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5th International Conference on Advances in Information Communication Technology and Computing, AICTC 2024

作者： Swapna, M.P. Ramkumar, J. Karthikeyan, R. Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Kollam Amritapuri India Department of Information Technology & Cognitive Systems Sri Krishna Arts & Science College Coimbatore India Department of Computer Technology Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (纸本)9789819761050

Wireless sensor networks, the dynamic networks with spatially dispersed sensors are used indispensably in a wide range of applications. Heterogeneous wireless sensor networks represent a collection of sensor nodes of varying capabilities. However, if the energy in the network is not handled effectively, the resource-constrained sensors may reach an early death, affecting the network lifetime. Security and routing are the two vital phenomena in wireless sensor networks that need careful and effective algorithmic execution. The proposed approach is an energy-aware routing algorithm combined with secure data transmission. TDDSRP is an innovative protocol to ensure security while reducing energy consumption. The residual energy and the direct trust value of nodes are crucial parameters to evaluate the energy trust value. The effectiveness of the protocol is tested against the existing counterparts using MATLAB simulations. The experimental results illustrate the performance of the protocol in energy consumption rate, throughput, and security measures. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： MATLAB

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Privacy‐preserving remote sensing images recognition based on limited visual cryptography

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CAAI Transactions on Intelligence Technology 2023年第4期8卷 1166-1177页

作者： Denghui Zhang Muhammad Shafiq Liguo Wang Gautam Srivastava Shoulin Yin Cyberspace Institute of Advanced Technology Guangzhou UniversityGuangzhouChina Department of New Networks Peng Cheng LaboratoryShenzhenChina College of Information and Communications Engineering Dalian Minzu UniversityDalianChina Department of Mathematics and Computer Science Brandon UniversityBrandonManitobaCanada Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina Department of Computer Science and Math Lebanese American UniversityBeirutLebanon College of Information and Communications Engineering Harbin Engineering UniversityHarbinChina

With the arrival of new data acquisition platforms derived from the Internet of Things(IoT),this paper goes beyond the understanding of traditional remote sensing *** fusion of remote sensing and computer vision has hit the industrial world and makes it possible to apply Artificial intelligence to solve problems such as automatic extraction of information and image ***,due to the complex architecture of IoT and the lack of a unified security protection mechanism,devices in remote sensing are vulnerable to privacy leaks when sharing *** is necessary to design a security scheme suitable for computation‐limited devices in IoT,since traditional encryption methods are based on computational *** Cryptography(VC)is a threshold scheme for images that can be decoded directly by the human visual system when superimposing encrypted *** stacking‐to‐see feature and simple Boolean decryption operation make VC an ideal solution for privacy‐preserving recognition for large‐scale remote sensing images in *** this study,the secure and efficient transmission of high‐resolution remote sensing images by meaningful VC is *** diffusing the error between the encryption block and the original block to adjacent blocks,the degradation of quality in recovery images is *** fine‐tuning the pre‐trained model from large‐scale datasets,we improve the recognition performance of small encryption datasets for remote sensing *** experimental results show that the proposed lightweight privacy‐preserving recognition framework maintains high recognition performance while enhancing security.

关键词： activity recognition feature extraction image classification KNN privacy protection remote monitoring remote sensing

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Multidimensional binary layered model for census prediction using machine learning techniques

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Asian Journal of Civil Engineering 2024年第6期25卷 4877-4891页

作者： El-Salhi, Subhieh Albqowr, Hala Mahmoud Saleh Igried, Bashar Awwad, Sari Dittakan, Kwankamon Department of Computer Information System The Hashemite University Zarqa 13133 Jordan Surveying and Geomatics Engineering Department Al-Balqa Applied University Al-Salt 19117 Jordan Department of Computer Science and Its Applications The Hashemite University Zarqa 13133 Jordan College of Computing Prince of Songkla University Phuket 83120 Thailand

The development of the resource allocation process is based on essential factors, such as population size, which can be considered one of the most critical ones. Estimating the population size for a particular region, especially in developing countries, indicates the government’s authority to invest and utilize the resources efficiently. As a result, forecasting population size is one of the most essential requirements that guide the entire development process and, as a result, affects human well-being. This paper presents a novel technique for census seven prediction of a specific region of interest in the context of satellite images. The Multidimensional Binary Layered Model (MDBL) is a representation technique founded on identifying a given region of interest in terms of the geographical nature of the local surrounding regions. The MDBL is then employed to generate an "effective" classifier using different machine learning techniques such as the Convolutional Neural Network (CNN), k-nearest Neighbor (k-NN), and Support Vector Machine (SVM). The Black Widow Optimization (BWO) method has been used to maximize performance and provide the best machine learning model. In order to produce the best prediction model, this optimization method ensures the models are tuned with the most appropriate hyperparameters. To this end, Al-Karak City, in Jordan, has been used as a case study to evaluate the proposed model. The evaluation of the classifiers has been reported in terms of the accuracy and the Area Under Curve (AUC) measurements. The CNN achieved the “best” overall AUC result of 88%, indicating the significant impact of using the proposed MDBL to generate an accurate, effective classifier. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.

