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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Sathiyaprasad, B. Reddy, Arva Hari Vardhan Abdurrahman, Usama Micheal, A. Annie Department of Computer Science and Engineering Sathyabama institute of science and technology Chennai India

ISBN: (纸本)9798331522667

Advances in immunological research are essential for elucidating immune responses and developing targeted therapeutic approaches. This study proposes an automated method for immune cell classification leveraging machine learning and image processing techniques. A novel framework is introduced, combining high-resolution imaging with computational algorithms to accurately classify various immune cell types from biological samples. The methodology incorporated preprocessing steps, like normalization and noise reduction. Then, deep learning models, such as CNNs and SVMs, will be applied for feature extraction and classification. These models were trained on annotated datasets derived from flow cytometry and microscopy, demonstrating better accuracy and reliability. Results demonstrate an improvement in processing speed and accuracy of classification over traditional approaches, which underlines the importance of automation in immunological diagnostics. The data obtained can be used in high-throughput immunotherapy and disease diagnostic screening;it allows for the identification of immune cell populations within a short period and therefore improves research efficiency. The work provides a basis for real-time analysis and incorporation of multiscale datasets that will help to better understand immunity and its associated disorders. © 2025 IEEE.

关键词： Convolutional neural networks

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Synergistic Fusion of Steganography and Cryptography in Medical Imaging: A Comparative Analysis to Enhance Image Security 1

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Ghosh, Sharmila Saha, Ashim Pal, Tannistha Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (数字)9789819752003

ISBN: (纸本)9789819751990

Image steganography is a technique for encrypting data, allowing covert communication, and enhancing data security inside image files. This study provides an in-depth analysis of image steganography, covering its core ideas, methods, uses, difficulties, and developments. Numerous embedding algorithms and methodologies, including transform domain methods and LSB-based strategies, are examined in this study. The paper highlights the need for effective defenses against steganalysis attacks, while also discussing the challenges and potential weaknesses in steganography. It examines common steganography techniques and discusses countermeasures such as adaptive embedding, key-based embedding, and algorithmic resistance. Additionally, the paper also highlights the significance of assessing the efficacy and caliber of image steganography techniques. Objective metrics such as PSNR and MSE are discussed, providing insights into the trade-offs between security and image fidelity. For those interested in understanding image steganography, its applications, and its challenges, this study offers a complete resource. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Medical imaging

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Detecting Inattentive and Aggressive Driver Behavior Using Deep Learning: Recent Advances, Challenges with Performance Evaluation 1

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4th International Conference on Smart IoT Systems: Innovations and Computing, SSIC 2023

作者： Debbarma, Tina Pal, Tannistha Debbarma, Nikhil Department of Computer Science and Engineering National Institute of Technology Agartala Agartala India

ISBN: (数字)9789819752003

ISBN: (纸本)9789819751990

Road safety faces a grave threat from the prevalence of aggressive and inattentive driving behaviors, contributing significantly to the global rise in traffic accidents and fatalities. Identifying, tracking, and proactively addressing instances of aggressive and inattentive driving are pivotal steps toward mitigating accidents and enhancing road safety. This research project seeks to explore the potential of deep learning methods for analyzing human driving behavior, with a specific focus on identifying inattentive and aggressive driving behaviors. The ultimate goal is to develop a robust and reliable system capable of accurately recognizing and categorizing such behaviors. To achieve this objective, we harness the power of deep learning algorithm. This paper also discusses the identification and categorization of aggressive and distracted driving behaviors among human drivers, using deep learning methodologies which offer substantial promise for elevating traffic safety. The literature review uncovered several noteworthy techniques, including CNN, MTCNN, transformer-based action detection systems, and fusion models inspired by densely connected convolution networks. Comparative analysis revealed improvements in accuracy and precision through the utilization of these techniques. However, the pursuit of larger and more diverse datasets, real-time implementation capabilities, and robustness in various environmental conditions remain essential requirements. Despite these challenges, deep learning-based systems offer the potential for automatic and precise analysis of intricate driving patterns, pivotal in accident prevention and overall road safety enhancement. Future research should focus on surmounting current limitations and advancing the development of more intricate deep learning models for accurate recognition and categorization of human driving behaviors. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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Automated Brain Tumor Detection and Classification in MRI Scans using a Deep Convolutional Neural Network and U-Net Architecture 6

Automated Brain Tumor Detection and Classification in MRI Sc...

