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8th International Conference on ICT for Sustainable Development, ICT4SD 2023

作者： Sudarsan Rao, V. Satyanarayana, N. Department of Computer Science and Engineering Sri Chaitanya Institute of Technology and Research Telangana Khammam India Department of Information Technology TKR College of Engineering and Technology Telangana Hyderabad India

ISBN: (纸本)9789819956517

Cloud computing permits customers with constrained computational capability and communication bandwidth to economically and feasibly outsource their massive-sized computations and operations to a public-oriented cloud having large-scale computation capacity. Cloud possesses the large storage, computational capability as well as software program which may be utilized by customers for the reduction of their overhead in terms of computing. Though, in the case scenario of the outsourcing, the privacy of customer’s private data must be preserved. In this research work, we developed a methodology for the outsourcing big-scale eigen count value computational threat to a malevolent-natured cloud that gives input/output information security, verifiability of results, and empirical performance. As far as the calculation approach to discover all the eigenvectors is of computationally high cost for huge-dimensional input data, we have utilized energy-iterative procedure for finding out the biggest eigenvalues well as the relatively corresponding eigen set vector of a certain matrix. In order to save the primitiveness, certain modifications are carried out to the certain input matrix in order to obtain its encrypted matrix that is transferred to the virtual cloud and further perform decryption of the result which is obtained back from the specific cloud entity for obtaining the accurate answer of eigen cost problem. We also provided end-result verification scientific mechanism to detect dishonest entities and supplied theoretical evaluation as well as experimental evaluation finding that explains excessive efficiency, feasibility, correctness, protection, and robust dishonest impending of the given protocol. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Outsourcing

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Chronic Disease Prediction after Covid-19 using Machine Learning 14

Chronic Disease Prediction after Covid-19 using Machine Lear...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Suresha, D. Prakash, H.N. Padmaja Devi, G. Arjun, B.C. Aravinda, C.V. Information Science and Engineering AJ institute of Engieering & Technology Karnataka Mangalore India Computer Science and Engineering Rajeev Institute of Technology Karnataka Hassan India Electronics and Communication Engineering Malnad College of Engineering Hassan India Information Science and Engineering Rajeev Institute of Technology Karnataka Hassan India Computer Science and Engineering N.M.A.M. Institute of Technology Karnataka Karkala India

Chronic Sickness Expectation framework utilizing artificial intelligence(AI) makes precise forecasts about the illness in view of the information/symptoms that the client goes into the framework. In the event that the patient isn't a lot of serious and the client simply needs to know the kind of sickness, client has had to deal with. A framework offers clients guidance on the most proficient method to keep their wellbeing frameworks with everything looking great and a strategy to distinguish illnesses utilizing this expectation. Today, the wellbeing area assumes a huge part in treating patients' sicknesses, so this framework is likewise useful to the area in illuminating the client and is gainful to the client in the event that the person decides not to visit an emergency clinic or other facility. By entering symptoms and other appropriate data, the client can realize what sickness they are at present encountering, and the area can likewise acquire from this framework by essentially requesting symptoms. This Chronic Sickness Expectation Utilizing AI is totally finished with the assistance of AI and Python Programming language and furthermore utilizing the dataset Persistent Illness Pointers that we will anticipate the infection. © Grenze Scientific Society, 2023.

关键词： Forecasting

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Optimizing mmWave UAV Networks: Mobility-Aware Deployment and Resource Allocation 100

Optimizing mmWave UAV Networks: Mobility-Aware Deployment an...

