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Challenges and Innovations in Multimedia and Real-Time Networking: A Review of Modeling Approaches

Procedia computer science 2025年 252卷 53-62页

作者： Milind Shah Kajal Parmar Priyanka Padhiyar Naina Parmar Monali Parikh Dhruvansh Patni Assistant Professor Department of Computer Engineering Sardar Vallabhbhai Patel Institute of Technology (S.V.I.T) Vasad Gujarat India Department of Computer Engineering Devang Patel Institute of Advance Technology & Research (DEPSTAR) Faculty of Technology & Engineering (FTE) Charotar University of Science & Technology (CHARUSAT) Changa 388421 India Assistant Professor Department of Information Technology Krishna School of Emerging Technology & Applied Research - Drs. Kiran & Pallavi Patel Global University (KPGU) Vadodara Gujarat India

Over the past decade, the rise of broadband and mobile Internet access has led to the widespread adoption of real-time networking and multimedia applications. These platforms have become essential for connecting individuals and supporting businesses, particularly with the growing trend of remote work. The demand for video streaming services has surged significantly and is expected to continue rising. However, this increased traffic has strained network performance, leading to congestion and diminishing the quality of service, especially during peak evening hours and the COVID-19 pandemic. Besides upgrading network infrastructure, it is crucial to develop intelligent streaming systems that adapt to network conditions and user expectations to enhance customer satisfaction. This review paper explores into the basics and introduction of Multimedia and Real-Time Networking. It also explores the advancements in Adaptive and Intelligent Networking for real-time multimedia communication. The paper formulates research questions based on the discussed topics, such as the distinctions between real-time networking and traditional networking, the ways multimedia applications adjust to fluctuating network conditions, and the challenges and limitations associated with these technologies.

关键词： Real Time Networking Multimedia Challenges Innovations Intelligent Networking

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A Novel Effective Lightweight Homomorphic Cryptographic Algorithm for data security in cloud computing

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International Journal of Intelligent Networks 2022年 3卷 16-30页

作者： Thabit, Fursan Can, Ozgu Alhomdy, Sharaf Al-Gaphari, Ghaleb H. Jagtap, Sudhir Postdoctoral Researcher Department of Computer Engineering Faculty of Engineering Ege University Turkey Assoc. Prof Dr. Department of Computer Engineering Faculty of Engineering Ege University Turkey Sana'a University Yemen Professor and Principal Research Centre of Computer Science S.V.M Campus - S.R.T.M. University India

Cloud computing is a technology in which the resources are delivered as services. Users can access them anywhere, anytime via the Internet without any need to know the infrastructure knowledge, experience, or even authority that provides such services. It has become an important medium for enterprises to develop their networks because of these resources. With the growing need for cloud computing, security is becoming important for both individuals and business needs. Many researchers have studied the security of cloud computing. Still, security gaps or threats are increasing as the demand for cloud computing connectivity increases. Because a third party mostly provides cloud platforms to the cloud client, data protection in the cloud is the primary issue. This paper presents a novel, effective lightweight homomorphic cryptographic algorithm which contains two layers of encryption. The first layer uses the new effective, light-weight cryptographic algorithm and the second layer multiplicative homomorphic schemes considered for improving security data in cloud computing. This approach offers both symmetric and asymmetric cryptography features. The proposed approach's performance is evaluated using a variety of metrics, including memory, computational time and (key sensitivity), statistical analysis, image histogram, and entropy change analysis. The proposed algorithm's experimental findings showed a high level of security and an apparent improvement in encryption execution time, memory usage, and throughput. When compared to the cryptographic systems widely used in cloud computing. © 2022 The Authors

关键词： Cryptography

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Digital Communications and Networks

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Digital Communications and Networks 2022年第3期8卷 235-236页

作者： Ruidong Li Shiwen Mao Periklis Chatzimisios Constandinos X.Mavromoustakis Intelligent computation and network laboratory(ICNL)at Kanazawa University Japan the Wireless Engineering Research and Education Center(WEREC) Department of Information and Electronic Engineering at the International Hellenic University(Greece) Constandinos X.Mavromoustakis is currently a Professor at the Department of Computer Science at the University of Nicosia Cyprus

Guest editorial The emerging applications,suchas Augmented and Virtual Realities(AR/VR),InternetofThings(IoT),4K/8Kstreaming,raisestrongrequirementsto movecomputationfrom thecloudtotheedgestobecloser *** are tremendous possibilities for the network edge,which may includeavariety ofentities,such as small datacenters,end devices,and resource-abundant network *** together provide the network computation and intelligence to users.