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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Devadas, Prathima Mathivanan, G. Department of Computer science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (纸本)9798331522667

Brain tumor identification and categorization serve a precarious role in early diagnosis and treatment planning. This study suggests a new deep learning (DL)-based structure for the automatic detection and categorization of brain cancers using medical imaging technologies. The framework uses a deep convolutional neural network (DCNN) with an exclusive segmentation technique, notably the U-Net architecture, to discrete tumor areas in magnetic resonance imaging (MRI) scans. The DCNN model, which contains multiple convolutional layers, is used for feature extraction, followed by fully connected layers for tumor classification. To increase segmentation accuracy, the U-Net model integrates both encoder-decoder structures and skip connections to capture fine-grained spatial details. The suggested algorithm is trained and evaluated on a large dataset of brain MRI images, achieving high accuracy, sensitivity, and specificity for both tumor detection and classification. Experimental results show that the model outclasses standard methods, providing a dependable and operative solution for the early detection and diagnosis of brain cancers, potentially aiding clinical decision-making processes. © 2025 IEEE.

关键词： Magnetic resonance imaging

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A Review on Oversampling Techniques for the Treatment of Class Imbalance Problem 3

A Review on Oversampling Techniques for the Treatment of Cla...

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3rd International Conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Anand, Apurv Biswas, Saroj Kr. Majumdar, Sounak Thounaojam, Dalton Meitei Department of Computer Science and Engineering National Institute of Technology Silchar Silchar India

ISBN: (纸本)9798331527549

The Class Imbalance Problem (CIP) is a critical challenge in machine learning, particularly in applications such as medical diagnosis and fraud detection, where minority classes are underrepresented but crucial. This research addresses the issue by introducing a novel classification of oversampling techniques into 11 distinct categories, with a unique distinction between duplication and derivation mechanisms. Through an in-depth review of each category, this study identifies specific challenges such as overfitting and noise in imbalanced datasets and demonstrates how these categorized strategies effectively enhance model performance. The outcomes highlight practical solutions to improving classification accuracy and robustness in imbalanced scenarios. © 2025 IEEE.

关键词： Contrastive Learning

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Analyzing the Impact of Demographic and Operational Variables on 1-to-Many Face ID Search

IEEE Transactions on Technology and Society

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IEEE Transactions on technology and Society 2024年第2期5卷 217-230页

作者： Pangelinan, Gabriella Bhatta, Aman Wu, Haiyu King, Michael C. Bowyer, Kevin W. Florida Institute of Technology Department of Electrical Engineering and Computer Science MelbourneFL32901 United States University of Notre Dame Department of Computer Science and Engineering Notre DameIN46556 United States

Concerns related to the proliferation of automated face recognition technology with the intent of solving or preventing crimes continue to mount. The technology being implicated in wrongful arrest following 1-to-many face identification has heightened scrutiny in the media and spawned lawsuits. We analyze the accuracy of 1-to-many face identification across African-American and Caucasian demographic groups and in the presence of blur and reduced resolution in the probe image, as might occur in "surveillance camera quality"images. Because of the high accuracy of modern face recognition algorithms with "government ID quality"images and the size of available datasets, we use the following indirect metrics to assess the relative propensity for false positive identifications: (1) the traditional d-prime statistic between mated and non-mated score distributions, (2) absolute score difference between thresholds in the high-similarity tail of the non-mated distribution and the low-similarity tail of the mated distribution, and (3) distribution of (mated - non-mated rank-one scores) across the set of probe images. We find that demographic variation patterns in 1-to-many accuracy largely, but not perfectly, follow that observed in 1-to-1 accuracy. We show that, different from the case with 1-to-1 matching accuracy, demographic comparison of 1-to-many accuracy can be affected by different numbers of identities and images across demographics. We also show that increased blur or reduced resolution of the face in the probe image can significantly increase the false positive identification rate. This point is important because the reputation of modern face recognition algorithms for high accuracy in 1-to-many face identification comes from statistics based on analyzing "government ID quality"rather than "surveillance camera quality"images. © 2024 IEEE.

关键词： Face recognition

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Enhancing IoT ECO System - Smart Parking System

Enhancing IoT ECO System - Smart Parking System

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Avinash, S. Muthukrishnan, S. Aishwarya, R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331529833

The Smart Parking System is an innovative solution designed to tackle the challenges of parking in densely populated urban areas. By integrating advanced technologies such as RFID (Radio Frequency Identification) sensors, automated gate systems, and proximity sensors, the system delivers a streamlined and efficient parking experience. RFID technology automates entry and exit processes by recognizing vehicles equipped with RFID tags, reducing wait times and enhancing security through controlled access. Proximity sensors at each parking spot monitor occupancy in real-time, feeding data to a central management system that directs drivers to available spaces via digital signage or a mobile app. This real-time guidance decreases search time, reduces congestion, and improves overall parking organization. Designed with scalability and future-readiness, the system's central management unit can integrate additional features like payment processing, reservation systems, and predictive analytics powered by machine learning. This adaptability ensures the system remains relevant as technology advances and user needs evolve. The Smart Parking System also promotes sustainability by minimizing vehicle idling and carbon emissions, contributing to a reduced environmental impact. For users, the system offers a more convenient, secure, and efficient parking experience. For operators, it enhances operational efficiency, reduces manual labor, and provides valuable insights into parking patterns, aiding in pricing, expansion, and maintenance decisions. The system's modular design allows for cost-effective implementation, making it suitable for various facilities from small lots to large complexes. In summary, the Smart Parking System represents a forward-thinking approach to urban parking, combining technology, efficiency, and sustainability to improve urban living. © 2025 IEEE.

