All of the articles in this proceedings volume have been presented at the International Conference on computer science and communication engineering (ICCSCE-2025) during 25-26April, 2025 in Holy Mary Institute of Technology & Science, Hyderabad, India These articles have been peer reviewed by the...

Alzheimer’s Disease (AD) is a degenerative neurological disorder causing progressive memory loss and cognitive decline. This research focuses on improving AD diagnosis using deep transfer learning with pre-trained CNN models like MobileNet, ResNet, and VGG. By applying MRI image preprocessing, data augmentation,...

In this fast-growing world full of technologies like artificial intelligence, machine learning, data science and so on, securing a job especially for freshers is becoming a hard nut to crack. But this does not mean we stop putting our efforts. We just need to accelerate the process. “Campus-Connect”...

This paper proposes an AI-powered system that can generate facial sketches from a witness’s description of a criminal’s traits. Making use of advanced technologies like Natural Language Processing (NLP) and Generative Adversarial Networks (GAN), the model decodes detailed written or spoken inputs and...

The brain’s unchecked and fast cell proliferation is what fuels a tumor. It may be mortal if left untreated in the early stages. Accurate separation and classification remain difficult despite a great deal of work and good results. The diversity in tumor site, form, and dimensions greatly complicate...

Event-Trace is an AI-based approach that utilizes case descriptions in order to predict law section classifications based on K-Nearest Neighbors (KNN), Linear Support Vector Machine (SVM), Decision Tree, Random Forest and Extra Trees Classifier. These models were chosen because of their advantage in...

Monkeypox, a re-emerging zoonotic disease, has raised global concerns due to its increasing transmission. Accurate and early detection is crucial for effective outbreak management and disease control.Various AI-driven diagnostic techniques have been developed, focusing on deep learning-based image classification...

Medical Image Captioning, which is the automatic generation of descriptive text through medical images, has yet to be a very valuable assistant for clinicians regarding image interpretation and diagnosis. Notable in particular is the recent break-through on deep learning that has greatly improved the...

Mental health disorders, especially those of depression, are now a concern at the universal level which necessitates an approach for early diagnosis and treatment options that are accessible and technology-based. The paper presents a web-based depression screening and support system that aims to provide...

The satellite image time series classification plays a crucial role in remote sensing and offers multiple applications for agriculture, land management, and disaster monitoring. Although deep learning methods are powerful, they suffer from overfitting in some cases, especially when there is a lack of...

This paper presents an innovative AI telehealth solution that unifies multiple essential services into one comprehensive platform. Unlike existing healthcare systems that operate in silos, DocConnect integrates an AI chatbot for real-time medical diagnosis, doctor appointment scheduling, emergency ambulance...

Plant disease prediction is a crucial aspect of modern precision agriculture, helping farmers detect and mitigate crop infections before significant yield losses occur. The proposed system leverages XGBoost-based machine learning integrated with IoT sensors and Raspberry Pi to provide an efficient, real-time,...

The proposed AI-driven strategic decision-making system leverages advanced machine learning models, real-time data processing, and automated risk assessment to enhance business management efficiency. The system integrates neural networks, deep learning algorithms, and Bayesian networks to ensure high...

Certificate generation is essential in education, corporate training, and events, yet traditional manual methods are slow and error-prone. This paper presents an automated system utilizing blockchain for secure verification and transformer-based deep learning models for fraud detection. Techniques such...

Efficient traffic management in urban areas is a growing concern due to increasing vehicle density, traffic congestion, and accident risks. Traditional traffic control methods often lack the ability to adapt dynamically to real-time traffic conditions. This paper proposes an AI-driven traffic monitoring...

Authentication systems play a crucial role in verifying the identity of individuals, whether they are accessing physical spaces, digital platforms, or services. Traditional methods of authentication, such as passwords, PINs, and security questions, have limitations, including vulnerability to theft,...