关键词： network IoT Communications

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Utilizing CNN-Based architecture for Automated Differentiation between Gram-Positive and Gram-Negative Bacteria

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Procedia computer science 2025年 258卷 3900-3909页

作者： Debashree Borthakur Smriti Priya Medhi Ranbir Chowdhury Subrata Sinha Bipin Kumar Sharma Jayanta Aich Research Scholar Dept of Microbiology Tripura University Tripura India Assistant Professor (Senior) Dept of Bio Sciences Assam Don Bosco University Assam India Assistant Professor (Senior) Dept of Computer Science and Engineering Assam Don Bosco University Assam India Undergraduate student Dept of Computer Science and Engineering Assam Don Bosco University Assam India Associate Professor Dept of Computational Sciences Brainware University Kolkata India Centre for Multidisciplinary Research and Innovations Brainware University Barasat Kolkata Assistant Professor (Stage III) Dept of Microbiology Tripura University Tripura India Associate Professor Department of Computational Sciences Brainware University Kolkata India

Objectives: This study explores the integration of advanced machine learning methods, specifically convolutional neural networks (CNN), with bacterial image classification to enhance the reliability and efficiency of bacterial classification systems. We present a deep learning approach for the automatic classification of Gram-stained bacterial images into Gram-positive and Gram-negative categories. Methods: Gram staining is a widely used microbiological technique that distinguishes bacteria based on cell wall composition, classifying them as either Gram-positive or Gram-negative. We propose a CNN-based architecture designed to automate this classification process. The model is trained on a large, annotated dataset of Gram-stained bacterial images, leveraging the power of deep learning to achieve state-of-the-art performance in terms of both accuracy and processing speed. Results: Our results show that deep learning can significantly improve the accuracy and efficiency of Gram-stained bacterial image classification, paving the way for automated microbiological analysis in clinical and research settings. Our proposed architecture has classified the gram-positive and gram-negative images of bacterial cells, achieving an accuracy of 95.74% and precision of 96.97%. Novelty: Historically, Gram-stained bacterial image classification has been performed manually by microbiologists. This research pioneers the use of deep learning techniques for the automated classification of these images, achieving substantial improvements in both accuracy and speed. This innovation paves the way for the integration of automated image analysis into microbiological diagnostics and research.

关键词： Gram staining Bacterial Image Classification Deep Learning Convolutional neural network (CNN) Microbiology Gram-positive Gram-negative Automated analysis

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Smart Water Taps Using IoT Sensors and Deep Reinforcement Learning for Personalized Consumption Control

Smart Water Taps Using IoT Sensors and Deep Reinforcement Le...

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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Mageshkumar Naarayanasamy Varadarajan Viyyapu Lokeshwari Vinya R. Jenin Prabhu G. Swathi N. Mohankumar S. Velmurugan Lead Software Engineer Capital One Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering Vardhaman College of Engineering Ranga Reddy Telangana India Assistant Professor Department of Electrical and Electronics Engineering Vel Tech Multi Tech Dr. Rangarajan Dr. Sakundhala Engineering College Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Professor Symbiosis Institute of Technology (SIT) Symbiosis International (Deemed University) Nagpur Maharashtra India Professor Department of Electronics and Communication Engineering T.J.S. Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research introduces an innovative approach to water conservation and personalized consumption management by combining Internet of Things (IoT) sensors with Deep Reinforcement Learning (DRL) methods. The suggested approach aims to give people more control over their water use in response to water shortages and the need for sustainable resource management. It installs IoT sensors on taps to track water use in real time. A DRL framework is fed data that these sensors have collected. The DRL algorithm develops individual consumption profiles for each user by factoring in past water use, environmental conditions, and personal tastes. Using this unique profile, water efficiency may be improved instantly. Users may easily enter their water conservation choices, budgets, and the system's user interface. Adjusting the water's flow rate by the user profiles learned by the DRL actively regulates water use while keeping people happy. The results of the experiments reveal that our method effectively reduces both water use and waste. Integrating IoT sensors with DRL methods provides a flexible and scalable answer for smart water management that economically benefits the environment and consumers.

