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

作者： Mohammed, Abdul Sajid Neravetla, Anuteja Reddy Nomula, Varun Kumar Gupta, Ketan Dhanasekaran, S. University of the Cumberlands Dept of School of Computer and Information Sciences KY United States University of the Cumberlands Department of Information Technology KY United States Georgia Institue of Technology Department of Analytics Industrial & Systems Engineering United States University of The Cumberlands Dept. of Information Technology WilliamsburgKY United States Sri Eshwar College of engineering Department of Electronics and Communication engineering Coimbatore641202 India

ISBN: (纸本)9798350370249

Medical selection aid structures (CDSSs) are facts systems designed to assist healthcare experts in the decision-making manner. Conventional CDSSs rely upon analytical fashions and understanding-based algorithms to guide choices. However, the latest advances have visible the emergence of AI-pushed CDSSs that utilize artificial intelligence (AI) to help clinicians in their selection-making. AI-pushed CDSSs are predicted to enhance selection-making accuracy and results by way of leveraging massive datasets and greater state-of-the-art modeling strategies. But, there is nonetheless a lot to understand about the impact of AI-driven CDSSs on healthcare shipping and results. This paper evaluates the present-day literature on the impact of AI-driven CDSSs, exploring the results of this generation for healthcare experts and the healthcare gadget as an entire. It highlights areas in need of additional research and suggests possibilities for the design and implementation of AI-pushed CDSSs. © 2024 IEEE.

关键词： Decision making

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Green UAV communications for 6G: A survey

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Chinese Journal of Aeronautics 2022年第9期35卷 19-34页

作者： Xu JIANG Min SHENG Nan ZHAO Chengwen XING Weidang LU Xianbin WANG School of Information and Communication Engineering Dalian University of TechnologyDalian 116024China State Key Laboratory of Integrated Service Networks Xidian UniversityXi'an 710071China School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China College of Information Engineering Zhejiang University of TechnologyHangzhou 310023China Department of Electrical and Computer Engineering Western UniversityLondon.ON N6A 5B9Canada

Unmanned Aerial Vehicles(UAVs) have received a wide range of attention for military and commercial applications. Enhanced with communication capability, UAVs are considered to play important roles in the Sixth Generation(6G) networks due to their low cost and flexible deployment. 6G is supposed to be an all-coverage network to provide ubiquitous connections for space, air, ground and underwater. UAVs are able to provide air-borne wireless coverage flexibly,serving as aerial base stations for ground users, as relays to connect isolated nodes, or as mobile users in cellular networks. However, the onboard energy of small UAVs is extremely limited. Thus,UAVs can be only deployed to establish wireless links temporarily. Prolonging the lifetime and developing green UAV communication with low power consumption becomes a critical *** this article, a comprehensive survey on green UAV communications for 6G is carried out. Specifically, the typical UAVs and their energy consumption models are introduced. Then, the typical trends of green UAV communications are provided. In addition, the typical applications of UAVs and their green designs are discussed. Finally, several promising techniques and open research issues are also pointed out.

关键词： Energy efficiency Green communications Sixth Generation(6G)networks Unmanned Aerial Vehicle(UAV) Wireless networks

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Robust Tweets Classification Using Arithmetic Optimization with Deep Learning for Sustainable Urban Living

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SN computer Science 2024年第5期5卷 549页

作者： Hamza, Manar Ahmed Hashim, Aisha Hassan Abdalla Motwakel, Abdelwahed Elhameed, Elmouez Samir Abd Osman, Mohammed Kumar, Arun Singla, Chinu Munjal, Muskaan Department of Electrical and Computer Engineering International Islamic University Malaysia Kuala Lumpur 53100 Malaysia Department of Computer and Self Development Preparatory Year Deanship Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Management Information Systems College of Business Administration in Hawtat bani Tamim Prince Sattam bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science College of Post-Graduated Studies University of Science and Technology Omdurman Sudan Department of Information System Faculty of Computer Science and Information Technology Omdurman Islamic University Omdurman Sudan Department of Electronics and Communication Engineering New Horizon College of engineering Bengaluru India Computer Engineering Thapar Institute of Engineering and Technology Punjab Patiala India

