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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Paul, Poulin Manishankar, S. Satheesh Kumar, R. Sahrdaya College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Thrissur India Jawaharlal College of Engineering and Technology Dept. of Computer Science and Engineering Kerala Palakkad India

ISBN: (纸本)9798350370249

This paper presents an integrated framework for the comprehensive analysis of diseases affecting pomegranate fruit. The suggested system, which makes use of deep learning techniques, includes semantic segmentation for diseased region localization, multiclass illness classification, and image processing-based severity assessment. Initially, four common diseases - Alternaria, Anthracnose, Bacterial Blight, and Cer-cospora - are successfully identified through the use of a Convolutional Neural Network (CNN) for multiclass classification. Then, annotated photos are used as training data for Semantic Segmentation, which uses the UNet architecture to identify the unhealthy spots within the fruit. The area and percentage of the segmented region are determined, and thresholds for high, medium, and low severity levels are then defined. The segmentation results are then used as inputs for severity estimate. Early identification and intervention tactics are made easier by the severity level prediction, which is based on the proportion of the segmented region. In order to improve pomegranate fruit management methods, the suggested framework provides a thorough method of disease analysis by combining categorization, semantic segmentation, and severity prediction. © 2024 IEEE.

关键词： Annotation Convolutional Neural Networks Deep learning Semantic segmentation Unet

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A Recent Survey on Bone Cancer Detection using Deep Learning Techniques 2

A Recent Survey on Bone Cancer Detection using Deep Learning...

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2nd IEEE International Conference on Advances in Information Technology, ICAIT 2024

作者： Murugan, S. Patil, Shravani S. Shree Deeksha, V. Reddy, Rohith K. Shravanth Kumar, K.N. Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru562157 India Dept. of Computer Science and Engineering Sri Venkateshwara College of Engineering Bengaluru56215 India

ISBN: (纸本)9798350383867

The emergence of deep learning methods in medical image analysis has transformed the detection and categorization of bone tumors, presenting encouraging pathways for enhanced diagnosis and treatment strategizing. This literature review synthesizes recent advancements in utilizing deep learning architectures, particularly convolutional neural networks (CNNs), for the automatic interpretation of various imaging modalities including X-rays, CT scans, MRI scans, and bone scintigrams in the context of bone cancer detection and classification. Each study discussed presents unique methodologies combining traditional image processing techniques also the augmentation techniques with deep learning models to accurately identify, categorize, and segment bone lesions. Despite the encouraging results in terms of accuracy, sensitivity, and specificity, several common limitations are identified across the studies, including small sample sizes, potential biases, limited validation on diverse datasets, and a lack of thorough comparisons with existing techniques or expert interpretations. Addressing these limitations through larger and more diverse datasets, rigorous validation protocols, uncertainty quantification, and consideration of clinical outcomes would enhance the reliability, generalizability, and clinical utility of the proposed deep learning-based methodologies. Overall, these studies underscore the immense potential of deep learning approaches in advancing the field of bone cancer diagnosis and management, paving the way for more robust and clinically applicable tools to assist medical professionals. ©2024 IEEE.

关键词： Diseases

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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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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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Detection of Vehicle Crashes on Roads using Deep Learning 2

Detection of Vehicle Crashes on Roads using Deep Learning

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2nd International Conference on Advancement in Computation and computer Technologies, InCACCT 2024

作者： Pokkuluri, Kiran Sree Sssn Usha Devi, N. Prasad, M. Raja Rao, Pbv Varma, Ch Phaneedra Ramesh Babu, G. Shri Vishnu Engineering College for Women Dept. of Computer Science and Engineering Bhimavaram India University College of Engineering College Dept. of Computer Science and Engineering JNTU Kakinada India

ISBN: (纸本)9798350371314

As the modern life is highly dependent on the modern transport system, road safety became a high priority. Most of the systems are aiming for very less reporting time to reduce the severity of the accidents. This paper presents an in-dept. analysis of crash detection on roads using deep learning(DL). We have use one of the mechanisms of deepening i.e. Convolutional Neural Networks (CNNs) to detect the same. These DL models are used to classify the traffic accidents and it automatically recognize accidents with minimum effort. We have integrated automatic SMS alert with the system, which send SMS automatically after detecting the crash. This feature enhances the acceleration of emergency response, which makes the system more robust. We have achieved an accuracy of 96.3% which relatively good compared with the existing baseline methods. © 2024 IEEE.