关键词： Water heating Water conservation User interfaces Deep reinforcement learning Real-time systems Sensor systems Internet of Things

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Standardization of Pediatric Cardiovascular Anomaly Anatomy Reconstructions

Standardization of Pediatric Cardiovascular Anomaly Anatomy ...

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Medical Technologies National Conference (TIPTEKNO)

作者： Mohamad Humam Alzaeim Kevser Banu Köse Şenol Pişkin Wael Shamsi Basha Fatma Zeynep Gungoren Ömer Faruk Şahin Js Femilda Josephin Master’s Student in Biomedical Engineering & Bioinformatics Istanbul Medipol University Istanbul Turkey Assistant Professor in Biomedical Engineering department Istanbul Medipol University Istanbul Turkey Assistant Professor in Mechanical Engineering department İstinye University Istanbul Turkey Department of Radiology The Anadolu Medical Center Istanbul Turkey Bachelor of Science in Molecula Biotechnology İstinye University Istanbul Turkey Assistant Professor in Computer Engineering Department İstinye University Istanbul Turkey

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

In the realm of congenital cardiovascular diseases, the understanding, modeling, and visualization of such diseases as they develop during the neonatal growth period continues to be a challenge despite the current technological advancements. Moreover, such diseases as vascular abnormalities pose further risks due to their implications for a proper standardized method for three-dimensional visualization for planning and growth monitoring. This study uses imaging and computational techniques to create multiple models that relate the diseases as they progress from the initial phase toward the late growth stage for newborns’ cardiovascular systems. Three-dimensional modeling of patients would be created from patient-specific scans coupled with image processing algorithms. This study also aims to use the working principles of already existing tools for diagnosing aortic & brain aneurysms and pulmonary hypertension for cardiovascular development. To comprehensively understand such diseases and how their physical morphology may be standardized.

关键词： Visualization Solid modeling Pediatrics Computational modeling Morphology Standardization Brain modeling Planning Monitoring Medical diagnostic imaging

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Relevance of ISO/SAE 21434 in Vehicular Architecture Development

Relevance of ISO/SAE 21434 in Vehicular Architecture Develop...

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IEEE International Conference on Systems, Man and Cybernetics

作者： V Kethareswaran Sanjay Moulik Doctoral Researcher in the Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Assam India Assistant Professor in the Department of Computer Science and Engineering Indian Institute of Information Technology Guwahati Assam India

Research in terms of security in vehicular architecture has been a focal point of deliberation in the automotive sector. The Automotive industry development has continuously evolved the scenario of adding more integration to networked vehicles or semi-autonomous vehicles. The end product of such manufacturing practices is a vehicle becoming a networked computer on wheels. In spite of providing numerous advantages, these developments in the automotive industry are also bringing more challenges in terms of security. Hence, the industry has been forced to emphasise the principle of Security by Design. Efforts in this direction have resulted in the formulation of the ISO/SAE 21434 standard. In this work, we focus on summarising the ISO/SAE 21434 standard and analysing its relevance for automotive architecture development. Related works in the standard's approach of Security by Design are summarised. Additionally, we propose an application work of the standard in the context of automotive Architecture development as future work.

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A Novel VGG-XGB Model for the Comprehensive Detection and Classification of Apple Leaf Diseases Using Transfer Learning

A Novel VGG-XGB Model for the Comprehensive Detection and Cl...

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Trends in Electrical, Electronics, computer engineering Conference (TEECCON)

作者： A. Joice Shakila S. Saravanan Research Scholar Department of Computer and Information Science Annamalai University Chidambaram Tamilnadu India Assistant Professor/Programmer Department of Computer and Information Science Faculty of Science Annamalai University Chidambaram Tamilnadu India

ISBN: (数字)9798331505714

ISBN: (纸本)9798331505721

The vital role of growing apples in global farming, both for their nutritional and economic power, shows the need to tackle diseases hitting apple orchards. Using technology, especially computer-aided diagnosis systems, offers a great way to boost the spotting and sorting of diseases. This gives big gains to the farmers by upping the quality and yield of their apples. Bringing tech progress to protect apple orchards from diseases ensures a steady flow of top-notch apples for folks worldwide. This helps the Apple business grow & flourish. Pursuing technological advancements in farming, especially through computer-aided diagnosis systems, represents a significant technological breakthrough and a critical step toward ensuring the sustainability and productivity of apple farming. The goal of this research is to implement a comprehensive approach to the detection and classification of diseases in apple leaves. This proposed methodology incorporates the Wiener filter for noise removal, which boosts image clarity and adaptive thresholding for segmenting the images to find the infected areas. The VGG-16 model helps to extract the key features from the images, and XG-Boost (Extreme Gradient Boosting) is used to closely sort the different apple leaf diseases. Together, these advanced techniques have hit an impressive accuracy rate of 95.90%, showing how good this integrated approach is at boosting disease management strategies for apple orchards.

