Breast cancer is a primary cause of mortality among women across the world, and early diagnosis and proper classification of the subtypes are of vital importance in enhancing the survival rate. The paper is a review synthesis of recent research developments in explainable artificial intelligence (XAI) with machine learning (ML) and deep learning (DL) models to diagnose, predict, and segment breast cancer based on 20 peer-reviewed articles published between 2023 and 2025. The significant themes are the use of XAI methods, including SHAP and LIME and Grad-CAM, to make medical imaging processes such as mammography, ultrasound, MRI, and histopathology images more understandable, and genomic and clinical data. The analyzed literature shows a higher level of diagnostic accuracy (between 84 and 99.5) and attempts to overcome such issues as data imbalance, high dimensionality, and clinical trust by visualizing features of importance and using attention mechanisms. This review shows the transition to clear AI systems that are in line with clinical processes, where there are gaps in multimodal integration and real-world testing.

Keywords : EAI, SHAP, LIME, Grad-CAM, DL, ML, Medical Imaging, Interpretability, Molecular Subtyping.

Author : Pakala Bhanu Prakash , K Naveen

Title : Enhancing Breast Cancer Diagnosis Using Explainable Models

Volume/Issue : 2025;2(4 (October-December))

Page No : 62 - 67

The Internet of Vehicles, or IoV, is a disruptive technology that comes with very minor latency time and high bandwidth support for the inter-connection of the entire traffic: vehicles, roads, and users. But this huge interconnection also makes vehicular networks vulnerable to various attacks like denial-of-service, impersonation, botnets, and zero-day attacks. The paper provides an overview of the latest trends in intrusion detection systems enabled by artificial intelligence (AI), such as machine learning (ML), Deep learning (DL), and hybrid methods in IoV networks. Aspects like ensemble-based training, deep neural design, knowledge distillation, and privacy-preserving systems such as federated learning and homomorphic encryption are being discussed. The performance evaluation conducted on both the actual and standard datasets shows that these complex ML/DL architectures are not only highly accurate but also very fast with short delays and are capable of detecting both regular and new threats. Other challenges like unbalanced data, low-power devices, zero-day attacks, and model interpretability are also examined. Moreover, the recent progress made in AI-based IDS and privacy-aware systems points towards a trend of a scalable, secure, and trusted IoV. The paper provides an overview of current trends, emphasizes necessary future research, and gives a glimpse of resilient IDS that would be able to secure vehicular networks in advanced and automated transportation systems.

Keywords : IoV, Intrusion Detection System, FL, Anomaly Detection, Autonomous Vehicles, Security.

Author : S Sunil Kumar , G Lokesh

Title : Securing Internet of Vehicles (IoV) : Robust Machine Learning Models

Volume/Issue : 2025;2(4 (October-December))

Page No : 55 - 61

Wheat is a major food commodity in the world, but it faces serious disease attacks, which have caused huge losses every year. The review summarizes the recent progress in deep learning (DL), especially, multi-stage ensemble and lightweight neural networks, to detect wheat leaf disease automatically. The models such as DenseNet and ResNet are highly accurate but are prone to overfitting, inefficiency in computation and generalization to real-field conditions. We discuss how ensemble learning techniques and architectural advances e.g. feature fusion, attention mechanism and knowledge distillation can be used to make them more robust, better at feature extraction and ameliorate environmental factors (complex backgrounds, occlusion etc.). The above analysis also establishes that multi-stage models are far more effective than single models, however, there is an issue of critical trade-off between accuracy and computational cost. Future efforts should focus on creating light interpretable models based on multisource data to be used on large scale and in real-time in precision agriculture.

Keywords : Wheat leaf disease Classification, Multi-Stage Ensemble Learning, CNN ,Transfer learning; Bagging.