Natural Language Processing (NLP) with Deep Learning (DL) for Tweets Classification includes use of advanced neural network designs to analyse and classify Twitter messages. DL techniques like recurrent neural network (RNN) or transformer-based frameworks like BERT are used to mechanically learn difficult linguistic patterns and contextual info from tweet data. These techniques able to capture subtleties of language with sarcasm, sentiment, and context-specific meanings and making them suitable for tasks like sentiment analysis or topic classification in realm of social media. Leveraging deep symbols learned from great amounts of textual data, these NLP techniques permit precise and nuanced classification of tweets, donating to enhanced information retrieval, sentiment tracking, and trend analysis in dynamic and fast-paced world of social media communication. In this view, this research develops an arithmetic optimization algorithm with deep learning based tweets classification (AOADL-TC) approach for sustainable living. The goal of the AOADL-TC technique is to identify and discriminate different kinds of sentiments that exist in the tweet data. At the initial stage, the AOADL-TC model pre-processes tweet data to convert uniform data into a useful format. For sentiment detection, the AOADL-TC technique applies a parallel bidirectional gated recurrent unit (BiGRU) model. At last, tuning of parameters related to parallel BiGRU model performed by AOA. An wide set of tests carried out to illustrate better performance of AOADL-TC model. The experimental outcomes portrayed that AOADL-TC technique demonstrates the supremacy of the AOADL-TC technique in terms of different evaluation metrics. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning Machine learning Natural language processing Sentiment analysis Social media

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A study on Efficiency of Bio Medical Data Extraction from PubMed Dataset using Elastic Search Engine 2

A study on Efficiency of Bio Medical Data Extraction from Pu...

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2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2024

作者： Subashree, S. Mahalakshmi, P. Pushpa, G. Senthilkumar, S. Saranya, E. E.G.S. Pillay Engineering College Department of Computer Science and Engineering Tamil Nadu Nagapattinam611002 India Rajalakshmi Engineering College Department of Information Technology Chennai India E.G.S. Pillay Engineering College Department of Electronics and Communication Engineering Tamil Nadu Nagapattinam611002 India

ISBN: (纸本)9798350368413

Modern software applications rely heavily on search and analytics as crucial components. Extracting valuable information from big data records is mandatory for development of effective text mining models. Existing text mining models require large number of trained data but lagging due to the small datasets available. For information extraction from the literature, there is a growing need for precise text mining methods. This research study provides an idea of extracting required information from the literature and identify research trends with the help of elastic search. People can better understand life's phenomena and its rules on biological knowledge with aid of elastic search. © 2024 IEEE.

关键词： Electronic health record

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Data communication Protocol using Elliptic Curve Cryptography for Wireless Body Area Network 6

Data Communication Protocol using Elliptic Curve Cryptograph...

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6th International Conference on Computing Methodologies and communication, ICCMC 2022

作者： Madhuri, T.N.P. Rao, M. Srinivasa Santosh, P.S. Tejaswi, P. Devendra, S. Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology TS Hyderabad India Department of Computer Science and Engineering AP Visakhapatnam India Department of Electronics and Communication Engineering AP Vizianagaram India Department of Electronics and Communication Engineering AP Srikakulam India

ISBN: (纸本)9781665410281

Wireless Body Area Network plays a major role in patient health monitoring, and controlling is the best method in the present generation. Nowadays, especially the patient's status information plays a vital role and must also be securely covered. One of the major challenges is to provide secure transmission between nodes. This paper recommends that the confidentiality of patients' medical data be protected by implementing two concepts called the Diffie-Hellman key generation method for different key sizes and Elliptic Curve Cryptography (ECC). A biometric authentication system is also proposed where the biometric images are used as secret keys to authenticate legitimate users and data users like doctors and patients. This system implements an asymmetric encryption algorithm that is more effective and produces a better outcome. The focal point is to produce a better performance on the wireless domain using ECC. © 2022 IEEE.

