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A Secure Framework for Blockchain Transactions Protection

computer systems science & engineering 2023年第5期45卷 1095-1111页

作者： Wafaa N.Al-Sharu Majdi K.Qabalin Muawya Naser Omar A.Saraerh Department of Electrical Engineering Faculty of EngineeringThe Hashemite UniversityZarqa13133Jordan Department of Computer Science King Hussein School of Computing SciencesPrincess Sumaya University for TechnologyAmman11941Jordan

One of the most extensively used technologies for improving the security of IoT devices is blockchain *** is a new technology that can be utilized to boost the *** is a decentralized peer-to-peer network with no central *** nodes on the network mine or verify the data recorded on the *** is a distributed ledger that may be used to keep track of transactions between several *** one can tamper with the data on the blockchain since it is *** the blocks are connected by hashes,the transaction data is *** is managed by a system that is based on the consensus of network users rather than a central *** immutability and tamper-proof nature of blockchain security is based on asymmetric cryptography and ***,Blockchain has an immutable and tamper-proof smart contract,which is a logic that enforces the Blockchain’s *** is a conflict between the privacy protection needs of cyber-security threat intelligent(CTI)sharing and the necessity to establish a comprehensive attack chain during blockchain *** paper presents a blockchain-based data sharing paradigm that protects the privacy of CTI sharing parties while also preventing unlawful sharing and ensuring the benefit of legitimate sharing *** builds a full attack chain using encrypted threat intelligence and exploits the blockchain’s backtracking capacity to finish the decryption of the threat source in the attack *** contracts are also used to send automatic early warning replies to possible attack *** tests are used to verify the feasibility and efficacy of the suggested model.

关键词： Manuscript preparation typeset format

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Smart Surveillance System and Prediction of Abnormal Activity in ATM Using Deep Learning

Smart Surveillance System and Prediction of Abnormal Activit...

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International Conference on Data science and Network engineering, ICDSNE 2023

作者： Gnanavel, S. Duraimurugan, N. Jaeyalakshmi, M. Department of Computing Technologies Faculty of Engineering and Technology SRM Institute of Science and Technology Tamil Nadu Kattankulathur 603203 India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai6021053 India

ISBN: (纸本)9789819967544

Although surveillance cameras are used in ATM cells, we face some problems of robbery and theft at ATMs due to lack of security;however, the monitoring capacity of law enforcement agencies has not kept pace. ATM spoofing attacks can be carried out to break or damage the ATM by stealing the machine and taking cash from the ATM. To reduce this problem, we arm the ATM with a camera module mounted in the room to perform continuous video observation. The camera detects the human and his activity in the ATM and attempts to breach the ATM. It detects unusual activities and immediately sends an alert notification to the police. Therefore, the system handles the application developed to automate video surveillance and detect any potential criminal activity at ATMs. Therefore, in this work, abnormal behavior is observed using CNN and RNN in surveillance videos. These algorithms can be used to recognize faces, detect and track camera movements, and detect and identify the action required to prevent such activity. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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Classification of Electroencephalogram Signals Using LSTM and SVM Based on Fast Walsh-Hadamard Transform

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computers, Materials & Continua 2023年第6期75卷 5271-5286页

作者： Saeed Mohsen Sherif S.M.Ghoneim Mohammed S.Alzaidi Abdullah Alzahrani Ashraf Mohamed Ali Hassan Electronics and Communications Engineering Department Al-Madinah Higher Institute for Engineering and TechnologyGiza12947Egypt Department of Artificial Intelligence Engineering Faculty of Computer Science and EngineeringKing Salman International University(KSIU)South Sinai46511Egypt Department of Electrical Engineering College of EngineeringTaif UniversityTaif21944Saudi Arabia Department of Electronics and Communications Engineering October High Institute for Engineering and Technology6th of October CityGiza12596Egypt

