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Bio-Inspired Tree Seed Algorithm Enhanced Discrete Wavelet Technique for Memory Control 4

Bio-Inspired Tree Seed Algorithm Enhanced Discrete Wavelet T...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： More, Sachin D Ingle, Dayanand Bharati Vidyapeeth College of Engineering University of Mumbai Dept. of Computer Engineering Navi Mumbai India

ISBN: (纸本)9798331523923

The widely recognized classical image compression techniques are renowned for their excellent performance, minimal computational complexity, and the ability to generate an embedded bitstream that can be decoded at varying bit rates, resulting in progressively improved image quality as more bits are received. It requires a significant amount of computer memory as it stores the generated sets and the pixel locations in linked lists. The multiresolution feature of transform does not result in the creation of a resolution-scalable bitstream that would allow the image to be decoded at various resolutions. The enormous memory issue with the conventional methods is resolved by the Tree Seed Algorithm (TSA) an Enhanced Discrete Wavelet Transform methodology which requires just one list of fixed size with average of 2.25 bits/pixel and size of 1/4 of the image. The primary contribution of this study lies in enhancing image quality and resolution thereby optimizing memory usage. The TSA Optimized Discrete Wavelet Transform approach is recommended in this study to decrease complexity and improve processing speed. Using the unique attributes of the algorithm, it is particularly well-suited to meet the diverse skill levels and resolution preferences of contemporary Internet users. © 2025 IEEE.

关键词： Decoding

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Designing and Implementing an IoT-Based Long-Distance Fire-Extinguishment System 'Stationary 3-Axis Fire-Emergency Response Base' 8

Designing and Implementing an IoT-Based Long-Distance Fire-E...

引用

8th International Conference on Electronics, Materials engineering and Nano-Technology, IEMENTech 2025

作者： Kundu, Shyantan Dey, Swarajit Abdullha, Md. Mustafa Sen, Gobinda Gangopadhyay, Malay Institute of Engineering & Management Dept. of Computer Science & Engineering Kolkata India Institute of Engineering & Management Dept. of Electronics & Communication Engineering Kolkata India

ISBN: (纸本)9798331520762

This paper presents the design and implementation of an Internet of Thing (IOT)-based long-distance fire-extinguishment system named 'Stationary 3-Axis Fire-Emergency Response Base' (STAFERB). The system aims to provide immediate response to fire hazards in various environments, including residential areas, building complexes, commercial areas, small-scale industries, large factories, dense slum areas, and remotely located forest regions. The proposed STAFERB is designed to receive fire location coordinates, fire intensity, and probable cause information. It then calculates the path of a projectile based on considering obstacles and self-detection of a clear line of sight to reach the t1arget location. The system uses this information to select the appropriate speed and type of fire retardant. The designed STAFERB features robust communication, self-path detection, auto-orientation, and reduced response time, making it a significant advancement in fire-fighting technology. Experimental results demonstrate the effectiveness of the proposed STAFERB in mitigating fire hazards and hence can be well suited for real-time disaster management. © 2025 IEEE.

关键词： Fire hazards

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Automated Skin Cancer Classification and Detection Using Convolutional Neural Networks and Dermoscopy Images 4

Automated Skin Cancer Classification and Detection Using Con...

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4th International Conference on Evolutionary Computing and Mobile Sustainable Networks, ICECMSN 2024

作者： Sara, Umme Das, Utpol Kanti Sikder, Juel Chowdhury, Sudipta Roy Dept. of Computer Science and Engineering National Institute of Textile Engineering and Research Savar Dhaka Bangladesh Dept. of Computer Science and Engineering Rangamati Science and Technology University Rangamati Sadar Rangamati4500 Bangladesh

The uncontrolled growth of skin cells in the epidermis producing the creation of a mass termed a tumor is a dangerous condition known as skin cancer. Current developments in deep learning artificial intelligence have greatly improved image-based diagnosis. In this study, we included a Skin Lesion Cancer feature extractor Convolutional Neural Network (SLC-CNN) model, which is used for both classification with the SVM classifier and segmentation with XGBoost for skin cancer. In our proposed system, a test image of skin cancer is taken and pre-processed for both classification and segmentation purposes. After applying pre-processing, the test image features are extracted using the SLC-CNN feature extractor, which features are used in SVM to classify the types of skin cancer (Benign and Malignant), and based on the classification result, a trained XGBoost model is called to segment the cancer region. We have tested our system using the dermoscopy image collection from the International Skin Imaging Collaboration (ISIC) and built it in Google Colab to best use the GPU. Our suggested approach has gained a segmentation accuracy of 95.25% and a classification accuracy of 99.6%. © 2025 The Authors. Published by Elsevier B.V.

关键词： Lung cancer

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Energy Efficient LEDCA Analysis for Improved Throughput in Wireless Sensor Networks 6

Energy Efficient LEDCA Analysis for Improved Throughput in W...