关键词： Convolutional neural networks

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A Comparative Analysis of Image Partitioning and Compression Mechanisms 15

A Comparative Analysis of Image Partitioning and Compression...

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Chaudhari, Sampada P. Thakur, Nileshsingh V. Dept. of Computer Science and Engineering Prof Ram Meghe College of Engineering & Management Badnera India Dept. of Computer Science and Engineering Yeshwantrao Chavan College of Engineering Nagpur India

ISBN: (纸本)9798331300579

This paper presents a comprehensive comparative analysis of image partitioning and compression mechanisms, two fundamental techniques in image processing and data compression. Image partitioning involves dividing an image into smaller components, while compression aims to reduce the size of digital images while preserving visual quality. The analysis encompasses various aspects of both techniques, including methodologies, algorithms, and performance metrics. For image partitioning, segmentation methods such as block-based, region-based, and hierarchical partitioning are examined, along with their respective advantages and limitations. Additionally, the paper explores compression mechanisms such as transform coding, quantization, entropy coding, and predictive coding, highlighting their roles in achieving efficient compression. Furthermore, the comparative analysis evaluates the tradeoffs between different partitioning and compression techniques in terms of compression ratio, image quality, computational complexity, and applicability across different types of images and applications. Various compression standards such as JPEG and PNG are also reviewed to provide insights into real-world implementations. Finally, the paper discusses future research directions and emerging trends in image partitioning and compression, including the integration of machine learning techniques for improved segmentation and compression performance. The findings presented in this comparative analysis can serve as a valuable resource for researchers, practitioners, and developers working in the fields of image processing, data compression, and multimedia systems. © Grenze Scientific Society, 2024.

关键词： Image compression

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ENHANCING E-HEALTH CYBERSECURITY AND RESILIENCE: SHIFTING FROM MONOLITHIC TO MICROSERVICES ARCHITECTURE

UPB Scientific Bulletin, Series C: Electrical Engineering an...

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UPB Scientific Bulletin, Series C: Electrical engineering and computer science 2025年第1期87卷 21-34页

作者： Contașel, Cristian Rughiniș, Răzvan Trancă, Dumitru Cristian Țurcanu, Dinu Dept. of Computer Science and Engineering National University of Science and Technology POLITEHNICA Bucharest Romania Dep. of Software Engineering and Automatics Technical University of Moldova Chișinău Moldova

This study provides an innovative architectural model for e-Health systems that aims to improve cyber resilience while maintaining high availability under fluctuating traffic loads. We examined typical cybersecurity incidents in the field of e-Health, their correlations with architectural defects, and frequent design patterns in currently operational systems. A testing approach based on our research finds those weaknesses and proposes viable fixes. In this paper, a comprehensive support strategy for transitioning from conventional monolithic architectures to microservices is presented. This change makes use of cloud computing's vertical and horizontal scalability to maximize resource utilization while ensuring system reliability. We also discuss deployment ideas for the new microservices, focusing on operational resilience and cybersecurity in e-Health environments. © 2025, Politechnica University of Bucharest. All rights reserved.

关键词： Electronic health record

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Performance Analysis of Deep Learning Models in Driver Behavior Recognition

Performance Analysis of Deep Learning Models in Driver Behav...

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2024 International Conference on Cybernation and Computation, CYBERCOM 2024

作者： Kapoor, Rameshwari Kuliyal, Rashmi Bhanot, Nilesh Bagla, Piyush Rawat, Ruchira Graphic Era Hill University Dept. of Computer Science and Engineering Uttarakhand Dehradun India University Dept. of Computer Science and Engineering Uttarakhand Dehradun India