关键词： Proximity sensors

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Early Heart Failure Recognition for Infants using Machine Learning 5

Early Heart Failure Recognition for Infants using Machine Le...

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5th International Conference on Smart Electronics and Communication, ICOSEC 2024

作者： Indumathi, N. Meera, J. Palani Upadhyay, Shrikant Pithamber, Khobragade Kumar, Manoj Gupta, Jaina Sumith Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Sathyabama Instittue of Science and Technology Department of Computer Science and Engineering Chennai India Mlr Institue of Technology Department of Electronics and Communiction Engineering Hyderabad India Mlr Institute of Technology Department of Computer Electronics and Communication Engineering Hyderabad India

ISBN: (纸本)9798331504403

Caring of newborn babies is one of major concern for the doctors and to take care of his/her life is very challenging and sometime critical. Cardiac attack is one the severe issue that require immediate medical support to the new born baby. Providing some shocks using some device is one the immediate treatment to save the life of babies. The computation and prediction of cardiovascular status is one of the challenging tasks. In this paper we try to analyze the cardia prior heart failure chances with the different levels using machine learning methodology. The target rate of cardiac arrest detected is 58.78% and level of no cardiac found is 46.67% is achieved and this prediction level which help to recognize the level of cardiac attack for new born child. Here, we also analyze the different parameters like false rate, false observation, stability rate etc., that will help for the proper analysis of the collected data with a stability rate of 1.67% and precision rate of 60.039%. This result may provide the healthcare professional to handle the different level of heart failure for new born child. © 2024 IEEE.

关键词： Machine learning

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An empirical evaluation of teaching–learning-based optimization, genetic algorithm and particle swarm optimization

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International Journal of computers and Applications 2023年第1期45卷 36-50页

作者： Shukla, Alok Kumar Pippal, Sanjeev Kumar Chauhan, Sansar Singh Department of Computer Science and Engineering G.L. Bajaj Institute of Technology & Management Greater Noida India Department of Computer Science & Engineering Galgotias University Greater Noida India

Metaheuristic algorithms have probable to solve global optimization problems in various fields of engineering and industry. To find a global solution by exploring irregular or non-linear surfaces, classical optimization techniques are not able. To overcome the limitation of the classical approach, in the recent study, a large number of metaheuristic methods have been investigated to improve the solution quality concerning convergence and accuracy on the complex problems. Nowadays, a popular metaheuristic algorithm is introduced called teaching–learning-based optimization (TLBO). It is recently being used as an innovative, and robust method to solve the global optimization problem, inspired by the teaching–learning phenomenon. On the other hand, particle swarm optimization (PSO) algorithm is one of the most utilized algorithms in the current scenario, which has indicated acceptable efficiency. Genetic algorithm (GA) is a valuable component of metaheuristic which has been applied in various research applications. In this article, the performance of TLBO algorithm is estimated on 25 numerical test suites against other metaheuristic algorithms such as PSO and GA. The results of our experimental study show that TLBO outperforms the PSO and GA algorithm regarding convergence solution. © 2019 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Genetic algorithms

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Data-Driven Cloud Based Soil Nutrient Prediction System for Precision Agriculture

Data-Driven Cloud Based Soil Nutrient Prediction System for ...

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2024 IEEE International Conference on Modeling, Simulation and Intelligent Computing, MoSICom 2024

作者： Mishra, Amit Dass, Jeevesh Saxena, Siddhant Prakash, Ishan Satya Sargun Garg, Arshia Verma, Karun Bhattacharya, Jhilik Thapar Institute of Engineering & Technology Department of Electronics and Communication Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Computer Science Engineering Punjab Patiala India Thapar Institute of Engineering & Technology Department of Electronics and Computer Engineering Punjab Patiala India

ISBN: (纸本)9798331533311

Decreasing arable acreage and a growing world population are pushing for new farming systems. Achieving high crop production requires maintaining a balanced nutrient level in the soil. This study offers a machine learning-based model that will predict the NPK fertilizer to achieve the higher crop yields. Field sensor data along with meteorological data and remote sensing data from various test fields are integrated. A random forest and decision tree machine learning model are applied to the integrated data to develop a software tool for precision agriculture, enabling accurate estimation of nutrient requirements. A mobile application is used to share the nutrient prediction with farmers, enabling them to make well-informed choices regarding resource utilization efficiency and environmental friendliness. This research can transform agricultural practices globally and contribute towards food security on earth. © 2024 IEEE.

关键词： Smart agriculture

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