Stroke is an illness that targets the arteries supplying blood to and in the brain. Stroke happens when a blood vessel that nourishes the brain is affected is clogged as a result of obstruction or hemorrhage. According to WHO Report, 3% population is affected by subarachnoid hemorrhage, 10% by intracerebral...

Maintaining discipline in institutional environments is a significant challenge, often requiring manual monitoring methods that are time-consuming and error-prone. This paper presents an automated system that utilizes machine learning for real-time discipline monitoring, specifically detecting the presence...

Skin cancer is a critical global health issue that requires early and accurate detection for effective treatment. This project introduces Skin Guard, an AI-powered cancer detection system designed to classify skin conditions into seven categories, including malignant melanoma, basal cell carcinoma, and...

The integration of new technologies such as artificial intelligence (AI) has transformed many fields, including fine arts, creating new possibilities for expression. In this paper, we present a model of a virtual painting platform using AI that aims to facilitate and democratize the painting process....

This research introduces a novel framework for detecting fake news using advanced transformer models combined with Chain-of-Thought (CoT) reasoning. The study utilizes the GossipCop dataset, employing ALBERT, Distilled GPT-2, and Google Flan T5 for reasoning-based representations. ClaimBuster was used...

The issue of inability to obtain personalized investment advice has remained a significant problem in the financial service industry due to prohibitive costs and limited access to traditional advisory services. The present study outlines an AI-based chatbot with a view to providing accessible financial...

Heart Valve Disease (HVD) is a critical cardiovascular condition and it affects millions of people globally. Early detection and diagnosis accuracy are imperative to prevent severe complications such as heart failure. Artificial Intelligence (AI) has emerged as a transformative technology in medical...

The paper introduces a novel approach as security threats continue to evolve in complexity and dynamism, the demand for advanced real-time surveillance systems has become increasingly critical. This paper presents a novel approach by integrating two state-of-the-art technologiesYOLOv8 (You Only Look...

Robotics has made great strides, and human–robot interaction (HRI) is increasingly crucial to providing the greatest user experience, reducing time-consuming activities and increasing public acceptance of robots.AI researchers frequently refer to robots as workspaces since they are artificial agents...

There is a significant challenge in densely populated areas which is traffic management in traditional ways leading to increased travel time, fuel consumption, and environmental pollution. Traffic signal controls operate on pre-programmed timings, failing to adapt dynamically to real-time traffic conditions....

Online applications for jobs have made recruitment complex, introducing problems such as dataset bias, unbalanced resume formats, and even heavy computational requirements. This paper surveys 44 peer-reviewed studies on methodologies, applications, and limitations of resume analyzers, explaining techniques...

This research proposes an innovative method for distinguishing real images from deepfakes using Vision Transformers (ViT) combined with the AdamW optimizer and cosine similarity. The approach utilizesViT’s self-attention mechanism to capture global spatial relationships and extract rich feature representations...

This study presents a data-driven system utilizing Random Forest to enhance organizational efficiency and foster innovation. The approach begins with data collection from diverse sources, followed by cleaning and preprocessing to ensure data quality. Feature engineering is applied to create relevant...

The objective of this project, the system is improved through the application of machine learning as well as natural language processing to diagnose the common diseases predominantly affecting females that include Urinary Tract Infection (UTI) as well as Polycystic Ovary Syndrome (PCOS). The constructiveness...

This observe introduces a revolutionary approach for enhancing nutritional monitoring and selling healthful consuming behaviour via deep analyzing technology. The primary objective is to correctly come across several meals objects and estimate their calorie content cloth in actual-time, offering customers...

Deep learning and machine learning techniques enable the AI system described in this research to enhance aircraft predictive maintenance operates. The program integrates three main components that function as Aircraft Monitor and support Battery Life Estimation and Jet Cycles Prediction. The Aircraft...

This paper introduces an intelligent voice-enabled mail client designed for touch-free usage enabling users to manage messages effortlessly via spoken commands utilizing cutting-edge speech-to-text and natural language understanding NLU the platform efficiently handles activities such as composing reading...