关键词： Binary layered model Black widow optimization (BWO) Census prediction Classification CNN Machine learning

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Hyperparameter Tuned Deep Learning Enabled Cyberbullying Classification in Social Media

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computers, Materials & Continua 2022年第12期73卷 5011-5024页

作者： Mesfer Al Duhayyim Heba G.Mohamed Saud S.Alotaibi Hany Mahgoub Abdullah Mohamed Abdelwahed Motwakel Abu Sarwar Zamani Mohamed I.Eldesouki Department of Computer Science College of Sciences and Humanities-AflajPrince Sattam bin Abdulaziz UniversitySaudi Arabia Department of Electrical Engineering College of EngineeringPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computers and Information Computer Science DepartmentMenoufia UniversityEgypt Research Centre Future University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Information System College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl KharjSaudi Arabia

Cyberbullying(CB)is a challenging issue in social media and it becomes important to effectively identify the occurrence of *** recently developed deep learning(DL)models pave the way to design CB classifier models with maximum *** the same time,optimal hyperparameter tuning process plays a vital role to enhance overall *** study introduces a Teacher Learning Genetic Optimization with Deep Learning Enabled Cyberbullying Classification(TLGODL-CBC)model in Social *** proposed TLGODL-CBC model intends to identify the existence and non-existence of CB in social media ***,the input data is cleaned and pre-processed to make it compatible for further *** by,independent recurrent autoencoder(IRAE)model is utilized for the recognition and classification of ***,the TLGO algorithm is used to optimally adjust the parameters related to the IRAE model and shows the novelty of the *** assuring the improved outcomes of the TLGODLCBC approach,a wide range of simulations are executed and the outcomes are investigated under several *** simulation outcomes make sure the improvements of the TLGODL-CBC model over recent approaches.

关键词： Social media deep learning cyberbullying cybersecurity hyperparameter optimization

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Modeling and Verification of Aircraft Takeoff Through Novel Quantum Nets

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computers, Materials & Continua 2022年第8期72卷 3331-3348页

作者： Maryam Jamal Nazir Ahmad Zafar Atta-ur-Rahman Dhiaa Musleh Mohammed A.Gollapalli Sghaier Chabani School of Systems and Technology University of Management&Technology(UMT)C-II Block C 2 Phase 1 Johar TownLahore54770PunjabPakistan Department of Computer Science COMSATS University Islamabad–Sahiwal CampusSahiwal57000PunjabPakistan Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)Dammam31441Saudi Arabia Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal University(IAU)Dammam31441Saudi Arabia

The formal modeling and verification of aircraft takeoff is a challenge because it is a complex safety-critical *** task of aircraft takeoff is distributed amongst various computer-based controllers,however,with the growing malicious threats a secure communication between aircraft and controllers becomes highly *** research serves as a starting point for integration of BB84 quantum protocol with petri nets for secure modeling and verification of takeoff *** integrated model combines the BB84 quantum cryptographic protocol with powerful verification tool support offered by petri *** model certain important properties of BB84,a new variant of petri nets coined as Quantum Nets are proposed by defining their mathematical foundations and overall system dynamics,furthermore,some important system properties are also abstractly *** proposed QuantumNets are then applied for modeling of aircraft takeoff process by defining three quantum nets:namely aircraft,runway controller and gate *** authentication between quantum nets,the use of external places and transitions is demonstrated to describe the encryptiondecryption process of qubits ***,the developed takeoff quantum network is verified through simulation offered by colored petri-net(CPN)***,reachability tree(RT)analysis is also performed to have greater confidence in feasibility and correctness of the proposed aircraft takeoff model through the Quantum Nets.

关键词： Aircraft takeoff BB84 quantum cryptography petri-nets quantum nets formal modeling and verification

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Accurate Parameter Estimation Based on the Two-Sided KZ Filter in Low Signal-to-Noise Ratio Conditions 6

Accurate Parameter Estimation Based on the Two-Sided KZ Filt...

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6th International Conference on Robotics, Intelligent Control and Artificial Intelligence, RICAI 2024

作者： Zhang, Liang Wang, Qing-Guo Hong Kong Baptist University Department of Computer Science and Technology Beijing Normal University-Hong Kong Baptist University United International College Zhuhai China Institute of AI and Future Networks Beijing Normal University Guangdong Provincial Key Laboratory IRADS Department of Computer Science BNU-HKBU United International College Zhuhai China

ISBN: (纸本)9798331541699

system identification is the process of developing models based on data collected from dynamic systems. A significant challenge in this field is accurately estimating model parameters using input-output data. This data, typically gathered through sensors, often contains noise due to inaccuracies in the sensors and errors during communication. The presence of such noise can lead to biased estimates of parameters, which undermines the model’s effectiveness. While traditional methods strive to minimize this bias during parameter estimation, they frequently encounter difficulties when dealing with high levels of noise. An alternative strategy involves employing filtering techniques to clean the data before estimation takes place. Low-pass (LP) filters are designed to reduce frequencies above a specific threshold and are widely used for mitigating noise issues. Among these filters, the Kolmogorov–Zurbenko (KZ) filter operates as multiple iterations of a moving average (MA) filter and has shown strong capabilities in reducing noise;it is commonly applied in high-resolution signal analysis and image processing tasks. In this study, we introduce two KZ filtering approaches aimed at denoising data prior to parameter estimation. Theoretical evaluations and experimental findings indicate that the proposed KZ filtering method surpasses LP filters and wavelet denoising techniques, particularly in environments characterized by low Signal-to-Noise Ratios (SNR). © 2024 IEEE.