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100th IEEE Vehicular technology Conference, VTC 2024-Fall

作者： Li, Sicong Yoshii, Kazutoshi Shimamoto, Shigeru Ho, Tu Dac Waseda University Dept. of Communications and Computer Engineering Tokyo Japan Norwegian University of Science and Technology Dept. of Information Security and Communication Technology Trondheim Norway

ISBN: (纸本)9798331517786

Application of Unmanned Aerial Vehicles (UAVs) as small airborne base stations is gradually becoming a research hotspot in the field of wireless communications. With the flexible deployment, wide coverage and low cost, UAV communications show great potential in the fields of emergency rescue, environmental monitoring and military operations. In particular, the easier availability of line-of-sight (LOS) links for UAVs makes its combination with millimetre wave (mmWave) technology significantly enhance the communication bandwidth and data transfer rate. However, the very limited coverage of mmWave communications greatly affects the actual data rates provided by small aerial base stations of mmWave UAVs, especially considering user movement on the ground. In this paper, we study the joint optimisation problem of UAV deployment, user association, spectrum resource allocation in millimetre-wave heterogeneous networks, and explore the impact of different user movement models on system performance. Given that this problem is NP-hard, our paper proposes an MCR-SAC method to maximize the network's total throughput. Simulation results demonstrate an average 18% improvement in total transmission rate compared to the conventional. © 2024 IEEE.

关键词： Unmanned aerial vehicles (UAV)

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Group decision making based on advanced interval-valued intuitionist fuzzy weighted averaging aggregation operator and score function of interval-valued intuitionist fuzzy values

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information Sciences 2023年 624卷 908-923页

作者： Kumar, Kamal Chen, Shyi-Ming Department of Mathematics Amity University Haryana Gurugram India Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan

This paper proposes a new group decision making (GDM) approach in the environments of interval-valued intuitionistic fuzzy values (IVIFVs). Firstly, we propose a new score function of IVIFVs, where the proposed score function of IVIFVs can overcome the drawbacks of the existing score function of IVIFVs. The properties of the proposed score function of IVIFVs are also presented. Then, we propose the advanced interval-valued intuitionist fuzzy averaging (AIVIFA) aggregation operator of IVIFVs. We also provide the proofs of the properties of the proposed AIVIFA aggregation operator. Then, we propose the advanced interval-valued intuitionist fuzzy weighted averaging (AIVIFWA) aggregation operator of IVIFVs. Then, we propose a new GDM approach based on the proposed AIVIFWA aggregation operator of IVIFVs and the proposed score function of IVIFVs. Finally, we apply the proposed GDM approach to deal with a real-world application of cloud service selection. The proposed GDM approach can overcome the drawbacks of the existing GDM approach, which is unable to distinguish the ranking orders of alternatives in some situations. The proposed GDM approach gives us a very useful way to deal with GDM problems in the environments of IVIFVs. © 2022 Elsevier Inc.

关键词： Mathematical operators

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Covertvasion: Depicting threats through covert channels based novel evasive attacks in android

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International Journal of Intelligent Networks 2023年第1期4卷 337-348页

作者： Gautam, Sunil Pattani, Ketaki Zuhair, Mohd Rashid, Mamoon Ahmad, Nazir Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad Gujarat India Department of Computer Sciences and Engineering Institute of Advanced Research Gujarat Gandhinagar India School of Information Communication and Technology Bahrain Polytechnic Isa Town Bahrain Department of Information System College of Applied Sciences King Khalid University P.O. Box 61913 Muhayel63317 Saudi Arabia

Privacy and security issues concerning mobile devices have substantial consequences for individuals, groups, governments, and businesses. The Android operating system bolsters smartphone data protection by imposing restrictions on app behavior. Nevertheless, attackers conduct systematic resource analyses and divert privacy-sensitive information from plain view. They employ evasive mechanisms to evade system monitoring and create an illusion of benign and non-sensitive communication. Furthermore, covert channels amplify the impact of these malicious activities by facilitating information transfer through non-standard methods. The purpose of this research is to shed light on these novel threats targeting Android systems. The study delves into security and privacy attacks that compromise sensitive user information. The methodology leverages evasion concepts and employs sound-specific covert channel communication, particularly ultrasonic channels. This research work introduces novel evasive attacks, namely Prime-Composite Evasive information Invasion (PCEII) and File-lock-based Evasive information Invasion (FEII), both relying on covert channel communication. These unique variants of attacks successfully evade user data within a few milliseconds for both noisy as well as non-noisy environments and do not show any signs of detection by antivirus mechanisms like Anti-Virus Guard (AVG), 360 security, etc. and state-of-the-art tools such as TaintDroid, MockDroid and others. The paper not only assesses their impact on the privacy and security of information but also introduces avenues for their detection and mitigation. © 2023 The Authors