The automatic detection of white blood cancer is carried out by examining bone marrow microscopic images, which is an important development in the medical profession. For the purpose of accurately recognizing and categorizing leukemic cells, we suggest a method for using convolutional neural networks....

Fraud in digital payments poses a significant threat to user security, requiring robust measures for prevention and detection. The proposed project, SafeScan, offers a comprehensive solution to address these challenges by developing an integrated system for proactive fraud detection in digital payments....

Breast cancer is a deadly illness affecting women worldwide which makes early and precise identification is crucial. To increase diagnostic accuracy, this work presents a dual strategy that integrates deep learning (DL) and machine learning (ML) algorithms. Random Forest model is used to examine tabular...

In today’s online environment, websites face constant SQL injection and cross-site scripting attacks - mostly because many developers still do not properly check user inputs. Our team has built a smart scanning tool that finds these security holes, thoroughly checks websites for weak points, and gives...

Due to the huge number of job advertisements created by the quick growth of online job portals, it is difficult for job searchers to locate opportunities that are relevant to them. In order to create a customized job recommendation system, this study introduces a hybrid unsupervised learning framework...

Human Activity Recognition (HAR) employs machine learning to identify human activities and behaviors. Improvements in deep learning and kinetic models enable HAR systems to identify temporal and spatial features from images and videos to identify activities such as running, sitting, and eating. The applications...

Epidemic outbreaks present great challenges to public health systems, often leading to very significant social and economic issues. Thus, timely predictions help relevant authorities to take proactive measures to minimize the impact of such epidemics. This study proposes an interactive epidemic outbreak...

Technological advancements and shifting industry demands have caused job markets to change quickly, making it difficult for job searchers to find positions that align with their goals and skill set. Conventional recommendation systems frequently fall short because they rely on out-of-date data and ignore...

This research introduces a system for recognizing Assamese Sign Language (ASLR) aimed at improving communication for individuals in the Assamese deaf community. The system utilizes advanced computer vision and deep learning methods to accurately identify and convert static hand gestures representing...

The proposed work uses a multifaceted approach to address mental health concerns among students, incorporating various machine learning algorithms to improve accuracy and efficacy. Mental health plays a vital role in the overall success of a student, both in school and in life. When students take care...

Online payment fraud has grown to be a serious cybersecurity risk that can result in data breaches and monetary losses. This study introduces a fraud detection system based on Deep Quantum Neural Networks (DQNNs), which use quantum computing to boost accuracy over conventional machine learning methods....

After finishing their senior secondary studies, a majority of students face confusion about what to choose as a career path and take an ample amount of time to decide on it. Traditional counseling methods fail to provide personalized, data-driven recommendations. So many uncertainties and so much more...

Text classification is a cornerstone natural language processing (NLP) task that involves giving predetermined labels to a given text. Its applications span several domains, including topic modeling, fake news detection and sentiment analysis. Text categorizing is extended and highlights domain specific...

This study investigates the YOLOv11 deep learning architecture for detecting chronological age from dental panoramic X-ray images, which is vital for dentistry and forensic science. By leveraging a diverse dataset and employing preprocessing techniques, the model effectively extracts age-related dental...

Rice is currently among the most used staple circles in the world with immense economic and nutritional values. The genetic variations on rice grains are varied and thus lead to the occurrence of unique characteristics like texture, shape, and color that may be used to address the classification and...

The medical diagnosis of brain tumors is challenging due to the intricate and complex nature of these tumors. The development of this research antecedents precision tumor classification and segmentation through the integration of transfer learning, EfficientNet, and YOLO. We proposed the EfficientNet-B0...

The internet plays a crucial role in ensuring public safety today. The U.S. Knowledge People group ranks digital threats alongside terrorism and other significant challenges. Securing a nation has become increasingly difficult, given the vast amount of information online, including misinformation, which...