Author : Mamanduru Sunil , Dharmaiahvari Prasad

Title : Multi-Stage Neural Network based Ensemble Learning Approach for Wheat Leaf

Volume/Issue : 2025;2(4 (October-December))

Page No : 49 - 54

Phishing attacks have become a key cybersecurity threat and have used the trust of users in order to harvest sensitive information. This research work is based on an advanced phishing detection model by combining feature selection tasks with machine learning and deep learning models. Using a labeled dataset, where the status field denotes legitimate or phishing websites, we do a performance evaluation and comparison of different models such as Graph Convolutional Network (GCN),Tab Transformer, Auto encoder, Feedforward Neural Network (FNN), and Deep Neural Network (DNN). By applying the optimum feature selection, we improve the performance of the models, lower the computational complexity, and improve the generalization. The system implementation is performed in Python programming language and deployed with a web interface for interaction (Flask web Service) according to the style of user interaction (html, css); Our results show that the synergy of integrating deep learning architectures with feature engineering results in good enhancement of phishing detection accuracy and robustness. This approach is scalable and efficient way to protect the users from phishing attacks in real world applications.

Keywords : Phishing Detection, Feature Selection, GCN, Tab Transformer, Auto Encode, FNN, DNN, Flask, Cybersecurity.

Author : Mekala Manoj Kumar , K. Niranjan

Title : A Comprehensive Machine Learning Approach to Phishing Detection Using Features Selection and Deep Learning Models

Volume/Issue : 2025;2(4 (October-December))

Page No : 43 - 48

Tone-mapping is a complex process for displaying HDR pictures on standard displays. There are several ways to tone-map pictures, thus it's important to come up with a fair quality measure to choose the best tone-mapping operator (TMO) and adjust its parameters to get the greatest reproduction quality. This is a novel way to objectively assess tone-mapped photos of real-life settings. It combines perceptually meaningful features picked using an acceptable technique. Additionally, the selection highlights the significance of perceptual factors in evaluating tone-mapped HDR video. A number of state-of-the-art criteria and three publicly available datasets are used to assess the feature combination. A different goal is suggested to optimize the picture quality. The foundation of this strategy is the DL-CNN fusion of many perceptually significant characteristics that have been meticulously chosen utilizing adequate high-level features.

Keywords : CNN, DL, ML, AI, TMO, CMOS, CCD.

Author : B Suresh Babu , Suman , Venkata Narayana , Venkata Ramana Reddy , N Naveen Kumar

Title : Features Fusion For Natural Tone - Mapped Image Quality Evaluation Using DL-CNN

Volume/Issue : 2025;2(4 (October-December))

Page No : 35 - 42

A wide range of topics related to electric two-wheelers (ETWs) and related technologies are covered in the literature study. The increasing significance of hybrid and electric automobiles as remedies for ecological predicaments in the transportation domain, stressing the necessity of tackling concerns like exorbitant battery expenses, energy usage, and charge durations. Battery-swapping charging stations are suggested as a possible remedy; nevertheless, difficulties in guaranteeing a sufficient supply of batteries are mentioned. Particular focus is placed on the particular difficulties large-sized electric two-wheelers have, which call for a different strategy than that of electric passenger cars. The possibility of various energy technologies, such as hybrid fuel cells and supercapacitors, in next-generation automobiles is examined. Research also looks at how air and noise pollution are affected by electric cars, addressing possible safety issues brought on by their low noise levels. The literature study provides significant insights into the field of electric two-wheelers by addressing a wide range of themes, including technical difficulties, energy storage methods, regulatory standards, and environmental concerns

Keywords : Electric two-wheelers, battery swapping, energy storage, hybrid technologies, environmental impact.

Author : Adarsh K , S Christopher Ezhil Singh , P Sridharan , T Mary Little Flower , S Jerril Gilda

Title : A Review on Hybrid Electric Motor Cycle

Volume/Issue : 2025;2(4 (October-December))

Page No : 31 - 34

The work compared the leaching of heavy metals (Cr, Ni, Zn, and Pb) from the hybrid bricks using the Toxicity Characteristics Leaching Procedure (TCLP) and Netherland’s tank leaching tests. Hybrid bricks were prepared by partially replacing clay with an increasing weight percentage of tannery sludge (0, 9, 18, and 27% by weight of brick specimen) and a constant weight percentage (10% by weight of brick) of glass powder. The prepared mixes were fired to 900oC, 950oC, and 1000oC to assess the leaching potential of metals from hybrid bricks at different temperatures. The TCLP test was conducted as per the United States Environmental Protection Agency (USEPA) 1311, whereas the Netherlands tank leaching test was performed as per the NEN 7345 standard. In both methods, the leaching of metals increased with the tannery sludge amounts and decreased with an increase in temperature. The release of metals from the TCLP test was slightly higher than that of the Netherlands tank leaching test. The higher amounts of metal released from the TCLP test were due to exposure to more areas of particles due to the crushing of particles. The results obtained from the TCLP test are more appropriate to the actual field conditions arising in the service life of materials.

