements of the organisation from 2021-22, 2022-23, and 2024-25. Financial ratio analysis will be conducted through the current ratio, inventory turnover ratio, and debtor's turnover ratio. From the findings, it is seen that although there are satisfactory levels of liquidity in the organization, there are some issues associated with the management of inventories and fluctuation in the profitability levels

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Working Capital, Liquidity, Inventory, Receivables, Profitability

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

The rapid proliferation of Internet of Things (IoT) devices has introduced significant cybersecurity challenges due to their resource-constrained nature and limited native protection mechanisms. Traditional intrusion detection systems (IDS), which rely on signature-based approaches and cloud-dependent architectures, are often unsuitable for decentralized IoT environments. This paper presents NeuroFence, a lightweight, AI-driven intrusion detection system designed for real-time cyber defense in edge-based IoT networks. The proposed system operates entirely on local gateways, such as Raspberry Pi, and leverages machine learning techniques to model normal network behaviour and detect anomalies in real time. NeuroFence incorporates a hybrid detection framework that combines rule-based analysis with unsupervised anomaly detection models, enabling both immediate threat identification and adaptive learning against evolving attack patterns. The system captures live network traffic, extracts features using sliding-window techniques, and evaluates traffic behaviour using models such as Isolation Forest and TinyML-based classifiers. A complete edge-first architecture is implemented, integrating packet sniffing (Scapy), lightweight data processing, local storage (SQLite), and an interactive dashboard built using Flask and React for real-time alert visualization and operator response. The system also includes mechanisms for safe mitigation, forensic logging, and continuous model improvement through feedback-driven learning. Experimental evaluation demonstrates that NeuroFence effectively detects anomalous IoT traffic with low latency and minimal computational overhead, making it suitable for deployment in resource-constrained environments such as smart homes, industrial IoT systems, and remote installations. The proposed solution highlights the potential of decentralized, AI-powered security frameworks in enhancing the resilience and autonomy of modern IoT ecosystems.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Internet of Things (IoT), Intrusion Detection System (IDS), Edge Computing, Machine Learning, Anomaly Detection, TinyML, Cybersecurity, Network Traffic Analysis, Raspberry Pi, Isolation Forest

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Land use pattern is a reflection of the interrelationship between natural resources and human activities in a region. Fatehpur district, located in the Ganga-Yamuna Doab, is highly suitable for land use studies due to its agrarian nature. The present study is based on the spatio-temporal analysis of land resources in Fatehpur district. The main objective of the study is to understand the distribution, trends, and variability of different land use categories in the district--such as agricultural land, forest area, fallow and wasteland, pasture land, and built-up area. In this research, secondary data (district statistical handbook, land records, and census data) have been used to classify land use and analyze changes over time. The analysis clearly indicates that agriculture is the dominant land use in the district, occupying the largest proportion of the total area, whereas forest area is very limited. Due to urbanization and infrastructural development, there has been a gradual increase in built-up areas, which is putting pressure on agricultural land. The study also reveals that land use patterns are strongly influenced not only by physical factors--such as soil, climate, and water resources--but also by socio-economic factors like population growth, technological development, and market accessibility. Additionally, issues such as land degradation, declining groundwater levels, and imbalanced land use are emerging challenges in the region. Finally, the study emphasizes the need for sustainable land use management measures, such as agricultural diversification and the promotion of modern irrigation techniques, to ensure balanced and long-term utilization of resources in Fatehpur district.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Land Use Pattern, Spatio-Temporal Analysis, Fatehpur District

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Over the past few years, the safety of women has emerged as an important issue in society, particularly in the presence of other people and when the circumstances are unfavorable during the nights. As the number of harassment and emergency incidents increases, the need to find intelligent, non-obtrusive safety solutions that will work in real time is growing. The proposed project is called Night Guardian: Empowering Women Safety with Hand Sign-based Communication and AI and suggests a new solution that would be based on leveraging artificial intelligence and computer vision to provide immediate assistance in case of distress scenarios without having to engage with the user directly. The system is developed based on the latest technologies like MediaPipe and OpenCV to record and process real-time video feeds of a webcam. MediaPipe is applied in the correct hand landmark detection and keypoint extraction in order to determine hand gestures very precisely. These features are extracted and converted into numerical forms and input to machine learning algorithms like Random Forest, Support Vector Machine (SVM) and K-Nearest Neighbor (KNN). Performance measures such as precision, recall, and F1-score are used to train and test the models to be reliable at gesture recognition. After a distressing gesture has been identified, an alert system is automatically triggered. This involves raising a local alarm to draw attention locally and an automated email notification and a picture of the gesture to emergency contacts or authorities that are predetermined. The combination of these alert mechanisms is to make sure that there is quick response hence increasing the possibility of intervention in time. With the application of AI, the system can be continuously and effectively used even in dynamic environments. Altogether, the offered system will be an effective and scalable way to enhance the safety of women with the help of intelligent automation. It offers a quick, convenient, and free form of emergency communication by using machine learning and real-time gesture recognition to deliver a hands-free form of communication. This project, apart from fostering innovation in technology, helps to enhance social well being by making people be empowered with proactive safety tools.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Over the past few years, the safety of women has emerged as an important issue in society, particularly in the presence of other people and when the circumstances are unfavorable during the nights. As the number of harassment and emergency incidents increases, the need to find intelligent, non-obtrusive safety solutions that will work in real time is growing. The proposed project is called Night Guardian: Empowering Women Safety with Hand Sign-based Communication and AI and suggests a new solut

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Creative Commons Attribution 4.0 and The Open Definition

 Abstract

Crop stress is one of the foremost threats to global food security, causing yield losses estimated at 20-40% annually. Traditional monitoring relies on costly sensor arrays or time-intensive manual inspection, limiting accessibility for small-scale farmers. This paper proposes a low-cost, camera-only crop stress detection system built around a fixed ESP32-CAM RGB module. A multi-task convolutional neural network (CNN) based on MobileNetV2 simultaneously classifies crop health (Healthy/Stressed), estimates a continuous stress severity score, and assesses soil surface condition (Dry/Moist/Wet). Temporal analysis via an exponentially weighted moving average (EWMA) over a rolling N-frame buffer enables detection of progressive stress onset before visible leaf damage appears, reducing false-positive alerts by 31% compared to single-frame classification. A hybrid learning pipeline combines supervised training on a labeled base dataset with semi-supervised pseudo-label fine-tuning for unseen crop types. Grad-CAM visualisations highlight stress-causative regions, improving model transparency and farmer trust. The system achieved 93.4% binary classification accuracy, 89.7% soil condition classification accuracy, and a stress severity MAE of 0.06 on held-out test data.

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Creative Commons Attribution 4.0 and The Open Definition

 Keywords

Crop Stress Detection, Convolutional Neural Network, ESP32-CAM, MobileNetV2, Hybrid Learning, Semi-Supervised Learning, Grad-CAM, Temporal Analysis, Precision Agriculture, Explainable AI.

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Creative Commons Attribution 4.0 and The Open Definition