关键词： Wireless local area networks (WLAN)

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A Cooperative Cognitive Radio Spectrum Sensing Based on Correlation Sum Method with Linear Equalization

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communications and Network 2023年第1期15卷 1-14页

作者： Entesar Gemeay Ahmed Lebda Department of Computer Engineering Computer and Information Technology College Taif University Taif KSA Department of Electronics and Communication Engineering Faculty of Engineering Tanta University Tanta Egypt

For moving forward toward the next generations of information technology and wireless communication, it is becoming necessary to find new resources of spectrum to fulfill the requirements of next generations from higher data rates and more capacity. Increasing efficiency of the spectrum usage is an urgent need as an intrinsic result of the rapidly increasing number of wireless users and the conversion of voice-oriented applications to multimedia applications. Spectrum sensing techniques in cognitive radio technology work upon an optimal usage of the available spectrum determined by the Federal communication Commission (FCC). In this paper, the performance of a cooperative cognitive radio spectrum sensing detection based on the correlation sum method by utilizing the multiuser multiple input multiple output (MU_MIMO) technique over fading and Additive White Gaussian Noise (AWGN) channel is analyzed. Equalization is used at the receiver to compensate the effect of fading channels and improve the reliability of spectrum sensing. The performance is compared with the performance of Energy detection technique. The simulation results show that the detection performance of cooperative correlation sum method is more efficient than that obtained for the cooperative Energy detection technique.

关键词： Spectrum Sensing Cognitive Radio MIMO Equalization

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Analysing Forecasting of Stock Prices: An Explainable AI Approach

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Procedia computer Science 2024年 235卷 2009-2016页

作者： Priyanshu Kumar Lopamudra Hota Vinay Anand Tikkiwal Arun Kumar Department of Computer Science and Engineering National Institute of Technology Rourkela 769008 India Department of Electronics and Communication Engineering Jaypee Institute of Information Technology Noida 201304 India

Predicting stock prices is a well-known and significant problem. We can learn about market behaviour over time and identify trends that might not have been seen without an effective stock prediction model. Machine learning will be a useful approach to solving this issue with the increased processing capacity of computers. Behavioural economics also asserts that the investments made by investors depend on their emotions so psychological theories can also be applied to explain their behaviour and its impact on the market. Combining the analysis of these behavioural patterns with the use of historical financial data sets can result in an approach for accurate stock price predictions. The primary focus of the proposed model is on comparing different models to provide a comparative analysis of the results provided by models used in the literature. The paper provides insight into the improvisation of the current techniques and how different parameters and different error analysis techniques can be implemented.

关键词： Stock Market Deep Learning XAI Forecasting Time-Series

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C2R: A mechanism for Coverage and Connectivity, Hole Recognition and Repairment in Wireless Sensor Network

C2R: A mechanism for Coverage and Connectivity, Hole Recogni...

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2022 IEEE International Conference on Knowledge engineering and communication Systems, ICKES 2022

作者： Rath, Rama Krushna Kondaveeti, Hari Kishan Nanda, Rupalin Pattnaik, Shrinibas Kumar Satapathy, Santosh Sri City Department of Computer Science and Engineering Chittoor India School of Computer Science Engineering VIT-AP University Vijayawada India College of Engineering Guindy Anna University Department of Electronics and Communication Engineering Chennai India Gandhi Institute of Science and Technology Department of Electronics and Communication Engineering Odisha Rayagada India Department of Information and Communication Technology Gandhinagar India

ISBN: (纸本)9781665456371

A Wireless Sensor Network (WSN) is a collection of sensor nodes, base station, and sink. Sensor nodes are the primary component of a sensor network as they are responsible for collecting the environmental conditions and transmit. The data of Region of Interest (RoI). There are two significant challenges of wireless sensor nodes: 1. Coverage and Connectivity, and 2. Energy Optimization. The second challenge is countered by the optimization of routing algorithms and Wake and sleep schedule of the sensor nodes. Whereas many issues in coverage and connectivity of a Wireless Sensor Network problems remain unsolved. One of the issues is the identification of holes and the process of hole healing to deal with coverage and connectivity. Holes in a sensor network are of many types;such as coverage holes, routing holes, Jamming holes, wormholes, etc. In this research, the coverage holes that make sensor networks inefficient is addressed. The proposed mechanism has the following steps: In the first step, sensor coordinates are extracted and then intra-network distances are calculated. In the second phase, regions without adequate working sensors are identified as holes;each hole's centroid is calculated. In the third step, the target node within the hole range is selected for hole healing. Further, the holes within the network area are considered for our simulation. © 2022 IEEE.