Classification of electroencephalogram(EEG)signals for humans can be achieved via artificial intelligence(AI)***,the EEG signals associated with seizure epilepsy can be detected to distinguish between epileptic and non-epileptic *** this perspective,an automated AI technique with a digital processing method can be used to improve these *** paper proposes two classifiers:long short-term memory(LSTM)and support vector machine(SVM)for the classification of seizure and non-seizure EEG *** classifiers are applied to a public dataset,namely the University of Bonn,which consists of 2 classes–seizure and *** addition,a fast Walsh-Hadamard Transform(FWHT)technique is implemented to analyze the EEG signals within the recurrence space of the ***,Hadamard coefficients of the EEG signals are obtained via the ***,the FWHT is contributed to generate an efficient derivation of seizure EEG recordings from non-seizure EEG ***,a k-fold cross-validation technique is applied to validate the performance of the proposed *** LSTM classifier provides the best performance,with a testing accuracy of 99.00%.The training and testing loss rates for the LSTM are 0.0029 and 0.0602,respectively,while the weighted average precision,recall,and F1-score for the LSTM are 99.00%.The results of the SVM classifier in terms of accuracy,sensitivity,and specificity reached 91%,93.52%,and 91.3%,*** computational time consumed for the training of the LSTM and SVM is 2000 and 2500 s,*** results show that the LSTM classifier provides better performance than SVM in the classification of EEG ***,the proposed classifiers provide high classification accuracy compared to previously published classifiers.

关键词： Electroencephalogram LSTM SVM fast Walsh-Hadamard transform seizure accuracy sensitivity specificity

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Posture Prediction in Response to Seat Angle Change with Smart Chair

Posture Prediction in Response to Seat Angle Change with Sma...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Esumi, Tsubasa Takemura, Noriko Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Department of Artificial Intelligence Fukuoka Japan

ISBN: (纸本)9781665410205

Long-term desk work with lousy posture causes physical disabilities. To solve this problem, we have developed a smart chair with a controllable seat angle in our previous research. In order to control the seat angle and guide the user to the correct posture using this smart chair, it is necessary to investigate how the posture changes in response to the seat angle change. In this study, we first constructed a dataset by capturing posture data from 30 subjects while changing the seat angle of the smart chair. This dataset trained a model based on a graph neural network to predict posture in response to seat angle changes. We compared the model's prediction performance in the evaluation experiment depending on the training data and the body part. © 2024 IEEE.

关键词： Graph neural networks

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A secure data hiding approach based on least-significant-bit and nature-inspired optimization techniques

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4639-4657页

作者： Hameed, Mohamed Abdel Abdel-Aleem, Omar A. Hassaballah, M. Department of Computer Science Faculty of Computers and Information Luxor University Luxor Egypt College of Computer Engineering and Sciences Prince Sattam Bin Abdulaziz University AlKharj Saudi Arabia Department of Computer Science Faculty of Computers and Information South Valley University Qena83523 Egypt

With remarkable information technology development, information security has become a major concern in the communication environment, where security must be performed for the multimedia messages exchanged between the sender and the intended recipient. Digital multimedia steganography techniques have been developed to attain a security for covert communication and secure data. This paper proposes an approach for image steganography using the Least Significant Bit Substitution (LSB) and Nature-Inspired Harris Hawks Optimization (HHO) algorithm for efficient concealing of the secret data inside a cover image;thus providing high confidentiality. The HHO based data encoding operation uses the PSNR visual quality metric as an objective function. The objective function is used to determine the ideal encoding vector to convert the secret message to its encoded form. The proposed approach performs better than other state-of-the-art methods in terms of standard measures of visual quality with maintaining high embedding capacity. Comparisons with existing LSB or multi-directional PVD embedding methods demonstrate that the proposed method has more optimized and higher embedding capacity with maintaining visual quality. Besides, the proposed approach achieves high security against statistical StegoExpose analysis, ALASKA2 deep learning steganalysis, and image processing attacks. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Steganography

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Hybrid Fuzzy Decision-Making Model 4