关键词： Android (operating system)

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D2R: Dual-Branch Dynamic Routing Network for Multimodal Sentiment Detection

D2R: Dual-Branch Dynamic Routing Network for Multimodal Sent...

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2024 Conference on Empirical Methods in Natural Language Processing, EMNLP 2024

作者： Chen, Yifan Li, Kuntao Mai, Weixing Wu, Qiaofeng Xue, Yun Li, Fenghuan Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Electronics and Information Engineering South China Normal University China School of Computer Science and Technology Guangdong University of Technology China

ISBN: (纸本)9798891761643

Multimodal sentiment detection aims to classify the sentiment polarity of a given image-text pair. Existing approaches apply the same fixed framework to all input samples, lacking the flexibility to adapt to different image-text pairs. Furthermore, the interaction patterns of these methods are overly homogenized, limiting the model's capacity to extract multimodal sentiment information effectively. In this paper, we develop a Dual-Branch Dynamic Routing Network (D2R), which is the first multimodal dynamic interaction model towards multimodal sentiment detection. Specifically, we design six independent units to simulate inter- and intra-modal information interactions without depending on any existing fixed frameworks. Additionally, we configure a soft router in each unit to guide path generation and introduce the path regularization term to optimize these inference paths. Comprehensive experiments on three publicly available datasets demonstrate the superiority of our proposed model over state-ofthe-art methods. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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Analyzing Risk Elements in Cardiovascular Diseases Prediction using Neural Networks Algorithm 10

Analyzing Risk Elements in Cardiovascular Diseases Predictio...

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17th INDIACom;2023 10th International Conference on Computing for Sustainable Global Development, INDIACom 2023

作者： Yadav, Dhyan Chandra Pal, Saurabh Yadav, Rakesh Kumar Pant, Hemlata J S University Department of Computer Science Shikohabad India Vbsp University Department of Computer Applications Jaunpur India Maharishi University of Information Technology Department of Computer Science and Engineering Lucknow India Ambalika Institute of Management and Technology Department of Information Technology Lucknow India

ISBN: (纸本)9789380544472

Globally, cardiovascular disease is the leading cause of death. In CVDs, the heart is a vital organ for supplying blood to other body organs. Complex datasets with heart problems are predicted by machine learning methods. Analysts and experts in diagnosis can easily locate reports of data on CVDs at the primary level. In the study, we investigate the performance of neural networks when used on datasets for CVD prediction. The performance of the method is assessed based on the characteristics of the dataset using Random Forest and Extra Tree feature selection strategies with the following basic analyses: accuracy score, precision, recall, f1-score, and roc. Our study highlights the critical properties of CVD datasets in order to determine which accuracy measures work best for predicting and diagnosing CVD. Our main findings were predictive Neural Network performance with an accuracy metric based on Random Forest and Extra Tree, highlighting key features for CVD diagnosis and prediction. Finally, a neural network using the Extra Tree features selection strategy achieved 97% accuracy, while a neural network using Random Forest achieved 98% accuracy. These can be effectively used to improve current CVD diagnosis efforts and help patients all around the world. Especially in places where there is a scarcity of medical professionals. © 2023 Bharati Vidyapeeth, New Delhi.

