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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Reddy, Manideep Raya V S R S Kumar, Sunil Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Patanjali CPS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

Breast cancer remains a leading cause of mortality among women worldwide, where early detection significantly improves survival rates. Traditional diagnostic methods like mammography, biopsy, and ultrasonography face challenges like diagnostic errors and low sensitivity. Recent advancements in Artificial Intelligence (AI), including deep learning for image analysis and natural language processing for patient data interpretation, have shown promise in enhancing diagnostic capabilities. The integration of these AI techniques with Quantum Machine Learning (QML) leverages quantum parallelism to process high-dimensional medical data and extract intricate imaging patterns more efficiently. This paper provides a comprehensive overview of cancer, its subtypes, symptoms, and the limitations of conventional diagnostics while highlighting the transformative potential of QML in improving diagnostic accuracy and efficiency for breast cancer detection and prognosis. © 2025 IEEE.

关键词： Biopsy

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Performance Analysis of Hybrid MPI and OpenMP on Smith-Waterman Algorithm 3

Performance Analysis of Hybrid MPI and OpenMP on Smith-Water...

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Ninama, Keyur Patel, Jainamkumar Girish, K.K. Reddy, Manideep Raya V S R S Bhowmik, Biswajit National Institute of Technology Karnataka Maharshi Sushrut CAS Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Ishwarchandra Vidyasagar AIT Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka Maharshi Kanad QC Lab BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India National Institute of Technology Karnataka BRICS Laboratory Dept. of Computer Science and Engineering Mangalore India

ISBN: (纸本)9798331523893

In the rapidly advancing field of bioinformatics, sequence alignment is a pivotal task for elucidating genetic statistics and evolutionary relationships. As the volume and complexity of biological data continue to grow, it becomes imperative to employ effective computational techniques to manage this expansion. The Smith-Waterman algorithm is a key tool for sequence alignment;however, its performance can be constrained by the substantial size of contemporary datasets. To overcome this limitation, this paper explores a hybrid parallelization strategy that integrates message passing interface (MPI) with open multi-processing (OpenMP). This approach aims to significantly enhance the algorithm's efficiency by leveraging the strengths of both parallelization models. By optimizing the scalability and execution speed of the Smith-Waterman algorithm on advanced high-performance computing (HPC) systems, the hybrid technique not only improves performance but also enables more rapid and accurate biological data analysis. © 2025 IEEE.

关键词： Open Data

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Evaluation of Machine Learning Models in Student Academic Performance Prediction 5

Evaluation of Machine Learning Models in Student Academic Pe...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Sandeepa, A.G.R. Mohottala, Sanka General Sir John Kotelawala Defence University Dept. of Computer Engineering Ratmalana Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Science Malabe Sri Lanka

ISBN: (纸本)9798331530983

This research investigates the use of machine learning methods to forecast students' academic performance in a school setting. Students' data with behavioral, academic, and demographic details were used in implementations with standard classical machine learning models including multi-layer perceptron classifier (MLPC). MLPC obtained 86.46% maximum accuracy for test set across all implementations while for train set, it was 99.45%. Under 10-fold cross validation, MLPC obtained 79.58% average accuracy for test set while for train set, it was 99.65%. MLP's better performance over other machine learning models strongly suggest the potential use of neural networks as data-efficient models. Feature selection approach played a crucial role in improving the performance and multiple evaluation approaches were used in order to compare with existing literature. Explainable machine learning methods were utilized to demystify the black box models and to validate the feature selection approach. © 2025 IEEE.

关键词： Students

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Emotion Classification in Text Using Machine Learning and LSTM

Emotion Classification in Text Using Machine Learning and LS...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Kumar, B. P. Pradeep Ravi Kumar, J. Biswas, Arpita Hafeeza, Bi Bi Dhaanya, D. Visvesvaraya Technological University Department of Computer Science Design Atria Institute of Technology Karnataka Bangalore India Visvesvaraya Technological University Dept. of Computer Science Engineeering Atria Institute of Technology Karnataka Bangalore India

ISBN: (纸本)9798331538538

Emotion categorization is used in domains like customer service, social media content moderation, and interaction between humans and computers. Classification of emotion from text is a fundamental issue in NLP, or natural language processing. This study examines both advanced deep learning models (LSTM networks with embedding layers) and traditional machine learning techniques (Naive Bayes, Logistic Regression, SVM, Random Forest) for the classification of the six primary emotions: joy, sorrow, anger, fear, love, and surprise. The study uses a preprocessed emotion-tagged text dataset, and metrics including accuracy, precision, recall, and F1-score are utilized to evaluate the models. LSTMs perform better than conventional models, which find it difficult to grasp language subtleties and contextual correlations, by processing sequential data and contextual information with ease. © 2025 IEEE.

关键词： Emotion Recognition

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Cross-Domain Adaptation Through Adversarial Teaching for Unmanned Ariel Vehicles in Smart Cities 22

Cross-Domain Adaptation Through Adversarial Teaching for Unm...