关键词： Sensor nodes

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A CH9 Low-Profile Circularly-Polarized Antenna for Indoor Positioning 7

A CH9 Low-Profile Circularly-Polarized Antenna for Indoor Po...

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7th World Conference on Computing and communication Technologies, WCCCT 2024

作者： Zhang, Zhenfeng Feng, Botao Gu, Wenzhe Ding, Xiao Sun, Sanshan Deng, Li Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Huizhou University School of Computer Science and Engineering Huizhou516007 China Macau University of Science and Technology Faculty of Humanities and Arts 999078 China Sichuan Normal University College of Physics and Electronic Engineering Sichuan China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China

ISBN: (纸本)9798350372328

This paper presents a low-profile CH9 circularly polarized 3-element multiple input multiple output (MIMO) antenna for precise positioning applications. The feeding structure of the antenna employs a conventional wilkinson power divider to achieve circular polarization, where the feeding structure and the radiating patch are located on the same layer of the dielectric substrate with a grounded backside. The -10-dB bandwidth of each antenna element ranges from 7.04 to 8.55 GHz, with a gain larger than 2.21 dBi within the desired bandwidth. In order to enhance localization accuracy and minimize the impact of spatial correlation, the distance interval between antenna elements is designed to be 12.5 mm. Experimental results show that when ports 1, 2 and 3 are excited with equal amplitude phase differences of 0 °, 90 ° and -90 °, respectively, the frequency bandwidth of the MIMO antenna is ranging from 7.61 to 8.48 GHz, with corresponding gain fluctuating from 5.39 to 8.07 dBi. In addition, the circularly polarized bandwidth is from 7 to 8.89 GHz, which fully covers the CH9 frequency band. © 2024 IEEE.

关键词： Bandwidth

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Deep Learning Model based IDS for Detecting Cyber Attacks in IoT based Smart Vehicle Network 2

Deep Learning Model based IDS for Detecting Cyber Attacks in...

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2nd International Conference on Sustainable Computing and Data communication Systems, ICSCDS 2023

作者： Anand, M. Kumar, S. Praveen Selvi, M. Santhosh Kumar, S.V.N. Ram, G. Dinesh Kannan, A. Saveetha Engineering College Department of Electronics and Communication Engineering Thandalam Chennai India Vellore Institute of Technology School of Computer Science and Engineering Vellore India Vellore Institute of Technology School of Information Technology and Engineering Vellore India College of Engnieering Guindy Anna University Department of Information Science and Technology India

ISBN: (纸本)9781665491990

The Internet of Vehicle (IoV) is a technology that connects vehicles such as cars and buses to the internet. It facilitates the exchange of information between these vehicles for increased safety, efficiency, and speed. The IoV architecture consists of three layers: perception, network, and application. The main challenge in the design of IoT is the security issue. To handle the security issue we proposed an Intrusion Detection System (IDS) that uses deep learning models, specifically deep neural networks, to identify malicious network traffic and to classify the type of attack. The system operates in two stages: in the first stage, normal and attack traffic are differentiated, and in the second stage, the type of attack is determined. Three variants of rule extraction methods are proposed here including homogeneous methods using either decompositional or pedagogical rule extraction in both stages, and a heterogeneous method using pedagogical for binary classification and decompositional for attack classification. The goal is to achieve improved classification accuracy while reducing resource consumption. The proposed IDS was tested using car hacking datasets for both external and in-vehicle communications. The results indicate that the deep learning based decompositional with pedagogical method achieved the best results in all cases of the IDS system with high accuracy under the Car-hacking dataset. © 2023 IEEE.

关键词： Vehicles

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