The increasing reliance on Unified Payments Interface (UPI) for digital transactions has significantly transformed the financial landscape, making digital payments seamless and accessible. However, this widespread adoption has also led to an escalation in fraudulent activities, including phishing, social...

Over the last few years, the demand for effective and precise malware and intrusion detection systems has been on the rise because of mounting cybersecurity threats. Conventional signature-based methods tend to face a high false positive rate, but machine learning models offer a robust alternative through...

Phishing attacks exploit users’ trust to steal sensitive information through fraudulent websites that mimic legitimate platforms, leading to identity theft, financial fraud, and unautho- rized data access. Traditional detection methods, such as black- lists and heuristic-based approaches, struggle against...

Agriculture is a critical industry that ensures food security and economic development. Farmers, however, usually face challenges in choosing the appropriate crops and yield estimation because of differences in soil characteristics and land availability. This project proposes a Crop Recommender System...

Ransomware performs file encryption to make systems unable to use their contents as well as bypass traditional antivirus programs. Most present detection systems track system calls along with processes and file activities on compromised systems before conducting data analysis. The monitoring of multiple...

This study presents an AI-driven approach for evaluating fruit freshness by utilizing the MobileNetV2 model. The method aims to replace conventional manual inspections with an automated system for distinguishing between fresh and spoiled fruits. Through transfer learning techniques, the model was trained...

The traditional methods used by herbal medicine stakeholders to discover therapeutic plants have significant limitations. Herb collectors often depend on subjective and inconsistent knowledge passed down through generations, and researchers have a hard time cataloging and studying medicinal plant species...

In the context of Industry 4.0, AI is transforming smart manufacturing by allowing for accurate and efficient predictive maintenance strategies. This paper aims to apply AI method to vibration analysis in manufacturing systems, using an existing dataset from previous research. The dataset, which included...

This project addresses the pressing challenge of the current world which is detecting the phishing websites and avoiding those websites to keep us safe. Phishing is a prevalent threat that endangers digital security. To deal with the evolving nature of these attacks and the limitations of traditional...

Heart disease continues to be among the leading causes of disease and mortality globally, and thus early diagnosis and risk stratification are critical. Conventional diagnostic methods tend to rely on clinical assessment and invasive procedures, which can lead to delays in prompt medical intervention....

The primary objective of this project is to improve speech emotion recognition (SER) development with HuBERT intended to draw out the meaning of the spoken content and with Prosody2Vec focused on the disentanglement of prosodic feature because of the embedding space learned from Hu- BERT, semantics of...

Driver drowsiness is one of the major causes of traffic accidents worldwide. Detecting fatigue early is essential for preventing accidents. In this study, electroencephalography (EEG) is used to capture brain signals and a suite of machine learning classifiers—including LightGBM, XGBoost, Extra Trees,...

Timely diagnosis of ocular diseases is essential for preventing vision loss and ensuring effective treatment. This research proposes an autonomous deep learning-based system for detecting common eye diseases from retinal fundus images. The study utilizes the publicly available Ocular Disease Recognition...

Facial Emotion Recognition (FER) plays a crucial role in applications such as human-computer interaction, affective computing, and mental health monitoring. This paper proposes a novel methodology that combines EfficientNet-based feature extraction, Self-Attention-based feature refinement, and Support...

The integration of machine learning and deep learning has significantly advanced personalized medicine, particularly in drug recommendation systems. This study presents a system that combines deep learning and ensemble learning techniques to generate tailored drug suggestions based on user-reported symptoms....

In today’s digital music platforms, recommendation algorithms are primarily static, relying on historical user data and past listening habits to suggest content. These systems often fail to address users’ dynamic emotional needs, leading to a disconnect between the recommended music and the user’s current...

This paper is about a cutting-edge human-computer interface system that enables precise mathematical computations via hand gestures. This innovative technology provides a simple substitute for common input modalities like keyboardsand touchscreens, promoting accessibility, particularly for those with...

