Authors - Sunkyo Jeong, Yongbeom Park Abstract - The brisk developments of advanced deep learning techniques have led to diverse applications of it in different sectors, including healthcare sec tor. Breast cancer is one of the most common and deadly cancer amongst women and the success percentage of the treatment depends heavily on the stage at which the detection happens. This field opens gateway of deep learn ing application in detecting of breast cancer tumour type at an early stage. In this research paper, model and the application of a CNN based early breast cancer detection algorithm is proposed. In this approach, the Wisconsin Hos pital Breast Cancer Database is considered to train the model and test the accu racy of the model. This study shows promising results by concluding Convolu tional neural network-based model is 98.24 % accurate which this better than previous models. Moreover, this paper proves that such application of deep learning techniques holds huge promise for bettering healthcare sector.
Authors - Francklin Rivas, Thanh Tran, Jorge J Roman, Aysha Al Ketbi Abstract - The rapid proliferation of GenAI has transformed the phishing threat landscape into one characterized by realistic, tailored, and scalable attacks on text-based, web-based, and multimodal platforms. The success rate of social engineering attacks has increased significantly due to advances in large language models, deep-fake technology, and automated phishing-as-a-service offerings. Despite notable advances in current phishing detection technologies, many oper ate as black-box systems and struggle to detect AI-generated, context-specific, zero-day phishing attempts. The resulting lack of transparency, combined with poor realistic dataset quality and inadequate resilience against adaptive threats, has further amplified trust concerns. This survey presents a comprehensive over view of the detection strategies based on semantic, structural, and multi-quality feature representations, with a concise review of the models of GenAI-enabled phishing attacks. Various detection methodologies, including machine learning, deep learning, and fusion-based techniques, are reviewed, with an emphasis on explainable AI methods like SHAP, LIME, attention visualization, and Grad CAM, which provide more understandable interpretations of AI-driven deci sions. To facilitate transparent, reliable, and trustworthy phishing defenses that make use of GenAI, the survey concludes with discussions of response mecha nisms, privacy-preserving learning strategies, and governance issues, with open questions and potential directions for future research.
Authors - Malika Acharya, Ankit Jain Abstract - Since Lin and Zadeh proposed granular computing in 1996, an increasing number of researchers have begun to study information granularity, which simulates human cognition to handle complex problems. Granular computing advocates observing and analyzing the same problem at different levels of granularity. Coarser granularity leads to more efficient learning processes and stronger robustness to noise, whereas finer granularity is able to capture more detailed characteristics of objects. Selecting appropriate granularity according to different application scenarios can therefore solve practical problems more effectively. This paper proposes a novel support vector regression algorithm via granular computing approach, which constructs regression models using granular balls generated from the dataset as inputs rather than individual data points. First, we analyze the geometric relationship between classification tasks and regression tasks. Then, based on this geometric relationship, we employ twin support vector classification algorithm via granular computing approach to address regression problems.
Authors - Dang Trong Hop, Than Ngoc Thien Abstract - Medical image classification is of immense importance in the context of early-stage diagnosis of various neurological diseases, including Alzheimer’s disease and brain tumours. However, it remains infeasible for conventional deep learning architectures to efficiently encode frequency domain information and long-range spatial dependencies found in medical images. In this paper, a novel Hybrid Wavelet CNN Vision Trans-former, coupled with Explainable Artificial Intelligence, has been proposed for efficient and accurate medical image classification. In the proposed architecture, the application of discrete wavelet transform, convolutional neural networks, and Vision transformers for medical image classification has been presented. Additionally, explainability aspects have been addressed using the Grad-CAM technique. The proposed model was experimented with using two datasets: one for Alzheimer’s disease MRI and another for brain tumours. The experimental results reveal that the proposed deep learning architecture achieves an accuracy of 96.8%, precision of 0.96, and recall of 0.97, F1score of 0.97 for the brain tumours dataset, which beats conventional CNN, vision Transformer, and Wavelet CNN architectures. The integration of explainable AI further enhances model transparency and clinical reliability, making the proposed framework suitable for real-world medical diagnostic applications.
Authors - Neha Aggarwal, Rajiv Singh, Swati Nigam Abstract - One advantage of using Large Language Models (LLMs) is the automation of tasks and the analysis of information. Engineering drawings, on the other hand, are standardized representations of products; they document their dimensions and geometries. Users can utilize them for manufacturing parts, assembly guides, and engineering analysis, among other uses. This article aims to 1) evaluate whether an LLM is capable of interpreting engineering drawings, 2) identify how it interprets them, as it may use a standard on which the generation of these drawings or the interpretation of images is based, and 3) determine if users as students can employ LLMs as a guide to interpret drawings. The results showed that the user requesting an interpretation of an engineering drawing must be familiar with the field, as the LLM sometimes fails to extract the correct in-formation from a drawing; furthermore, any detail in the drawing can confuse the LLM. Once the LLM extracts the correct information from the drawing, it can use it to generate CNC code to machine a part, predict its behavior using a neural network, or perform engineering analysis, to name just a few examples.
Authors - Muhammad Elfata Rasyid Hammuda, Irmawan Rahyadi Abstract - Inventory management in warehouse environments frequently faces recurring limitations related to material searching, manual record updating, and control inconsistencies, which increase delays and disrupt operational continuity. This study develops an intelligent stock-tracking system based on weight sensing using load cells, signal conditioning through the HX711 module, and processing via an ESP32 microcontroller, with real-time data transmission using MQTT and visualization through a Unity-based mobile application with augmented reality (AR) support. The study included the diagnosis of the current process through process mapping and ABC analysis to prioritize critical consumables, the design of the system architecture, the implementation of the IoT prototype and its integration with the AR interface, and performance evaluation through time comparisons, before-and-after record analysis, and administration of the System Usability Scale (SUS) questionnaire. Findings indicate operational improvements in efficiency and record consistency, along with a favorable perceived usability among the evaluators.