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22nd International Learning and technology Conference, L and T 2025

作者： Mazhar Ul Haq, Qazi Yao, Leethar Lin, Bo-Yu Yuan Ze University Dept. of IBPI and Computer Science and Engineering Zhongli Taiwan National Taipei University of Technology Dept. of Electrical Engineering Taipei Taiwan

ISBN: (纸本)9798331532215

Person re-identification has been an important issue of surveillance systems in smart cities. However, this requires huge datasets to supervise deep learning models for accurately identifying and tracking people in smart cities. In this paper, we propose adversarial learning in integration with a mutual mean teaching model known as adversarial teaching to take the issue of domain adaptation. Initially, they learn a source dataset in a supervised way. This model information is then transferred to another model through unsupervised learning and pseudo-labels are generated by generative adversarial learning. The multi-class invariance is handled by adversarial learning. The performance of the proposed model is evaluated on two different datasets source and target datasets. Multiple experiments on Duke and Market datasets indicate higher accuracy of F-1 score on both datasets compared to previous methods, demonstrating the superior performance of the proposed method. © 2025 IEEE.

关键词： Adversarial machine learning

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Music Recommendation System Based on Facial Emotion Detection Using Spotify API

Music Recommendation System Based on Facial Emotion Detectio...

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2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025

作者： Neeli, Jyoti Shetty S, Vijaya Rao, Shreya Srujana, S.N. Narayan, Nidhi Nitte Meenakshi Institute of Technology Dept of Computer Science and Engineering Bengaluru India Bms Institute of Technology & Management Bengaluru India

ISBN: (纸本)9798331538538

In the world of entertainment, music holds considerable importance, especially for those who find joy in rhythmic experiences. Despite the abundance of streaming platforms that enable access to favorite songs, they often fall short in capturing the intricate emotional nuances of users. This research recognizes a spectrum of emotions, including fear, happiness, sadness, anger, and neutrality. Its goal is to enrich the user experience by developing a recommendation system that proposes songs based on the user's current emotional state. The emotion-driven recommendation engine has seamlessly integrated into Spotify, a well-known music streaming service, providing users with a smooth and individualized journey in exploring music. The system aims to simplify the user experience by eliminating the necessity for manual song searches and, instead, intuitively suggests tracks that resonate with the user's emotions. The Spotify API serves as a crucial tool for accessing curated playlists, enabling the retrieval of desired music from thoughtfully organized collections centered around specific themes or titles. © 2025 IEEE.

关键词： Visualization

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Multispectral Images and IoT Based Tea Plantation Monitoring: A Proposed System Architecture 5

Multispectral Images and IoT Based Tea Plantation Monitoring...

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5th International Conference on Advanced Research in Computing, ICARC 2025

作者： Kariyawasam, K.P.W.D.V. Kahandagamage, P.N. Fernando, M.R.R. Fernandopulle, J.M. Jayasinghearachchi, Vishan Dissanayaka, Kapila Dept. of Software Engineering Sri Lanka Dept. of Information Technology Sri Lanka Dept. of Computer Science Sri Lanka

ISBN: (纸本)9798331530983

In recent years, the Sri Lankan tea industry has fallen behind its competitors in the global tea market. This decline is caused by the challenges in productivity and resource management due to the limitations of traditional crop monitoring methods. This study presents a prototype system architecture that integrates multispectral imagery and IoT technologies to enhance plantation monitoring. The proposed system uses drones to capture high-resolution multispectral images and IoT devices to collect real-time environmental data, providing a comprehensive approach to assessing plant yield and detecting stress. Built on a hexagonal architecture, the system emphasizes modularity, reliability, and scalability by ensuring a clear separation of core functionalities from external components. This design facilitates easy adaptation to various crop types and agricultural contexts, enabling flexibility in deployment and use. This flexible framework can serve as a blueprint for improving decision-making and management practices across diverse plantation environments. © 2025 IEEE.

关键词： Risk perception

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Theta-λ Optimization for Network Performance in Rician Fading and Multi-User Interference Scenarios 4

Theta-λ Optimization for Network Performance in Rician Fadi...

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4th International Conference on Power, Control and Computing Technologies, ICPC2T 2025

作者： Upadhyay, Deepak Sharma, Kunj Bihari Dhondiyal, Shiv Ashish Venu, Nookala Dept. of Computer Science and Engineering Graphic Era Hill University Dehradun India Dept. of Computer Science and Engineering Graphic Era Deemed to be University Dehradun India Centre for Internet of Things Madhav Institute of Technology & Science Deemed University Gwalior India

