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Communications and Information technologies (GCCIT), Global Conference on

作者： K. Yuvaraj Ayush Gandhi Ankur Singh Bisht P. Chandrakala Kiran Lokesh Maney Dattatraya Babanrao Nalawade Department of Computer Science Karpagam Academy of Higher Education Coimbatore Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Computer Science and Engineering Graphic Era Hill University Bhimtal Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management - UG Management JAIN Deemed to be University Bangalore Karnataka Mech Engg Vishwakarma Institute of Technology Pune India

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

The stability and security of a network depends on the prompt and precise identification of intruders. Conventional machine learning and deep learning technologies are the backbone of current network intrusion detection algorithms, which categorize intrusions according to statistical characteristics of network flows. Since feature extraction is dependent on expert knowledge, it cannot be executed until network flows have concluded, postponing intrusion detection. The current approaches of graph-based intrusion detection are laborious and complicated since they rely on worldwide network traffic to build communication graphs. In addition, current intrusion detection systems that rely on graphs or deep learning need very large training sets. This study presents Graph2vec+RF, a graph embedding technology-based approach to network intrusion detection that is both early and accurate. Instead, we use graph2vec, an embedding technique, to learn the flow graph’s vector representation, form a flow graph from the first few interaction packets for every bidirectional network flow, and then use Random Forest (RF) to categorize the graph vectors. With Graph2vec+RF, you may perform early and effective network intrusion detection utilizing a small number of the first interactive packets per bidirectional system flow. It automatically extracts flow graph characteristics using subgraph structures and doesn’t need enormous training sets. Our suggested Graph CNN with SVM achieves better accuracy, recall, precision, as well as F1-score than the state-of-the-art approaches, as shown by our experimental findings on the CIC-IDS2017 as well as UNSW-NB15 datasets.

关键词： Training Deep learning Support vector machines Accuracy Network intrusion detection Telecommunication traffic Vectors Data models Flow graphs Random forests

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An Evaluation of the Impact of AI on Data Access Optimization Performance

An Evaluation of the Impact of AI on Data Access Optimizatio...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Vijaykumar R. Ghule Ankur Kumar Agrawal Jambi Ratna Raja Kumar Dipannita Mondal S. Hemelatha Preetha. M Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Department Of Management Mangalayatan University Aligarh India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Artificial Intelligence And Data Science Department Dr. D.Y. Patil College Of Engineering And Innovation Talegaon Department of Computer Science Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai

The emergence of synthetic intelligence (AI) has substantially revolutionized the way records is accessed and optimized. With AI-powered equipment and algorithms, groups are capable of efficiently method and manipulate large amounts of data, main to upgrades in overall performance and decision-making. This paper pursuits to evaluate the impact of AI on statistics get entry to optimization performance, discussing both the potential blessings and challenges that include its integration. One of the major blessings of using AI for information get entry to optimization is its capability to automate and streamline obligations. AI-powered structures can analyze and categorize massive datasets at a quicker and extra correct rate than human beings, liberating up time and assets for different obligations. This results in stronger efficiency and productivity in statistics control methods. Further, AI also can enhance the accuracy and best of facts access. thru system learning and predictive analytics, AI structures can identify styles and correlations in information, providing treasured insights for decision-making. This now not simplest improves the speed at which records is accessed, however also the reliability and relevance of the data. However, there are also demanding situations associated with the combination of AI in facts get admission to optimization. one of the fundamental worries is the potential bias in algorithms, which can result in erroneous or discriminatory selection-making. it's far vital for groups to carefully display and review the algorithms utilized by AI structures.

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Next-Gen Telehealth Ecosystems Using IoT for Virtual Consultations and Support

Next-Gen Telehealth Ecosystems Using IoT for Virtual Consult...

