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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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Water Distribution Network for Irrigation using Artificial Neural Network (ANN) Algorithm

Water Distribution Network for Irrigation using Artificial N...

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2025 International Conference on Visual Analytics and Data Visualization, ICVADV 2025

作者： Sankari, M. Viveha, R. Varshita, Y. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (数字)9798331521394

ISBN: (纸本)9798331521394

With agriculture being a significant consumer of freshwater resources, optimizing water distribution based on accurate soil moisture predictions can minimize water waste, preserving valuable resources for other needs and reducing the environmental footprint of farming. Proper irrigation management based on real-time soil moisture predictions ensures crops receive adequate water at critical growth stages, improving both yield and quality, which is essential for food security and economic viability. This project presents the development of an Artificial Neural Network (ANN) model for optimizing a water distribution network for irrigation. The ANN model predicts soil moisture levels based on geospatial data, soil properties, and temporal features, enabling efficient irrigation scheduling. The dataset used includes soil texture properties (clay, sand, and silt content), geospatial coordinates (latitude and longitude), auxiliary soil moisture measurements, and temporal features (month and day of the year). The model architecture includes multiple hidden layers with ReLU activation functions and a linear output layer to predict the target soil moisture content. The system aims to optimize water usage by accurately predicting soil moisture, contributing to more sustainable irrigation practices. © 2025 IEEE.

关键词： Soil moisture

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A QoS-aware routing approach for Internet of Things-enabled wireless sensor networks in smart cities

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Multimedia Tools and Applications 2025年第17期84卷 17951-17977页

作者： Karunkuzhali, D. Meenakshi, B. Lingam, Keerthi Department of Information Technology Panimalar Engineering College Chennai India Department of Electrical and Electronics Engineering Sri Sairam Engineering College Chennai India Department of Computer Science and Engineering Gitam University Visakhapatnam India

In the Internet of Things (IoT), optimizing machine performance through data analysis and improved connectivity is pivotal. Addressing the growing need for environmentally friendly IoT solutions, we focus on "green IoT." Quality in smart cities heavily relies on efficient data gathering and communication. In this study, we propose a novel software architecture tailored for data collection and communication in IoT-enabled smart applications, emphasizing sustainability from the outset. Specifically, for smart cities utilizing IoT-enabled wireless sensor networks, we introduce a Quality of Service (QoS)-aware routing strategy. In the data collection phase, we employ Chaotic Bird Swarm Optimization (CBSO) for creating IoT sensor clusters, accompanied by Improved Differential Search (IDS) to assess the reliability of individual sensor nodes, ultimately designating the most trusted node as the Cluster Head (CH). In the data transport phase, we implement lightweight signcryption to enhance the security of IoT sensor data. Furthermore, we employ an Optimal Data Routing (ODM) approach to compute the most efficient data transmission paths between source and destination nodes within the IoT network. Finally, the performance of our QoS-aware routing strategy is evaluated through network simulations using NS2 and compared against existing approaches, offering valuable insights into its effectiveness and suitability for smart city IoT applications. The main findings of the study indicate that the proposed QoS-aware routing strategy (QOS-AWARE ROUTING) consistently outperforms existing routing algorithms in terms of energy efficiency, network longevity, and latency, both in scenarios with varying sensor node density and over multiple cycles. Additionally, it demonstrates significant advantages, such as up to 90.8% less energy consumption, 66.8% longer network lifespan, and 80.1% reduced latency when compared to the benchmarked methods. © The Author(s), under exclusive licence to S

关键词： Internet of things

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Automated Immune Cell Classification using Image Processing Techniques 6

Automated Immune Cell Classification using Image Processing ...

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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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Distributed Population-Based Simultaneous Perturbation Stochastic Approximation for Fine-Tuning Large Language Models 21st

Distributed Population-Based Simultaneous Perturbation Stoch...