关键词： Wiener filters Thresholding (Imaging) Transfer learning Feature extraction Boosting computer aided diagnosis Sustainable development Diseases Farming Sorting

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Developing smart devices with automated Machine learning Approach: A review

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Materials Today: Proceedings 2022年 51卷 826-831页

作者： Sunil A. Patel Sanjay P. Patel Yagna Bhupendra Kumar Adhyaru Santosh Maheshwari Pankaj Kumar Mukesh Soni Assistant Professor Computer Science and Engineering Department Government Engineering College Patan Gujarat India Assistant Professor Computer Engineering Department Hansaba College of Engineering & Technology Gokul Global University Gujarat India Head Computer Engineering Department Hansaba College of Engineering & Technology Gokul Global University Gujarat India Department of Computer Science & Engineering Noida Institute of Engineering & Technology Greater Noida India Research Scholar Computer Science and Engineering Department University Bhopal 462044 India

In the current era, Artificial Intelligence is playing a major role in smart devices. Apart from incorporating intelligence in the devices, it has extended its range beyond that. Now new AI development tools such as automated machine learning (AutoML) approach are increasing rapidly because of its performance on smart devices. AutoML is used to design a machine learning model with self-adapting capability which can perform the task in less time with advanced skills. This approach does not need any explicit coding and the data science experts but this can build smart sensor codes that will get executed automatically on the microcontrollers embedded within the IoT devices. Due to these capabilities, the developers can build efficient smart devices with real-time responsiveness, security, and data privacy without the help of machine learning experts. In this paper, we will show how AutoML will be used to create smart de-vices. The objective of this paper is to develop the skills that will help to develop smart edge devices which include planning, design-ing, implementation, and the evaluation of the smart IoT applications.

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Decoding Sentiments: Harnessing the Power of NLP for Comparative Analysis of ML Algorithms

Decoding Sentiments: Harnessing the Power of NLP for Compara...

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International Conference on Computing and Machine Learning, CML 2024

作者： Varma, Nadimpallli Madana Kailash Harshini, Marisetti Madhan Mohan, R. Arora, Gagandeep Singal, Swati Student Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Department of Computer Science and Engineering Faculty of Engineering Annamalai University Chidambaram India Head of the Department Professor Department of Artificial Intelligence & Machine Learning Vardhaman College of Engineering Telangana Hyderabad501286 India Sharda University Uttar Pradesh Greater Noida India

ISBN: (纸本)9789819765874

Machine learning algorithms have become pervasive in diverse applications, revolutionizing various domains. However, the abundance of algorithms, each designed for specific purposes, poses a challenge for both novice users and experts in selecting the most suitable model. This research addresses this issue through a comprehensive analysis, leveraging Natural Language Processing (NLP) techniques and the powerful Sentiment decoding libraries. Researcher’s comments from published papers were analyzed using the various approaches, presenting a novel approach that maps algorithm scores based on adjectives. Results indicate that Support Vector Machine consistently outperforms other algorithms, providing valuable insights for practitioners. Our research contributes to a systematic evaluation framework, aiding researchers in algorithm selection. This paper presents a study focused on sentiment analysis, aimed at identifying the most effective machine learning algorithm among various contenders. Traditional conclusions regarding the superiority of specific algorithms are often based on their performance on specific datasets or tasks, which may not provide a comprehensive comparison due to the diverse nature of algorithms. To address this, a standard dataset encompassing the advantages and disadvantages of all algorithms is utilized. Three approaches employing VADER, TextBlob, and transformers are employed for sentiment analysis. Results consistently highlight SVM as the top-performing algorithm, followed by Associative analysis. This standardized conclusion contributes to the broader understanding of sentiment analysis within the realm of machine learning algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Convolutional neural networks

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