Keywords : leaching of metals; USEPA 1311; bricks; NEN 7345.

Author : G Venkatesh , S Krishnaiah

Title : Comparative Study of Heavy Metal Leaching from Clay – Tannery Sludge – Glass Powder Bricks using TCLP and Netherlands Tank Leaching Methods

Volume/Issue : 2025;2(4 (October-December))

Page No : 26 - 30

The current study article examines the growing use of automation and Artificial Intelligence (AI) in enterprises' diverse human resource roles. The process of improving the HR department's efficiency through human resource automation involves relieving staff members of time-consuming manual activities so they can concentrate on more complicated responsibilities like decision-making and strategy-making. Technology like artificial intelligence is defining modern paradigms for conducting business. It is a solution that completes the majority of low-value HR tasks so that the strategic scope of work can receive more attention. Artificial intelligence is most frequently used in human resource applications like hiring and onboarding, internal mobility and employee retention, and task automation. HR professionals may expedite candidate vetting with the help of AI. By employing more user-friendly forms and experiences that draw higher talent and guarantee that interested parties complete their applications, adopting AI tools improves the application process for candidates as well. This research work is based on concepts and descriptive analysis. This research study used secondary data from recent research articles that were found on search engine websites. The study is an effort to recognize the important role AI has played in improving organizational decision-making. AI has the potential to improve HR departments in a number of ways. Employing AI may assist HR managers in a number of ways, including keeping up with trends; analysing employee sentiment; streamlining talent acquisition and onboarding; and spotting patterns to make decisions. HR will take on more strategic roles and become more data-driven. For HR professionals who are eager to keep up with the pace ,the technology reckoning can open up a whole new chapter. This observation will assist policymakers in understanding the advantages and importance of implementing AI in human resource management.

Keywords : Artificial Intelligence, Automation, Human Resource Management, HR Professionals, Technology.

Author : Artificial Intelligence and Automation in Human Resource

Title : Artificial Intelligence and Automation in Human Resource

Volume/Issue : 2025;2(4 (October-December))

Page No : 22 - 25

Introduction of Artificial Intelligence in organisations has transformed the functions of management through automation of tasks, facilitating data based decision making, accelerating competence easing the management to concentrate on organisation as well as employee advancement. Business related AI was being developed since 1950s. Significant developments came into existence since 1980s. It gained momentum in 2010. At present an approximate 83 % of the companies implemented AI. Business Management is being restructured with the introduction of AI in business. Companies are able to improvise the key functions of Management which include planning function, organising function, directing activity, staffing function and controlling activities and build data-driven decisions. AI is now considered as an advantage in Staffing function. Artificial Intelligence is playing a dominant role in Employee Engagement in the present tech world. AI implementation facilitates various tools for addressing clients’ enquiries promptly, manage people and develop people, monitor performance, convene meetings and generate reports. With this, all corporate concerns initiated the implementation of AI towards Employee Engagement and address the problems of Employee Engagement. AI facilitates decision making related to Headhunting, Recruitment activities, assessing the applicants, selection, training based on individual strengths and weaknesses, Performance Evaluation, future planning. This paper highlights the benefits of implementing AI in Employee Engagement and risks that may be encountered in AI implementation in Employee Engagement. It also covers the AI driven employee engagement tools which replaces the previous time-taking tasks in HR. Artificial Intelligence hastens employee engagement and employee retention through concurrent feedback, experiences which creates a compassionate and appealing work environment.

Keywords : AI , Employee Engagement, Employee Retention, Decision Making, Management functions.