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21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024

作者： Tan, Yajing Huang, Yuwei Duan, Qiqi Yang, Yijun Shi, Yuhui Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China

ISBN: (纸本)9789819601219

Very recently, a memory-efficient version (called MeZO) of simultaneous perturbation stochastic approximation (SPSA), one well-established zeroth-order optimizer from the automatic control community, has shown competitive fine-tuning performance on large language models (LLMs) with billions of weights under the limited memory of a single GPU, which often hinders the direct usage of the standard backpropagation (BP) update. Furthermore, population-based evolutionary methods (such as evolution strategies from OpenAI and population-based training from DeepMind) have shown a promising direction to improving and scaling up various large-scale AI systems via efficiently utilizing increasingly available distributed computing resources, owing to their relatively simple yet efficient data and control flows. In this paper, we propose a population-based SPSA for fine-tuning LLMs with non-differentiable objective functions. Specifically, our proposed method combines the desirable exploration ability of distributed population-based evolutionary methods with the powerful exploitation ability of SPSA in the modern distributed computing environment wherein the efficient implementation of BP is still challenging because of its complex dependencies in forward-pass and backward-pass information flows. Simulation experiments on a set of challenging (non-differentiable) natural language process tasks empirically validate the potential of our method on fine-tuning even in a relatively small-scale distributed computing scenario. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Stochastic systems

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Fuzzy Decision-Based Clustering for Efficient Data Aggregation in Mobile UWSNs

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computers, Materials & Continua 2025年第4期83卷 259-279页

作者： Aadil Mushtaq Pandith Manni Kumar Naveen Kumar Nitin Goyal Sachin Ahuja Yonis Gulzar Rashi Rastogi Rupesh Gupta Department of Computer Science and Engineering Lovely Professional UniversityJalandhar144402PunjabIndia Department of Computer Science and Engineering Chandigarh UniversityMohali140413PunjabIndia Salesforce Inc. DallasTX 75201USA Department of Computer Science and Engineering School of Engineering and TechnologyCentral University of HaryanaMahendragarh123031HaryanaIndia Department of Management Information Systems College of Business AdministrationKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Computer Application Sir Chhotu Ram Institute of Engineering and TechnologyCh.Charan Singh UniversityMeerut250001Uttar PradeshIndia Chitkara University Institute of Engineering and Technology Chitkara UniversityRajpura140401PunjabIndia

Underwater wireless sensor networks(UWSNs)rely on data aggregation to streamline routing operations by merging information at intermediate nodes before transmitting it to the ***,many existing data aggregation techniques are designed exclusively for static networks and fail to reflect the dynamic nature of underwater ***,conventional multi-hop data gathering techniques often lead to energy depletion problems near the sink,commonly known as the energy hole ***,cluster-based aggregation methods face significant challenges such as cluster head(CH)failures and collisions within clusters that degrade overall network *** address these limitations,this paper introduces an innovative framework,the Cluster-based Data Aggregation using Fuzzy Decision Model(CDAFDM),tailored for mobile *** proposed method has four main phases:clustering,CH selection,data aggregation,and *** CH selection,a fuzzy decision model is utilized to ensure efficient cluster head selection based on parameters such as residual energy,distance to the sink,and data delivery likelihood,enhancing network stability and energy *** the aggregation phase,CHs transmit a single,consolidated set of non-redundant data to the base station(BS),thereby reducing data duplication and saving *** adapt to the changing network topology,the re-clustering phase periodically updates cluster formations and reselects *** results show that CDAFDM outperforms current protocols such as CAPTAIN(Collection Algorithm for underwater oPTical-AcoustIc sensor Networks),EDDG(Event-Driven Data Gathering),and DCBMEC(Data Collection Based on Mobile Edge Computing)with a packet delivery ratio increase of up to 4%,an energy consumption reduction of 18%,and a data collection latency reduction of 52%.These findings highlight the framework’s potential for reliable and energy-efficient data aggregation mobile UWSNs.

关键词： Clustering data aggregation data collection fuzzy model monitoring UWSN

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ComStreamClust: a Communicative Multi-Agent Approach to Text Clustering in Streaming Data

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Annals of Data science 2023年第6期10卷 1583-1605页

作者： Najafi, Ali Gholipour-Shilabin, Araz Dehkharghani, Rahim Mohammadpur-Fard, Ali Asgari-Chenaghlu, Meysam Department of Computer Science and Engineering Sabanci University Istanbul Turkey Department of Computer Engineering University of Tabriz Tabriz Iran Computer Engineering Department Isik University Istanbul Turkey Department of Computer Engineering Sharif University of Technology Tehran Iran

Topic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal with this phenomenon. In this paper, we propose a novel, multi-agent, communicative clustering approach, so-called ComStreamClust for clustering sub-topics inside a broader topic, e.g., the COVID-19 and the FA CUP. The proposed approach is parallelizable, and can simultaneously handle several data-point. The LaBSE sentence embedding is used to measure the semantic similarity between two tweets. ComStreamClust has been evaluated by several metrics such as keyword precision, keyword recall, and topic recall. Based on topic recall on different number of keywords, ComStreamClust obtains superior results when compared to the existing methods. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： COVID-19

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Enhancing Lung Cancer Early Detection Using Deep Learning Techniques 7th

Enhancing Lung Cancer Early Detection Using Deep Learning Te...