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

作者： Padmageetha, B.G. Naik, Pramod Kumar Patil, Mallanagouda Dept.of Computer Applications Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering Dayananda Sagar University Bengaluru India Dept. of Computer science and Engineering RVITM Bengaluru India

ISBN: (纸本)9798331522667

Wireless Sensor Networks (WSNs) face significant challenges in terms of energy conservation, scalability, and data throughput owing to their limited node energy resources. This paper presents a low-energy data-centric algorithm (LEDCA), a novel energy-aware clustering and routing protocol designed to optimize energy efficiency and improve throughput in WSNs. Through simulations, LEDCA demonstrated a Packet Delivery Ratio (PDR) of up to 90%, throughput of 255,217 bps, and average latency of 159 ms, validating its efficiency in resource-constrained environments. The algorithm integrates residual energy awareness by prioritizing high-energy nodes for Cluster Head (CH) roles, dynamically adjusting routing paths, and balancing the network load to prevent premature node failure. By applying an Energy Mapping Node and a real-time Energy Mapping Table, LEDCA minimizes re-clustering, reduces data redundancy, and ensures stable data transmission even under high-density networks. Simulation results also show LEDCA's scalability of LEDCA, with A9_T_200 achieving the highest throughput under extended transmission ranges and A3_T_200 achieving the lowest latency, making it suitable for energy-sensitive and latency-critical applications. Future enhancements, such as integrating Non-Orthogonal Multiple Access (NOMA) and dynamic resource allocation, could further improve LEDCA’s interference management and scalability of LEDCA for IoT and 5G/6G networks, addressing the needs of next-generation applications. © 2025 IEEE.

关键词： Resource allocation

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Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent Intrusion Detection System 2

Deep Defense: A Hybrid LSTM-ANN Architecture for Intelligent...

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

作者： Shadeja, Mohit Singh, Shashwat Prakash, Ayushi Dept. of Computer Science and Engineering Ajay Kumar Garg Engineering College Uttar Pradesh Ghaziabad India

ISBN: (纸本)9798331530389

The rapidly evolving cyber threat landscape requires innovative and adaptive intrusion detection solutions. Traditional signature-based intrusion detection systems, despite their high accuracy, are inherently inflexible and susceptible to evasion tactics, making them ineffective against novel and sophisticated attacks. In contrast, machine learning-powered anomaly-based intrusion detection systems demonstrate improved detection capabilities, but their advantages are often outweighed by substantial drawbacks, including significant computational resource requirements when the dataset and its features for behavior analysis increase. Also, there are high power consumption and latency issues in communication that compromise network performance. This study proposes a hybrid ANN-LSTM approach for performing deep packet inspection and efficiently detecting malicious network packets. By analyzing binary container data, our model reduces time and space complexity, thereby mitigating the computational resources and power consumption challenges but also increasing the efficiency of intrusion detection by 3%, making it more intelligent than various established IDS. © 2025 IEEE.

关键词： Network intrusion

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Smart Agriculture: A Crop Monitoring System to Empower Agricultural Field Using IOT

Smart Agriculture: A Crop Monitoring System to Empower Agric...

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2025 International Conference on Emerging Technologies for Intelligent Systems, ETIS 2025

作者： Madhu, Surya Sujith Kumar, P.S. Supriya, L.P. Sree Buddha College of Engineering Dept. of Computer Science and Engineering Kerala India

ISBN: (纸本)9798331507541

Agriculture has long been the cornerstone of India's economy and way of life, contributing significantly to the nation's GDP and employment. With a diverse range of crops and climatic conditions, India's agricultural sector is both vast and complex, demanding innovative approaches to meet the growing needs of its population. The sector faces numerous challenges, including climate variability, soil degradation, water scarcity, and pest infestations, which impact productivity and sustainability. In response to these challenges, modern agricultural system in India increasingly focuses on integrating technology to enhance efficacy and eco-friendly. One such initiative is the development of smart agricultural systems that leverage advanced technologies. This paper presents an advanced Internet of Things (IoT) based crop monitoring system designed to enhance agricultural efficacy and conciliatory. The system integrates multiple sensors to monitor critical environmental parameters, including pH levels, soil properties, and temperature. By continuously measuring these variables, the system provides real-time data to optimize crop health and growth conditions. Additionally, the paper incorporates pest control mechanisms, leveraging IoT technology to detect and manage pest populations effectively. This comprehensive approach not only ensures the optimal condition for crops but also minimizes the need for manual intervention and reduces the reliance on chemical treatments. The data collected through this system is analysed and visualized through a user-friendly interface, offering farmers actionable insights and enabling timely decision-making. The integration of these technologies aims to promote sustainable agricultural practices, improve crop yield, and reduce environmental impact, demonstrating the potential of IoT in modernizing agricultural operations. © 2025 IEEE.

关键词： Insect control

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A Review of Various Deep Learning Approaches for Placement Prediction 2

A Review of Various Deep Learning Approaches for Placement P...