Author : P. Shobha Rani , Mahadevan M

Title : A Conceptual Study on Role of AI in Employee Engagement

Volume/Issue : 2025;2(4 (October-December))

Page No : 18 - 21

This paper introduces an innovative solution to water pollution through the development of a remote-controlled floating waste cleanup system. Designed to autonomously collect floating waste from water bodies, the system minimizes human intervention while ensuring safe and efficient operations. It incorporates a belt-driven waste collection mechanism that continuously gathers debris into a designated collector bin, powered by a battery charged via a solar panel to promote sustainable energy use. The cleanup vessel is equipped with an ESP32-CAM module that enables live video streaming for real-time remote monitoring, allowing operators to oversee the cleaning process effectively. An integrated ultrasonic sensor measures the fill level of the collector bin, providing essential feedback to ensure timely waste disposal. Additionally, a web-based dashboard offers wireless control, enabling operators to manoeuvre the vessel in multiple directions—forward, reverse, left, and right, while managing the activation of the conveyor belt. Overall, this system not only improves the efficiency of water surface cleaning but also reduces the risks and labor associated with manual cleanup operations. It demonstrates a scalable, environmentally friendly solution for maintaining cleaner aquatic environments through the integration of automated control, renewable energy, and real-time monitoring technologies

Keywords : Floating Waste Cleanup, ESP32-CAM, Conveyor Belt, Water Pollution, Renewable Energy.

Author : M. Revathy , M. Yaswanth Maruthi Pritam , B Thasneem , M. Neelima , S. Fatheema

Title : Remote Controlled Floating Waste Cleanup System

Volume/Issue : 2025;2(4 (October-December))

Page No : 12 - 17

The current study article examines the growing use of automation and Artificial Intelligence (AI) in enterprises' diverse human resource roles. The process of improving the HR department's efficiency through human resource automation involves relieving staff members of time-consuming manual activities so they can concentrate on more complicated responsibilities like decision-making and strategy-making. Technology like artificial intelligence is defining modern paradigms for conducting business. It is a solution that completes the majority of low-value HR tasks so that the strategic scope of work can receive more attention. Artificial intelligence is most frequently used in human resource applications like hiring and onboarding, internal mobility and employee retention, and task automation. HR professionals may expedite candidate vetting with the help of AI. By employing more user-friendly forms and experiences that draw higher talent and guarantee that interested parties complete their applications, adopting AI tools improves the application process for candidates as well. This research work is based on concepts and descriptive analysis. This research study used secondary data from recent research articles that were found on search engine websites. The study is an effort to recognize the important role AI has played in improving organizational decision-making. AI has the potential to improve HR departments in a number of ways. Employing AI may assist HR managers in a number of ways, including keeping up with trends; analysing employee sentiment; streamlining talent acquisition and onboarding; and spotting patterns to make decisions. HR will take on more strategic roles and become more data-driven. For HR professionals who are eager to keep up with the pace ,the technology reckoning can open up a whole new chapter. This observation will assist policymakers in understanding the advantages and importance of implementing AI in human resource management.

Keywords : Artificial intelligence, Automation, Human Resource Management, HR Professionals, Technology

Author : S. Shaheera Bhanu

Title : Revolutionizing Human Resources through Artificial Intelligence and Automation

Volume/Issue : 2025;2(4 (October-December))

Page No : 8 - 11

The cryptanalysis of multimodal datasets is particular, given the distinct statistics formats and cryptographic strategies that pose demanding situations to analysis. In this studies paintings, an green cryptographic algorithm for boosting the robustness and accuracy of cryptanalysis the usage of deep mastering is proposed. This method includes the usage of CNNs and transformer-based totally architectures in a systematic evaluation of multimodal datasets along with textual content, pictures, and numerical statistics to pick out the cryptographic styles and vulnerabilities. The model consists of feature extraction, modality fusion, and automated key inference to decode encrypted information with progressed efficiency.

Keywords : Kidney disease classification, CT images, CNN- LSTM, Deep learning, Grad-CAM, Medical imaging.

Author : M. Pavan , S. Dilli Babu

Title : An Effective Cryptographic Algorithm For Multimodal Datasets Cryptanalysis Using Deep Learning

Volume/Issue : 2025;2(4 (October-December))

Page No : 1 - 7