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International Conference on Communications and Signal Processing

作者： A Kathija Nasreen S. Priya Nukala Sujata Gupta L K Shoba N. Mohankumar S Arunkumar Department of Computer Science and Engineering RMK College of Engineering and Technology Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Academy of Maritime Education and Training (AMET) Deemed to be University Chennai Tamil Nadu India Department of Computer Engineering and Technology Chaitanya Bharathi Institute of Technology Hyderabad Telangana India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chenagalpattu Chennai Tamil Nadu India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Maharashtra India Department of Computer Applications Adhiparasakthi Engineering College Melmaruvathur Tamil Nadu India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

The research discussed focuses on improving virtual medical consultations and support using Internet of Things (IoT) technologies in telehealth services. It is predicted that IoT devices can assist with creating a new telehealth environment where patients and doctors can communicate easily for remote monitoring, diagnosis, and treatment. The main objective of this unique approach is to expand access to healthcare, especially in poor and rural areas. Connected medical equipment, smart sensors, and wearable technology all play an important role in this system. These IoT components enable gathering real-time and complete patient data, helping doctors make better, more individualized choices and care plans. Data safety, privacy, and system compatibility are some areas where this integration hits a snag. Sensitive patient data and the integrity of the IoT-enabled systems need robust safety measures. These IoT improvements in telemedicine have had a revolutionary effect. Proactive interventions, continuous health monitoring, and individualized care are ways patients might benefit from improved medical care. Virtual consultations are increasingly used in the medical field, and a more connected and data-driven framework for the patient experience is emerging.

关键词： Data privacy Technological innovation Telemedicine Ecosystems Medical services Real-time systems Safety

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Reliable Densely Connected Network with Machine Learning based Diabetic Retinopathy Grading Approach 1

Reliable Densely Connected Network with Machine Learning bas...

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1st IEEE International Conference on Automation, Computing and Renewable Systems, ICACRS 2022

作者： Sirisala, Srinivasulu Behera, Nihar Ranjan Amuthaguka, D. Chaudhari, Sachin Vasant Prasad, Jhakeshwar Swetha Reddy, A. Department of Computer Science and Engineering CVR College of Engineering Telangana Hyderabad501510 India Swiss School of Business and Management Geneva Av. des Morgines 12 Petit-Lancy1213 Switzerland Department of Computer Applications Kalasalingam Academy of Research and Education Tamilnadu Krishnankoil626126 India Department of Electronics & Computer Engineering Sanjivani College of Engineering Kopargaon423601 India Department of Pharmacology Shri Shankaracharya College of Pharmaceutical Sciences Junwani Chhattisgarh Bhilai490020 India Department of Electronics and Communication Engineering R.M.K. College of Engineering and Technology Thiruvallur District Tamilnadu Puduvoyal601206 India

ISBN: (纸本)9781665460842

Lately, diabetic retinopathy (DR) is deliberated to be the major cause of blindness in today's world. There are various reasons for loss of eyesight but DR has greatly contributed to blindness. The main challenge of DR is that it is incurable at an early stage, therefore basic analysis is of great significance. Consequently, it is mentioned as a classic difficulty in medical schemes due to minimum number of medicinal experts and facilities and massive patients. Accordingly, it resulted in deploying automated diagnosing methodology that is very beneficial for the analysis of DR. With this aspect, this article develops a reliable Densely Connected Network with Machine Learning based Diabetic Retinopathy Grading Approach, called RDCN-MLDR. The presented RDCN-MLDR model follows two major stages namely feature extraction and DR classification. Initially, the DenseNet model processes the input fundus images. In addition, squirrel search algorithm (SSA) is applied for the hyperparameter tuning of the DenseNet model. As soon as the features are created, they are fed to the LR module for proper identification of DR. The performance assessment of the RDCN-MLDR model is tested using open access dataset from Kaggle dataset and it produces the 97.8% accuracy. The attained outcomes highlighted that the RDCN-MLDR model can accomplish enhanced DR classification performance. © 2022 IEEE

关键词： Convolutional neural networks

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LSTM-Based Jamming Diagnosis and Prediction Model Utilizing Transport in Wi-Fi-Enabled IoT Systems

LSTM-Based Jamming Diagnosis and Prediction Model Utilizing ...