Student placements are now a crucial component in assessing the efficacy of educational institutions due to the heightened competition in the market. The dynamic nature of employment patterns, individual student talents, and industry expectations are frequently overlooked by traditional placement prediction...

Cardiovascular disease remains a leading global health concern. Electrocardiograms (ECGs) are widely used for heart disease detection, but manual interpretation is time-consuming and prone to error. This study compares two deep learning models—Convolutional Neural Networks (CNN) and MobileNet—for classifying...

Social networks include a large number of social members who cooperatively forward messages. However, spammers publish links to the virus and view or follow a large number of users, creating many misleading news on mobile social networks. In this article, we propose an adaptive model for social spammer...

Denial of Service attacks is a substantial threat to the network security, as they overwhelm system resources and disrupt essential services. This study investigates DoS attack detection using a hybrid LSTM SVM machine learning approach, integrating Support Vector Machine and Long Short Term Memory networks....

Blood donation is a critical aspect of health care, yet donor availability, timely supply of blood, and demand prediction continue to be problems. This paper presents a machine learning blood donation application to enhance donor-recipient interaction, predict demand for blood, and maximize blood bank...

A brand-new supervised machine learning method is created to categorise network fraud applications as either benign or malevolent. It has been discovered that a combination of feature selection and supervised learning algorithms should be employed to determine the optimal model when taking the detection...

Digital content in regional languages like Marathi is increasing every second on various online platforms, making it difficult to categorize manually. Marathi, a morphologically rich language, presents unique challenges for automated text classification. This study examines the performance of Support...

Hand gesture-based sign language recognition distributed as major interaction link to people with hearing difficulties. The absence of a universal sign language allows distinct linguistic variations are evident in different regions, includes Indian Sign Language and American Sign Language. Conventional...

Sign Language Recognition (SLR) is essential for facilitating communication in the hearing-impaired community. However, existing SLR systems primarily focus on recognizing isolated words, lacking the ability to generate grammatically structured sentences, which is crucial for natural communication. Additionally,...

Financial decision-making is challenging because of large volumes of market data. Users find it difficult to interpret trends, so a tool that simplifies analysis and provides predictive insights is critical to making well-informed decisions. Existing tools such as Bloomberg Terminal and Trading View...

The purpose of the Smartphone Recommendation System (SRS) is to create personalized smartphone suggestionsusing Machine Learning (ML) approaches. The system utilizes a hybrid recommendation strategy based on Content-Based Filtering (CBF), recommending devices by identifying similarities based on documented...

Home cooking supports a healthy lifestyle, but selecting what to cook using accessible ingredients is problematic. The AI-driven Smart Recipe Generator resolves this by leveraging Machine Learning (ML), Computer Vision, and Natural Language Processing (NLP) to offer tailored recipes. Users can either...

This paper introduces a new hybrid system for food image recognition and nutrition facts retrieval. A baseline Convolutional Neural Network (CNN) started with 25% classification accuracy. For a significant gain in performance, the system combined Mo-bileNetV2 and transfer learning, and the accuracy was...

This paper presents a novel AI-driven framework for PDF translation that ensures accuracy, structural preservation, and security throughout the document processing pipeline. The system integrates advanced techniques such as deep learning-based watermark removal (achieving 92.5% detection accuracy), BERT-powered...

Diabetic Retinopathy (DR) is the most common cause of blindness and visual impairment in diabetic patients, primarily due to delayed diagnosis and inadequate screening. The diagnosis is currently human interpretation of fundus retinal images by ophthalmologists, which is time-consuming, labor-intensive,...

Mental health has recently become a crucial area of concern across various fields, gaining significant attention due to the rising number of individuals suffering from mental disorders, especially anxiety. A large portion of these individuals includes university students, who face immense academic and...

Loan approval forecasting is a key application of machine learning in the financial sector, supporting banks and financial institutions assessing the creditworthiness of users. This study presents details such as a machine-based approach to predict loan amounts based on applicants, income, credit history,...