ISBN: (纸本)9798331530402

In this paper, an investigation into the application of Theta-λ optimization strategies to improve network performance is made in a challenging wireless communication systems environment subject both Rician fading and multi-user interference (MUI). This paper discusses the dynamic adjustments of Theta (angle-of-arrival) and λ parameters necessary to balance system resiliency against high throughput. We did some simulations to test the performance in terms of latency, throughput as well as accuracy using values such frequency stability and jitter. The results show strong enhancements after the optimization - latency: from a mean of 20 ms to stabilizing at 15ms, throughput: dramatically enhanced from existing baseline of around ~50 to 100 Mbps with enhancement between ~70-120Mbps while maintaining range and jitter reduction halved as average jitter is now less than two milliseconds compared previous Jitter at an optimal value. This had the added benefit of improvements in frequency stability: now, instead of hovering around 2.4 GHz like when we first tested it out-of-the-box, average was up to 2.5 GHz after optimization as well! The results of this study illustrate the potential for enhanced network reliability and availability through fading and interferer mitigation using Theta-λ optimization. This case study is representative of the relevance for real-time adaptive optimization techniques in current-day telecommunications given surging demands on resilient wireless connectivity. Further research directions include enhancement of the optimization algorithms depending on specific network conditions and integration with adaptive strategies for real-time delivery using QoE-oriented mechanisms. © 2025 IEEE.

关键词： Fading MUI Rician Theta-λ Wireless

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VisionAid Band: Real-Time Smart Navigational and Alert Support System for Visually Impaired Persons Using Wearable technology 2

VisionAid Band: Real-Time Smart Navigational and Alert Suppo...

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2nd International Conference on Computational Intelligence, Communication technology and Networking, CICTN 2025

作者： Kumar, Kanak Verma, Anshul Verma, Pradeepika Dept. of Computer Science Banaras Hindu University Varanasi India Technology Innovation Hub Indian Institute of Technology Patna India

ISBN: (纸本)9798331530389

The"VisionAid Band" represents a groundbreaking advancement in assistive technology, designed to empower visually impaired individuals by enhancing their spatial awareness and mobility through an innovative, low-cost solution. Utilising an Arduino-UNO, ultrasonic sensor, and vibrator sensors to interpret the environment, translating physical obstacles into auditory or tactile feedback that guides users safely around their surroundings. The device's affordability and simplicity are pivotal in addressing the accessibility gap in current assistive technologies, which are often prohibitively expensive or complex. This paper details the development process of the VisionAid Band, from conceptualisation to prototype testing. It discusses the selection of the microcontroller platform, sensor integration, and the development of algorithms that efficiently process sensor data to provide real-time, intuitive feedback to the user. Emphasis is placed on the device's design considerations, including ergonomics, power consumption, and user interface, ensuring that the VisionAid Band is effective but also comfortable and user-friendly. Through a series of controlled experiments and user feedback sessions, the effectiveness of the VisionAid Band in improving navigational abilities among visually impaired participants is evaluated. Preliminary results indicate a significant enhancement in users' ability to navigate complex environments safely and confidently. The paper concludes with a discussion of potential improvements, future enhancements, and the broader implications of the VisionAid Band on the accessibility and independence of individuals with visual impairments. The VisionAid Band exemplifies how low-cost, technology-driven solutions can substantially impact the quality of life for people with disabilities. Its development underscores the potential of innovative engineering to address critical challenges in accessibility, paving the way for more inclusive technology solutions

关键词： Assistive technology

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Disaster Management Based on Biodiversity Conservation Using Remote Sensing Data Analysis Using Machine Learning Model

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Remote Sensing in Earth Systems sciences 2025年第1期8卷 191-199页

作者： Pokkuluri, Kiran Sree Mounika, Talla Devi, N. Durga Kishore, D. Ratna Balakiruthiga, B. Krishna, B. Murali Department of Computer Science and Engineering Shri Vishnu Engineering College for Women Telangana Bhimavaram India Department of CSE-DS Mallareddy College of Engineering Hyderabad India Department of Computer Science and Engineering Aditya University Andhra Pradesh Surampalem India Dept of Information Technology Lakireddy Balireddy College of Engineering Mylavram Vijayawada India Department of Networking and Communications College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering MLR Institute of Technology Hyderabad India

Artificial intelligence (AI) applications in forestry as well as wildlife domains have become more feasible due to the advancements in data science and digital and satellite technologies. However, there is a serious global threat to biodiversity due to the rapid expansion of urban, agricultural, and development initiatives. Therefore, the effective monitoring, management, and preservation of biodiversity as well as forest resources may be facilitated by integration of cutting-edge methods like AI in disciplines of forests as well as biodiversity. This study suggests a novel approach to disaster management based on biodiversity protection that makes use of machine learning models and remote sensing image analysis. In this case, the data for disaster management has been gathered and processed for normalisation and smoothing. The processed image features have been segmented using active contour Gaussian Q- histogram equalisation model and feature extracted using probabilistic Bayesian fuzzy transfer NN. Experimental analysis is carried out based on parameters like random accuracy, average precision, sensitivity, AUC, and normalized co-efficient. The proposed technique attained average precision was 95%, sensitivity was 97%, random accuracy was 98%, and normalized coefficient was 94%, AUC of 96%. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Biodiversity conservation Disaster management Machine learning model Q- histogram equalisation Remote sensing

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