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Communications and Information technologies (GCCIT), Global Conference on

作者： Wamika Goyal Vikrant Sharma K Karthika Raghu G Anand Y.V. Dongre K. Ranjith Singh Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Computer Science and Engineering Graphic Era Hill University Dehradun Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai School of Management -UG Management JAIN Deemed to be University Bangalore Karnataka Comp Vishwakarma Institute of Technology Pune India Department of Computer Science Karpagam Academy of Higher Education Coimbatore

ISBN: (数字)9798350388916

ISBN: (纸本)9798350388923

Every industry is seeing a rise in the use of the Internet of Things (IoT). Because of its widespread availability, high bandwidth, low cost, and adaptability, Wi-Fi-based communication is favored by wireless IoT devices. However, serious dangers exist due to security flaws in IoT devices, particularly in mission-critical and industrial settings. With an emphasis on industrial communication and environmental dynamics, this article introduces a new method for detecting jamming in the Internet of Things (IoT). Using production lines that include wireless IoT devices with a server-client architecture, it first focuses on collecting connection parameters and quality of service (QoS) during normal communication and jamming attacks so that it can more effectively investigate the communication conditions along with jamming in the industrial production environment. Results from experiments show that spot jamming has a devastating effect on higher-level protocol layers, showing that physical-layer jamming assaults may cause transport and application-layer failures to cascade. This research introduces a novel multi-layer jamming detection model based on stacked Long Short-Term Memory (LSTM). With its strong performance in real-time applications for IoT and impressive accuracy in anticipating jamming attempts, the suggested LSTM model is a clear winner. The model undergoes testing on a real-time experimental testbed that simulates different sorts of jamming assaults after being trained on a dataset that has been meticulously selected. A 99.5% accuracy rate for jamming detection and a 99.4% precision rate were produced by the findings. This study provides new insight into the effects of jamming assaults on Internet of Things (IoT) devices and introduces a sophisticated detection model that has the potential to reduce the severity of these attacks.

关键词： Wireless communication Accuracy Quality of service Production Real-time systems Data models Internet of Things Jamming Long short term memory Wireless fidelity

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Bayesian Optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction Model 5

Bayesian Optimization with Stacked Sparse Autoencoder based ...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Shaikh, Irfan Abdul Karim Krishna, P. Vamsi Biswal, Swagat Gourav Kumar, A. Sasi Baranidharan, S. Singh, Kamlesh Faculty of Business Bahrain Polytechnic Isa Town Bahrain School of Management Malla Reddy University Telangana Hyderabad India Odisha University of Tecnology and Research Department of Electrical Engineering Orissa Bhubaneswar India Institute of Engineering and Technology Srinivas University Mentor-IT-Inurture Education Solutions Pvt Ltd Bangalore Department of Cloud Technology and Data Science Karnataka Dakshina Kannada India Department of Mba and Research Center Karnataka Bangalore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun Campus Dehradun India

ISBN: (纸本)9781665474672

Digital currency is a way of currency utilized in the digital world namely electronic devices or digital forms. Many terms are alternative words for digital currency such as cyber cash, digital money, and electronic money. Cryptocurrency is a type of asset that has developed due to the progression of financial technology and it has made a tremendous chance for research workers. Cryptocurrency price prediction is challenging because of the dynamism and price volatility. The electronic economy is severely hazardous and should be advanced with greater caution, to minimize or avoid the risk that occurs in this case. Therefore, this study develops a new Bayesian optimization with Stacked Sparse Autoencoder based Cryptocurrency Price Prediction (BOSSAE-CPP) model. The major intention of the BOSSALCPP technique lies in the effectual prediction of cryptocurrency prices. To attain this, the BOSSAE-CPP technique exploits SSAE model for price prediction process. Moreover, the BO technique is used to optimally choose the hyperparameter values of the SSAE model and results in enhanced predictive outcomes. To deliberate the enhanced outcomes of the BOSSALCPP technique, extensive experimentation study is made. The comparison study highlighted the improved performance of the BOSSAE-CPP technique. © 2023 IEEE.