Ayurvedic classical medicine depends, to a greater extent, upon the accurate identification, health appraisal and ripeness estimation of medicinal leaves. Yet, all such classification through manual processes involves expertise-based discrimination, takes lot of time, and hence contributes to variations...

In an era of increasingly complex food production, diversified preferences, and dietary restrictions, consumers want accurate information on food classification to help them make informed food decisions. The paper presents an all-rounded system using machine learning to classify foods into vegetarian,...

Parkinson’s Disease (PD) is a progressive neurodegenerative condition with motor impairment and vocal deficiency. This work proposes a machine learning-based non-invasive early diagnosis system of PD from speech recordings and handwriting samples. MFCCs, jitter, shimmer, and pitch features were obtained...

Research has shown that social media platforms on the internet such as Twitter, Facebook, Youtube, Reddit, blogging platforms have become a breeding ground for terrorist organisations to spread their beliefs and agenda, and recruit members by reaching audiences far beyond the country borders. Concerns...

Personalized learning is a critical need in today’s dynamic educational landscape, where learners require tailored pathways to develop skills effectively. This paper presents a web-based platform designed to assess users’ current skills and recommend courses aligned with their interests and career aspirations....

The traditional voting process face challenges like long queues, manual identity verification, and human error, reducing accessibility and voter turnout. To address these issues, a Facial Recognition-based Smart Voting System is proposed, using advanced machine learning. The system employs Python’s OpenCV...

Detecting and recognizing ships at sea presents unique challenges due to their irregular shapes, complex features, and varying sizes. This study explores the capabilities of different YOLO(YouOnlyLookOnce) architectures—specifically versions V5, V6, V7, and V8—for maritime applications. Each version...

Ransomware poses major threats to cybersecurity, disrupting networks, applications, and data centers across various sectors. Traditional defenses fail against sophisticated attacks, necessitating advanced solutions. We propose a feature selection-based framework using deep learning to enhance ransomware...

In this artificial intelligence era, health care plays vigorous role in day today life. The disease caused to the cattle’s are pink eye, new forest disease are caused by infectious Bovine Keratoconjuctivitis (IBK) and cancer eye are caused by Ocular Squamous Cell Carcinoma (OSCC) disease which are often...

The rapid evolution of wireless communication technology has led to the emergence of sixth-generation (6G) networks, which require highly efficient Multiple-Input Multiple-Output (MIMO) systems. Precoding is a basic technique to improve spectral efficiency and mitigate interference in MIMO systems and...

Millions of people around the world depend on rice as their main crop. And the quality of the grain has a great influence on its market value and social acceptance. Traditional methods of counting and classifying rice grains are time-consuming and manual. This results in inconsistent and inaccurate final...

Brain tumors are deadly and early diagnosis is crucial for successful treatment. Traditionally, radiologists rely on their experience to analyze brain scans, but this can be time-consuming and error-prone, especially with a growing number of patients. This paper presents a fast and efficient deep learning-based...

This paper explores the development and implementation of a face recognition-based attendance system using group photos. The system leverages advanced AI algorithms and deep learning techniques, such as convolutional neural networks (CNNs) and facial landmark detection and Face Recognition, to accurately...

Agriculture affects people’s lives and financial standing. It provides a significant portion of the GDP and has a large workforce. Crop infections are one of the main issues contributing to farmers’ current crop production losses, which can be attributed to a variety of factors. Naturally occurring diseases...

A number of disciplines rely heavily on satellite images, including those dealing with land use analysis, urban planning, agricultural monitoring, and the detection of environmental change. Nevertheless, because of differences in scale, illumination, and weather, distinguishing land features from satellite...

Rice is one of the most widely farmed grain crops and a key food source in India. According to FAO, 50% more food will be required by 2050 to support the increasing global population. For the country, rice exports contribute to India’s foreign exchange earnings. Disease damage to rice can significantly...