Hybrid Fuzzy Decision-Making Model

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Kumar, Vikram Saini, Dimple Rastogi, Shailesh Raman, Ramakrishnan Verma, Apurv Meenakshi, R. Srv Media Pvt. Ltd India India Pune India Department of Cse India Computer Science and Engineering Faculty of Engineering and Technology Parul University India Computer Science and Engineering Chennai Institute of Technology India

ISBN: (纸本)9798350360165

Establishing a sustainable the healthcare system necessitates the development of social, economic, and environmental norms. Although many studies concentrate on these elements separately, an integrated method of handling evaluating environmentally friendly development in healthcare institutions is conspicuously lacking. To address this gap, this paper presents a technique for studying indicators of sustainable development in healthcare organizations by modifying dimensions, criteria, and characteristics. In all, the three sustainability aspects are covered by the study with 62 traits and 15 criteria identified. Furthermore, a unique implementation technique is proposed to enable this methodology to be applied to real-world issues. This paper makes three contributions: it provides a new methodology for gathering basic data to measure long-term viability levels in healthcare systems;it presents the ability to identify implementation weaknesses in sustainability aspects;and it determines the sustainability level of the healthcare system under imprecise conditions. The study findings provide practitioners and specialists with significant perspectives to assess the sustainability levels of healthcare systems and improve their overall efficacy. © 2024 IEEE.

关键词： Sustainable development goals

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Enhanced Security through Distributed Fragmentation and Encryption for Cloud based Data Storage 2

Enhanced Security through Distributed Fragmentation and Encr...

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2nd IEEE International Conference on Emerging Research in Computational science, ICERCS 2024

作者： Palani, S. Ajay, B. Bhavadharani, B. Varshini, A. Coimbatore India Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534950

Integrating cloud computing into organizational data management demands paramount protection of sensitive information. This paper presents a framework to enhance data security through distributed fragmentation and advanced encryption techniques. By segmenting sensitive data into multiple fragments distributed across various cloud storage locations, the approach mitigates risks associated with centralized data repositories. Each fragment is encrypted with robust cryptographic algorithms, ensuring that unauthorized access renders intercepted data unintelligible. This fragmentation not only strengthens security but also improves performance through parallel data processing and retrieval, optimizing resource utilization. Additionally, decentralized storage reduces the impact of single points of failure, enhancing resilience against data loss or breaches. Simulation results demonstrate the effectiveness of this model in countering attack vectors, including data tampering and unauthorized access. Overall, this research contributes to secure cloud storage solutions, offering a robust framework for organizations to protect sensitive information, ultimately fostering trust in cloud technologies and enabling safer data management practices. © 2024 IEEE.

关键词： Cloud security

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Towards Atomic Scale Quantum Dots in Silicon: An Ultra-Efficient and Robust Subtractor Using Proposed P-Shaped Pattern

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IEEE Transactions on Nanotechnology 2024年 23卷 482-489页

作者： Rasmi, Hadi Mosleh, Mohammad Navimipour, Nima Jafari Kheyrandish, Mohammad Dezful Branch Islamic Azad University Department of Computer Engineering Dezful313 Iran Kadir Has University Department of Computer Engineering Faculty of Engineering and Natural Sciences Istanbul34083 Turkey National Yunlin University of Science and Technology Future Technology Research Center Yunlin Douliou64002 Taiwan Western Caspian University Research Center of High Technologies and Innovative Engineering Baku Azerbaijan

Today, Complementary Metal-Oxide-Semiconductor (CMOS) technology faces critical challenges, such as power consumption and current leakage at the nanoscale. Therefore, Atomic Silicon Dangling Bond (ASDB) technology has been proposed as one of the best candidates to replace CMOS technology;due to its high-speed switching and low power consumption. Among the most important issues in ASDB nanotechnology, output stability and robustness against possible faults may be focused. This paper first introduces a novel P-shaped pattern in ASDB, for designing stable and robust primitive logic gates, including AND, NAND, OR, NOR and XOR. Then, two combinational circuits, half-subtractor and full-subtractor, are proposed by the proposed ASDB gates. The simulation results show high output stability as well as adequate robustness, against various defects obtained by the proposed designs;on average, they have improvements of more than 56% and 62%, against DB omission defects and extra cell deposition defects;respectively. Also, the results of the investigations show that the proposed circuits have been improved by 65%, 21% and 2%, in terms of occupied area, energy and occurrence, respectively;compared to the previous works. © 2002-2012 IEEE.