关键词： Electronic money

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Strengthening Mobile Cloud Facilities: Techniques and Prospective Perspectives

Strengthening Mobile Cloud Facilities: Techniques and Prospe...

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2024 International Conference on Communication, Computing and Energy Efficient technologies, I3CEET 2024

作者： Soni, Anita Kuppuraj, T. Almusawi, Muntather Udayakumar, R. Bansal, Saloni Sharma, Shavnam Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering India Karpagam Academy of Higher Education Department of Computer Applications Coimbatore641 021 India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq The Islamic University of Al Diwaniyah College of Technical Engineering Department of Computers Techniques Engineering Al Diwaniyah Iraq Kalinga University India Gla University Department of Computer Engineering and Applications Mathura India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Jhanjeri Punjab Mohali140307 India

ISBN: (纸本)9798331541583

Despite their quick surge in popularity and technical developments, mobile devices' limited battery life and computing power provide a serious barrier to meeting the growing needs of mobile consumers. In order to bridge the gap between customer expectations and the capabilities of mobile phones and tablets, a combination of well-established processes and cutting-edge approaches is necessary. Lately, efforts in research have been directed on using augmentation methods to improve mobile device performance. In the field of mobile cloud computing, augmentation strategies seek to maximize energy economy and processing capability via task delegation to more powerful computing resources. However, when using augmentation techniques, the heterogeneous and interrupted nature of portable internet-based computing architectures creates complications pertaining to computational management, energy saving, and system dependability. We also provide a summary of open problems and directions that might be explored in this area in the future. © 2024 IEEE.

关键词： Service oriented architecture (SOA)

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An enhanced Walrus Optimizer with opposition-based learning and mutation strategy for data clustering

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Array 2025年 26卷

作者： Abualigah, Laith Alomari, Saleh Ali Almomani, Mohammad H. Zitar, Raed Abu Migdady, Hazem Saleem, Kashif Smerat, Aseel Snasel, Vaclav Ezugwu, Absalom E. Computer Science Department Al al-Bayt University Mafraq25113 Jordan Faculty of Science and Information Technology Jadara University Irbid21110 Jordan Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Faculty of Engineering and Computing Liwa College Abu Dhabi United Arab Emirates CSMIS Department Oman College of Management and Technology Barka320 Oman Department of Computer Science & Engineering College of Applied Studies & Community Service King Saud University Riyadh11362 Saudi Arabia Faculty of Educational Sciences Al-Ahliyya Amman University Amman19328 Jordan Computer Technologies Engineering Mazaya University College Nasiriyah Iraq Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava Poruba-Ostrava 70800 Czech Republic Unit for Data Science and Computing North-West University 11 Hofman Street Potchefstroom2520 South Africa

Data clustering plays a crucial role in various domains, such as image processing, pattern recognition, and data mining. Traditional clustering techniques often suffer from limitations like sensitivity to initialization, poor convergence, and entrapment in local optima. To address these challenges, this paper proposes an Enhanced Walrus Optimizer (IWO) tailored for clustering tasks. The proposed IWO integrates two powerful strategies–Opposition-Based Learning (OBL) and Mutation Search Strategy (MSS)–to improve population diversity and prevent premature convergence, thereby enhancing both exploration and exploitation capabilities. These enhancements enable more accurate and stable identification of cluster centers. The effectiveness of IWO is validated through extensive experiments on multiple benchmark clustering datasets and compared against several state-of-the-art metaheuristic algorithms, including PSO, GWO, AOA, and others. The results demonstrate that IWO achieves better results, indicating improved compactness and separation of clusters. Statistical validation using p-values and ranking scores further confirms the superiority of the proposed method. These findings suggest that IWO offers a robust and flexible framework for solving complex clustering problems. Future work will explore hybrid deep learning-integrated models and parallel implementations to enhance scalability. © 2025

关键词： Swarm intelligence

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The Role of Blockchain Integration in Edge Computing for 6G IoT: Challenges and Applications

The Role of Blockchain Integration in Edge Computing for 6G ...