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7th International Conference on Soft Computing and Signal Processing, ICSCSP 2024

作者： Kranthi Reddy, S. Moinuddin, Khaja Waseem Akram, Md. Sachithananda, Sai Department of Computer Science and Engineering Vignan Institute of Technology and Science Hyderabad India

ISBN: (纸本)9789819799251

Exposure to carcinogens, particularly those found in cigarette smoke, destroys lung cells DNA and promotes their unchecked proliferation, which is the main cause of lung cancer. Its growth is also greatly influenced by genetic predisposition, family history, and environmental factors such as radon and asbestos. Lung cancer can affect nonsmokers due to these genetic and environmental factors, even though smoking is the main cause. These malignancies have the potential to impair breathing and the exchange of oxygen and carbon dioxide by interfering with the lungs natural function. When applied to tabular datasets, machine learning and deep learning algorithms can help forecast the prognosis of lung cancer and the response to treatment, enabling tailored care plans and enhancing patient outcomes. This study suggests using a convolutional neural network (CNN) architecture to accurately identify lung cancer from medical photos. Clinicians might possibly improve patient outcomes and survival rates by using CNNs’ sophisticated feature extraction capabilities to produce more accurate diagnoses and customized treatment programs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep learning

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Predictive Modeling of Social Media Data Using Machine Learning Techniques 2

Predictive Modeling of Social Media Data Using Machine Learn...

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2nd International Conference on Machine Learning and Autonomous Systems, ICMLAS 2025

作者： Anitha, T. Srihitha, G. Sai Aiswarya, G.R.P Lakshmi Raghi, K.R. Sathyabama Institute of Science and Technology Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331505745

Social media platforms, such as YouTube, generate an extensive amount of unstructured data, offering valuable insights into user behavior, engagement patterns, and preferences. This project focuses on predictive analysis of YouTube data using a hybrid machine learning framework. Various video metadata, including descriptions, titles, tags, views, likes, and comments, are analyzed to forecast audience engagement and trends. The pipeline begins with data preprocessing, including handling missing values, removing duplicates, and preparing the data for analysis. Exploratory Data Analysis (EDA) is performed to uncover hidden patterns and relationships, followed by feature selection techniques to identify the most influential variables. The dataset is then split into training and testing subsets, and advanced machine learning models are employed to predict video performance and user engagement. The results are evaluated using key performance metrics, delivering actionable insights to improve content strategies and audience targeting. This study highlights the effectiveness of machine learning techniques in optimizing YouTube content and enhancing decision-making in digital marketing. © 2025 IEEE.

关键词： Tweets

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Enhancing Occluded Face Recognition Through Device-Edge Collaboration and Cross-Domain Feature Fusion with Heatmaps 3

Enhancing Occluded Face Recognition Through Device-Edge Coll...

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

作者： Prasanth, Ashwin Annie Micheal, A. Department of Computer Science & Engineering Sathyabama Institute of Science & Technology Chennai India

ISBN: (纸本)9798331527549

Face recognition under occlusion presents a persistent challenge in computer vision, primarily due to difficulties in capturing and effectively integrating visible and obscured facial features. This paper introduces a novel device-edge collaborative framework that advances occluded face recognition through an innovative cross-domain feature fusion technique. Our method employs a software-generated, occlusion-aware heatmap created from standard visible-light images, which emphasizes key facial landmarks even when regions of the face are partially covered. This heatmap provides a complementary data layer that enhances traditional visible facial features, allowing the cross-domain feature fusion process to synthesize a more comprehensive set of features for reliable recognition under occlusion. The combined feature set leverages both visible and simulated heatmap information to improve model robustness, even in complex occlusion scenarios. Additionally, a delay-optimized task scheduling method is applied on the edge, ensuring efficient resource allocation, faster processing, and minimal latency. Although this project is ongoing, preliminary evaluations indicate the potential effectiveness of this approach, with expected outcomes to include high recognition accuracy and reduced latency. This framework provides a scalable solution for occluded face recognition by integrating complementary data sources without requiring specialized hardware. © 2025 IEEE.

关键词： Face recognition

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