关键词： Silicon

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General Optimal Trajectory Planning:Enabling Autonomous Vehicles with the Principle of Least Action

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engineering 2024年第2期33卷 63-76页

作者： Heye Huang Yicong Liu Jinxin Liu Qisong Yang Jianqiang Wang David Abbink Arkady Zgonnikov School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China Department of Cognitive Robotics Faculty of MechanicalMaritime and Materials Engineering(3mE)Delft University of TechnologyDelft 2628Netherlands Department of Software Technology Faculty of Electrical EngineeringMathematics and Computer ScienceDelft University of TechnologyDelft 2628Netherlands Xi’an Institute of High-Technology Xi’an 710051China

This study presents a general optimal trajectory planning(GOTP)framework for autonomous vehicles(AVs)that can effectively avoid obstacles and guide AVs to complete driving tasks safely and ***,we employ the fifth-order Bezier curve to generate and smooth the reference path along the road *** coordinates are then transformed to achieve the curvature continuity of the generated *** the road constraints and vehicle dynamics,limited polynomial candidate trajectories are generated and smoothed in a curvilinear coordinate ***,in selecting the optimal trajectory,we develop a unified and auto-tune objective function based on the principle of least action by employing AVs to simulate drivers’behavior and summarizing their manipulation characteristics of“seeking benefits and avoiding losses.”Finally,by integrating the idea of receding-horizon optimization,the proposed framework is achieved by considering dynamic multi-performance objectives and selecting trajectories that satisfy feasibility,optimality,and *** simulations and experiments are performed,and the results demonstrate the framework’s feasibility and effectiveness,which avoids both dynamic and static obstacles and applies to various scenarios with multi-source interactive traffic ***,we prove that the proposed method can guarantee real-time planning and safety requirements compared to drivers’manipulation.

关键词： Autonomous vehicle Trajectory planning Multi-performance objectives Principle of least action

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A Hybrid Neural Network-based Approach for Forecasting Water Demand

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computers, Materials & Continua 2022年第10期73卷 1365-1383页

作者： Al-Batool Al-Ghamdi Souad Kamel Mashael Khayyat Department of Computer Science and Artificial Intelligence College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia Department of Computer and Network Engineering College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia Department of Information Systems and Technology College of Computer Sciences and EngineeringUniversity of JeddahJeddah 21959Saudi Arabia

Water is a vital *** supports a multitude of industries,civilizations,and ***,climatic conditions impact water availability,particularly in desert areas where the temperature is high,and rain is ***,it is crucial to forecast water demand to provide it to sectors either on regular or emergency *** study aims to develop an accurate model to forecast daily water demand under the impact of climatic *** forecasting is known as a multivariate time series because it uses both the historical data of water demand and climatic conditions to forecast the *** on the collected data of Jeddah city,Saudi Arabia in the period between 2004 and 2018,we develop a hybrid approach that uses Artificial Neural Networks(ANN)for forecasting and Particle Swarm Optimization algorithm(PSO)for tuning ANNs’*** on the Root Mean Square Error(RMSE)metric,results show that the(PSO-ANN)is an accurate model for multivariate time series ***,the first day is the most difficult day for prediction(highest error rate),while the second day is the easiest to predict(lowest error rate).Finally,correlation analysis shows that the dew point is the most climatic factor affecting water demand.

关键词： Water demand forecasting artificial neural network multivariate time series climatic conditions particle swarm optimization hybrid algorithm

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