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

作者： Bodke, Minal Tiwari, Vishnu Wadhwani, Jayesh Gorbade, Trapti Soundane, Rohan Pimpri Chinchwad College of Engineering and Research dept. Computer Engineering Pune India

ISBN: (纸本)9798331505745

In recent years, predictive analysis for student placement has attracted a lot of attention, and machine learning approaches have shown to be useful in enhancing decision-making and accuracy in higher education. This review paper provides a conceptual framework to improve student placement predictions by using deep learning models with self-attention mechanisms. The aim of this model is to tackle problems related to feature importance selection, imbalanced datasets, and complex data patterns. We suggest a multi-tiered strategy that combines deep learning's ability to identify complex correlations in instructional datasets with ensemble learning techniques for reliable performance. Predictive quality will be thoroughly examined through the use of performance indicators such as accuracy, precision, recall, and F1 score in the evaluation of the models. It is anticipated that this strategy will give educational institutions more accurate forecasting instruments, revealing important factors influencing students' employability while guaranteeing decision-makers can understand the data. This framework intends to make a contribution to the expanding field of educational data mining and placement prediction by improving existing approaches. © 2025 IEEE.

关键词： Deep learning

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AI-Driven Solutions with CNN and IoT for Enhancing Visual Impairments 5

AI-Driven Solutions with CNN and IoT for Enhancing Visual Im...

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5th International Conference on Emerging Systems and Intelligent Computing, ESIC 2025

作者： Prasad, M. Lakshmi Varshitha, Veeramalli Goud, A. Praneeth Bindu, R. Srinivasulu, Ch. Rao, S. Janardhana Institute of Aeronautical Engineering Dept. of Computer Science and Engineering Dundigal Hyderabad India

ISBN: (纸本)9798331522100

Visual impairment is a problem that affects millions of people worldwide, preventing them from walking independently and performing daily tasks. Some of the challenges that blind people face include crossing the street, reading, driving, and socializing. Despite the scientific community's continuous efforts to treat the eye, these solutions have not yet been found. Traditional aids such as canes are useful but limited. Get in the groove. The main focus of the project is to integrate the YOLOv5 object detection model with a convolutional neural network (CNN). Known for its fast and accurate target detection, YOLOv5, together with CNN, improves the physical ability to detect and classify targets in difficult areas with high accuracy. The eye uses the camera module to continuously monitor the environment and identify various objects in real time;the detected data is converted into speech by text-to-speech (TTS) converters such as Google Text- to-Speech (gTTS) and pyttsx3 This description allows users to get an instant description of their surroundings, unlike helping the blind, it improves their ability to navigate independently rather than just providing references, but there are also good ways to work with better and more reliable assistive technologies. © 2025 IEEE.

关键词： Convolutional neural networks

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An Evolutionary Algorithm for the Travelling Sales Man problem 3

An Evolutionary Algorithm for the Travelling Sales Man probl...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Waseem, Shaik Mohammad Jeshwanth, N.L. Lingesh, A. Jayanth, Matta Bhargavi, C.H. Dhanwanth, Batini Dept. of Computer Science and Engineering Chennai India Dept. of Artificial Intelligence and Data Science Chennai India Sai Spurthi Institute of Technology Dept. of Computer Science and Engineering Gangaram Sathuaplly India Jönköping University Dept. of Ai Engineering Jönköping Sweden

ISBN: (纸本)9798331509675

This study explores the use of Genetic Algorithms (GA) to solve the NP-hard combinatorial optimization problem known as the Travelling Salesman Problem (TSP). The suggested GA, called GA-P, performs better than conventional techniques because it uses a new randomized crossover threshold. According to experimental assessments, GA-P outperforms a normal Genetic Algorithm with fixed crossover (GA-N, 628.6) and approaches the best Branch and Bound solutions (B&B, 560.6) with an average cost of 617.4 for 16-node graphs. With average execution times of 0.1347 seconds for 16 nodes, GA-P also demonstrates constant computational efficiency, outperforming B&B's exponentially growing temporal complexity (169.37 seconds for 16 nodes). In order to balance solution quality and computational cost, hyperparameter tweaking revealed the ideal values of population percentage (pp = 1.1), crossover proportion (cp = 0.8), mutation threshold (mt = 0.3), and exploration probability (ep = 0.2). The results show that GA-P is a scalable and efficient substitute for TSP, with room for improvement via parallelization and exploratory techniques. © 2025 IEEE.

关键词： Traveling salesman problem

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Multi-Layered Approach to IoT Cybersecurity in Smart Cities 3

Multi-Layered Approach to IoT Cybersecurity in Smart Cities

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

作者： Benjamine, Daniel Anto Madhavan, K. Pavithra, S. Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India

ISBN: (纸本)9798331527549

Internet of Things (IoT) has been rapidly growing over the past years. There have been many cybersecurity challenges faced by IoT-based smart cities in existing work for the energy, health and transportation sectors. These cybersecurity challenges are crucial to remove in the IoT sector to implement a safe and secure IoT based smart city. Vulnerabilities across layers in IoT are analyzed and a multi-layered model of security is proposed to enhance data integrity and communication security by using AI, blockchain and ML. This paper would also present several policy recommendations to stakeholders and IoT developers for building a safe urban IoT ecosystem. © 2025 IEEE.

关键词： Cybersecurity frameworks IoT smart city technologies urban

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