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International Conference on Network Applications Protocols and Services (NETAPPS)

作者： Abdulwadood Alawadhi Abdullah Almogahed Bushra Abdullah Shtayt Samera Obaid Barraood Mohammad Ibrahim Adawy Omar Al-Jamili InterNetWorks Research Laboratory School of Computing Universiti Utara Malaysia Sintok-Kedah Malaysia Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Johor Malaysia Information Technologies Management Management Technical College of Basra Southern Technical University Basra Iraq Department of Computer Science College of Computers and Information Technology Hadhramout University Hadhramout Yemen Faculty of Information Technology The World Islamic Sciences and Education University Amman Jordan School of Computing Universiti Utara Malaysia Sintok-Kedah Malaysia

ISBN: (数字)9798331519513

ISBN: (纸本)9798331519520

Blockchain is the technology on which cryptocurrencies operate within the 6G Internet of Things. Due to its flexibility, it has received significant adoption in numerous applications as a possible distributed data management solution. Nonetheless, a significant obstacle that impedes blockchain's potential is its constrained scalability, which limits its capacity to facilitate applications requiring frequent transactions. Conversely, edge computing, an innovative technology, addresses the limitations of conventional cloud-based systems by decentralizing cloud functionalities and alleviating problems such as elevated latency and insufficient security. Nonetheless, edge computing presently encounters challenges related to decentralized governance and security. As a result, the integration of blockchain and edge computing within a cohesive framework offers substantial benefits, such as dependable network access and governance, decentralized storage, and computing at the peripheries. This integration effectively mitigates many security problems and guarantees data integrity in edge computing contexts. This article offers a comprehensive analysis of blockchain technology, its attributes, and its relevance in the context of 6G. Additionally, it investigates the amalgamation of blockchain and edge computing, analyzes diverse application cases, and addresses the related issues. Finally, the report delineates contemporary research trajectories and draws conclusions based on the findings.

关键词： 6G mobile communication Cloud computing Protocols Scalability Data integrity Blockchains Trajectory Security Internet of Things Edge computing

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Role of Quantum Cryptography in Securing Communication Channels in Industry 4.0

Role of Quantum Cryptography in Securing Communication Chann...

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Advances in computer science, Electrical, Electronics, and Communication technologies (CE2CT), International Conference on

作者： Sonal Pramod Patil Vikram Nattamai Sankaran Kalpit Lamsoge Poovendran Alagarsundaram Eric Howard Prasanna Guduru G H Raisoni College of Engineering and Management Pune India Innovation Consultant Nagpur Maharashtra India Humetis Technologies Inc Kingston NJ USA Department of Physics and Astronomy Macquarie University Australia Department of Computer Science National Sanskrit University Tirupati Andhra Pradesh India

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

In the context of Industry 4.0, which is characterised by networked systems, Internet of Things devices, and cyber-physical networks, quantum cryptography is essential to strengthening communication channels. In contrast to traditional cryptography techniques, quantum cryptography uses quantum physics concepts, notably quantum key distribution (QKD), to provide unmatched security. By taking advantage of the quantum property of superposition and measurement-induced disruption, QKD offers tamper-evident key exchanges, protecting data from efforts at eavesdropping. This guarantees that any communication interception causes system disruption and warns users of possible dangers. Quantum cryptography reduces the risks associated with traditional encryption techniques in Industry 4.0, as sensitive data moves across automated systems, smart factories, and decentralised supply chains. This is especially true as quantum computing advances. Industries can attain strong data integrity, confidentiality, and resistance against new cyberthreats by incorporating quantum-secured channels, opening the door for a dependable and safe digital transformation. This presentation highlights how quantum cryptography is revolutionising the security paradigms of Industry 4.0.

关键词： Costs Scalability Supply chains Communication channels Communications technology Fourth Industrial Revolution Communication networks Quantum cryptography Research and development Smart manufacturing

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