Authors - Ikram Ahamed Mohamed, Hafiz Abdulla, Mohaideen Mohamed Mohabilasha, Fiyaz Ahmed, Pankaj Chandre, Rohini Bhosale Abstract - The Electric vehicles (EVs) are one way to help the environment by reducing carbon emissions and aiming for the net zero supply chain in logistics. This paper is a complete readiness assessment frame work for the green logistics practices on using electrics vehicles. The method categorizes preparation factors in five key categories, i.e., strategic and governance commitment, technological and infrastructure capability, financial and Investment Capacity, operational and human resource readiness, and environmental and policy alignment. It is pro-posed to use a multi-criteria decision-making framework to analyze the relation-ship between these variables and quantify the level of organizational readiness by using language evaluation scales converted to fuzzy numbers. The study con-tributes to the theoretical knowledge of creating a unified property of the various readiness criteria in a unitary evaluation framework and synthesises empirical methods with a measurable metric of the uptake of the electric vehicle in the logistics networks. Practically, the framework assists logistics managers, legislators, and sustainability planners in identifying issues, establishing priorities on investments, and accelerating the transition to the low-carbon transportation systems. The findings support the concept of fact-based decision-making that can lead to a green logistics revolution which can expand and remain sustainable.
Authors - Sabid Rahman, Sadah Anjum Shanto, Segufta Nasrin Tamanna, Zurin Alam Aongon, Md. Soadul Islam, Nasirul Islam Abstract - This research suggests a system for the real-time detection of road hazards, specifically potholes, cracks, and open manholes, using deep learning and image processing, and pinpointing the exact geographical location of the defects. These defects can cause road accidents, vehicle damage, traffic congestion, and other inconveniences. To solve these, a YOLOv8m model integrated with the CBAM module was developed for enhanced feature attention and trained on a custom dataset of 2,400 road images containing the three hazard classes. The model achieved a mAP@50 of 82.2%, and the individual class performance scores are 72.2% for potholes, 81.0% for cracks, and 93.3% for open manholes, and a recall of 76.4%, demonstrating reliable performance under varied conditions. An OCR module was integrated with the CBAM-YOLOv8 model to extract GPS coordinates from user-captured photos and videos, and an interactive mapping interface was designed to show and report the exact locations of detected hazards for timely action by authorities.
Authors - Mandar K Mokashi, Sonali P Bhoite, Vishal Nayakwadi, Atul P Kulkarni, Parikshit Mahalle, Pankaj Chandre Abstract - The purpose of this study is to examine the impact of DAT, AIR and ICM to-ward DAM in SMEs and at the same time determine the moderating effect of in-ternal control maturity. Drawing on the technology–organization–environment (TOE) framework and Resource-Based View (RBV), this study utilises a quantitative approach by employing Partial Least Squares Structural Equation Model-ling (PLS-SEM). Data was collected through structured questionnaires sent out to SMEs that have begun using digital audit tools. The relationships with DAM of DAT, AIR and ICM presented evidence on the individual impact on DAM indicating that technological readiness, organizational willingness to accept AI solutions successfully and mature internal controls are vital. Nevertheless, internal control maturity is not conducive to stronger.
Authors - Nguyen Thi Hoi, Dao Thi Huong Abstract - This аrticle exаmines the impаct of аccelerаted digitаlizаtion of the Uzbek econo-my on improving the effectiveness of pаrticipаtory budgeting. Reforms аimed аt creаting а "New Uzbekistаn" hаve elevаted pаrticipаtory budgeting to а key tool for citizen engаgement аnd increаsing the trаnspаrency of budget аllocаtion. However, the complexity аnd multifаceted nаture of this work аnd the further de-velopment of pаrticipаtory budgeting require the constаnt аdаptаtion of proce-dures, tools, аnd mаnаgement аpproаches to the emerging digitаl reаlities. The purpose of this study is to substаntiаte the need to trаnsform the pаrticipаtory budgeting mechаnism using аrtificiаl intelligence technologies аnd propose prаcticаl solutions to improve the efficiency, fаirness, аnd sustаinаbility of this process. Bаsed on аn аnаlysis of the regulаtory frаmework аnd current prаctices in implementing pаrticipаtory budgeting projects in the Republic of Uzbekistаn, key chаllenges limiting the potentiаl of pаrticipаtory budgeting hаve been identi-fied, including: low digitаl literаcy аmong some of the populаtion, limited func-tionаlity of digitаl plаtforms, insufficient аutomаtion of project evаluаtion аnd se-lection processes, weаk integrаtion with government informаtion systems, аnd а lаck of аnаlyticаl tools for forecаsting sociаl performаnce. The study proposes аreаs for improving the mechаnism, including expаnding the functionаlity of the Open Budget plаtform, implementing аrtificiаl intelligence, big dаtа, аnd digitаl plаtforms to increаse the openness аnd effectiveness of аnаlyticаl dаtа, аs well аs using elements of finаnciаl modeling to forecаst future stаte budget expenditures аnd develop multifаctor criteriа for аssessing the effec-tiveness of pаrticipаtory budgeting projects. The prаcticаl significаnce of the аrti-cle lies in the development of а comprehensive аpproаch to modernizing pаr-ticipаtory budgeting, which contributes to increаsing citizen trust in government institutions, optimizing the use of budgetаry resources, аnd аchieving the goаls of the Digitаl Uzbekistаn 2030 strаtegy. The results obtаined cаn be used by gov-ernment аgencies, locаl governments, аnd developers of digitаl solutions in public finаnce.