ISBN: (纸本)9798331542344

The World Health Organization report states that nearly 1.25 million people lose their lives due to an increasing number of road accidents every year. The majority of accidents are reported due to driver errors, which include distractions like the use of mobile phones and fatigue, which causes drowsiness. This study aims to address these issues by performing a comparative analysis between different deep learning models representing two types of learning mechanisms: Traditional deep learning and transfer learning. This study portrays the use of two datasets to accomplish this task. These two datasets are the Driver Drowsiness Dataset DDD and the State Farm Distracted Drivers Dataset. The purpose of this investigative research is to explore the execution and efficiency differences between three deep learning algorithms, the traditional convolutional neural network, ResNet50, and VGG16, in classifying driver behavior by training these models individually on both datasets. This study also targets developing an alarm system that sounds an alarm based on a threshold by using an ensemble of all the models trained for comparative study and averaging their predictions to classify driver behavior. VGG16 achieved an accuracy of 99.74% on the driver drowsiness dataset, whereas Custom CNN Architecture achieved an accuracy of 98.70% on the driver distraction dataset. © 2024 IEEE.

关键词： Contrastive Learning

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A Hybrid Approach Using Cryptography and K-Least Significant Bit Steganography Algorithm in Cloud Computing 1

A Hybrid Approach Using Cryptography and K-Least Significant...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Goyal, Anshika Gupta, Bhumika VMSB Uttarakhand Technical University Dept. of Computer Science & Engineering Dehradun India G.B. Pant Institute of Engineering & Technology Dept. of Computer Science & Engineering Pauri India

ISBN: (纸本)9798331518578

The exponential growth of communication technologies has broadened the cloud computing ecosystem horizon to meet main communication needs. However, in-parallel upsurge in online attacks has alarmed industries to ensure optimal data security. The combination of steganography and cryptosystems has been recognized as an enhanced security model for data transfer among the main security systems now in use. In this paper, a hybrid approach using cryptography and K- Least Significant Bit Steganography algorithm in Cloud Computing (HYBC-KLSBS-CC) are proposed. Initially, input text data is retrieved from cloud storage and compressed using Huffman Compression Coding (HCC) to reduce data size. The compressed data is then encrypted with a Hybrid Cryptography Algorithm (Hyb-MRFC-ALC), combining Modified Random Fibonacci Cryptography and Asymmetric Lightweight Cryptography to ensure robust encryption. Finally, encrypted data is embedded within an image using the K-Least Significant Bit (KLSB) steganography technique, providing additional layer of security. The proposed method enhances data confidentiality, integrity, and storage efficiency in cloud environments. The proposed HYBC-KLSBS-CC approach attains 26.89%, 29.49% and 18.64% lower PSNR and 26.89%, 29.49% and 18.64% lower MSE when analyzed through existing techniques like data security dependent on cryptography steganography related with OTP procedure and Huffman coding in the cloud environment (CS-OTP-CE), a novel hybrid cryptographic framework for secure data storage in cloud computing: Integrating AES-OTP and RSA with adaptive key management and Time-Limited access control (AES-OTP-RSA-CC) and Crypto-Stegno dependent model for securing medical information on IOMT platform (CS-SMI-IOMT). © 2025 IEEE.

关键词： Cloud platforms

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Power Control of Preamble Transmission for Grant-free Random Access in Cell-Free IoT Networks 15

Power Control of Preamble Transmission for Grant-free Random...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Youn, Jiseung Cho, Sunghyun Hanyang University Dept. of Computer Science & Engineering Seoul Korea Republic of Hanyang University ERICA Dept. of Computer Science & Engineering Ansan Korea Republic of

ISBN: (纸本)9798350364637

As Internet of Things (IoT) technologies are inves-tigated to support key 5G services, significant research efforts are being made to enable machine-type communication (MTC). Cell-free IoT (CF-IoT) systems, characterized by favorable prop-agation and macro diversity, are being studied in conjunction with grant-free random access (GFRA) as a potential enabler to achieve massive connectivity for MTC. However, existing GFRA schemes in CF-IoT transmit preambles at a fixed power level resulting in severe preamble collisions at nearby access points (APs). In this paper, we analyzes the analysis of preamble collisions in massive connectivity scenario and proposes a sim-ple power control scheme to address preamble collision. The proposed scheme mitigates preamble collisions by transmitting preambles only to the nearest AP in high-density access scenes. Simulation results show that the proposed method improves the random access success rate compare to existing GFRA method by 10%. © 2024 IEEE.

关键词： cell-free massive MIMO grant-free random access Internet of Things power control

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