关键词： Forecasting

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Opportunistic Relay Strategy for Body Area Networks

Opportunistic Relay Strategy for Body Area Networks

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Wu, Hongbo Jia, Yukuan Yan, Jintao Zhou, Sheng Niu, Zhisheng Chang, Zheng Beijing National Research Center for Information Science and Technology Department of Electronic Engineering Tsinghua University Beijing China School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350350548

In wireless body area networks (WBANs), the deep channel fading between the nodes and the hub significantly impairs the reliability of end-to-end signal transmission. However, some nodes in WBANs necessitate high-priority data transmission with stringent latency and accuracy requirements. Retransmission is ineffective against channel fading and can result in increased communication overhead and extended transmission delays. This paper proposes an opportunistic relay strategy tailored to the characteristics of WBAN nodes with varying priorities. This strategy converts suitable low-priority nodes as relays during high-priority time slots to forward high-priority data to the hub when deep fading occurs. The relay can be opportunistically selected based on the channel condition and the power usage. With Lyanupov optimization, we maximize the transmission reliability while ensuring the extra power consumption of low-priority nodes are acceptable. Subsequently, simulations are conducted in the Network Simulator 3 (NS3) to validate the proposed relay selection strategy, showing that the proposed strategy effectively improves the reliability from 90.2% to 99.1% with an additional power consumption of 30%. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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Intrusion detection based on the SS-CBLM model

Intrusion detection based on the SS-CBLM model

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2024 International Conference on Algorithms, High Performance Computing, and Artificial Intelligence, AHPCAI 2024

作者： Huang, Tinghui Zhang, Wenshuai Wang, Yu School of Computer and Information Security Guilin University of Electronic Technology Guangxi Guilin China School of Ocean Engineering Guilin University of Electronic Technology Guangxi Guilin China

ISBN: (纸本)9781510685840

To address the issue of low intrusion detection accuracy due to incomplete feature extraction by a single model, an SS-CBLM model detection method is proposed. This method combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (Bi-LSTM) to extract and integrate features. SE modules and self-attention mechanisms are incorporated into the CNN and Bi-LSTM, respectively, to enhance the model's ability to extract important features. Finally, a Multilayer Perceptron (MLP) model is used as a classifier for multi-classification. Additionally, class merging, RandomUnderSampler, and Synthetic Minority Over-sampling Technique (SMOTE) are employed to address the class imbalance issue. The model is trained on the CIC-IDS2017 dataset, and experimental results show that the model achieves an accuracy of 99.94%, effectively detecting abnormal traffic. © 2024 SPIE.

关键词： Network intrusion

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Low-cost Granary Vehicles Positioning Using a Distributed LED Network 24

Low-cost Granary Vehicles Positioning Using a Distributed LE...

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24th IEEE International Conference on Communication technology, ICCT 2024

作者： Liu, Jingya Ma, Pengtao Zhang, Lingmin Geng, Yu Yan, Lei Northeastern University School of Computer and Communication Engineering Qinhuangdao066004 China Hebei Normal University of Science and Technology Institute of Mathematics and Information Technology Qinhuangdao066004 China

ISBN: (纸本)9798350363760

To address the safety hazards and inefficiency of traditional manual grain depot management, the application of granary vehicles has emerged as a promising solution. However, the absence of GPS signals indoors and the cumulative errors inherent in inertial navigation system (INS) make it difficult for vehicles to achieve the required positioning accuracy for operations. To achieve accurate positioning in the grain depot work area, this paper proposes a distributed auxiliary positioning scheme based on a light emitting diode (LED) network, combined with a attitude angle-oriented variable state dimension (AAVSD)-Kalman filter to achieve low-cost and high-precision positioning. The scheme selects the positioning method according to the vehicle's attitude angle: LED network positioning is used when the attitude angle is within the certain threshold range. Simulation results show that the proposed scheme effectively solves the problem of INS cumulative error and significantly improves the positioning accuracy of granary vehicles without excessive budget. © 2024 IEEE.

关键词： Budget control

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