关键词： Low pass filters

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Dual Encoder Cross-Shape Transformer Network for Medical Image Segmentation in Internet of Medical Things for Consumer Health

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IEEE Transactions on Consumer Electronics 2025年第1期71卷 538-549页

作者： Zhao, Yawu Wang, Shudong Zhang, Yulin Ren, Yande Zhang, Yuanyuan Pang, Shanchen the School of Computer Science and Technology China University of Petroleum Shandong Qingdao China the College of Mathematics and System Science Shandong University of Science and Technology Shandong Qingdao China the Department of Medical Imaging Center the Affiliated Hospital of Qingdao University Shandong Qingdao China the School of Information and Control Engineering Qingdao University of Technology Shandong Qingdao China

In emerging consumer healthcare, high-performance and robust medical image segmentation methods are essential for personalized diagnosis and treatment. Thus, early screening of aneurysms reduces the risk of aneurysm rupture and provides therapeutic and preventive measures. However, uncontrollable factors such as uncertainty in the size and location shape of tumors in medical images a significant challenge to medical image segmentation. These factors make extracting high-quality features from aneurysm images difficult, resulting in poor segmentation. Then, we designed a dual encoder cross-shape transform network (DECSTNet) to capture aneurysm feature information. The dual encoder structure can extract aneurysm feature information at different scales, the adaptive dynamic feature fusion module can fuse features at different scales between the encoders, and the cross-shape window transform layer can compute the width and height of the image in parallel for local self-attention, which enhances the interactive capability of the telematic information while realizing the complementarity of the local information. Through extensive experiments, we demonstrate the excellent segmentation performance of DECSTNet on the private and three public datasets. Noteworthy our method has significant segmentation performance and fewer parameters, making it well-suited to be deployed on IoMT diagnostic platforms for medical image segmentation to promote healthy patient consumption. © 2025 IEEE.

关键词： Semantic Segmentation

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Hybrid DF and SIR Forwarding Strategy in Cooperative Communications and Higher Order Modulations

Hybrid DF and SIR Forwarding Strategy in Cooperative Communi...

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International Conference on Communications and Networking, ComNet

作者： Slim Chaoui Madallah Alruwaili Haifa Harrouch Omar Alruwaili Department of Computer Engineering and Networks College of Computer and Information Sciences Jouf University KSA Computer Science Department Applied College University of Hail KSA

ISBN: (数字)9798331520793

ISBN: (纸本)9798331520809

In this paper we propose a hybrid decode and forward and soft information relaying (HDFSIR) strategy to address the issue of error-propagation in coded cooperative communications. We also introduce a novel method for mapping the conveyed bits' log likelihood ratios onto higher order modulated soft symbols and provide a model characterizing the relationship between the estimated complex soft symbol and the actual high order modulation symbol. A closed form expression for the symbol error rate of the coded cooperative communication with HDFSIR strategy is derived. Monte Carlo simulations are presented to demonstrate the validity of the analytical results.

关键词： Protocols Closed-form solutions Monte Carlo methods Cooperative communication Symbols Modulation Probability Power system reliability Relays Signal to noise ratio

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Peak-Average-Power Ratio Techniques for 5G Waveforms Using D-SLM and D-PTS

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computer systems science & Engineering 2023年第5期45卷 1199-1210页

作者： Himanshu Sharma karthikeyan Rajagopal G.Gugapriya Rajneesh Pareek Arun Kumar HayaMesfer Alshahrani Mohamed K.Nour Hany Mahgoub Mohamed Mousa Anwer Mustafa Hilal Department of Computer Science&Engineering JECRC UniversityJaipurIndia Centre for Nonlinear Systems Chennai Institute of TechnologyChennaiIndia School of Electronics Engineering Vellore Institute of TechnologyChennaiIndia Department of Electronics and Communication Engineering JECRC UniversityJaipurIndia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science College of Computing and Information SystemUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Electrical Engineering Department Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11845Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Multicarrier Waveform(MCW)has several advantages and plays a very important role in cellular *** generation(5G)MCW such as Non-Orthogonal Multiple Access(NOMA)and Filter Bank Multicarrier(FBMC)are thought to be important in 5G *** Peak to Average Power Ratio(PAPR)is seen as a serious concern in MCW since it reduces the efficiency of amplifier use in the user *** paper presents a novel Divergence Selective Mapping(DSLM)and Divergence Partial Transmission Sequence(D-PTS)for 5G *** is seen that the proposed D-SLM and PTS lower PAPR with low computational *** work highlighted a combination of multi-data block partial transmit schemes along with tone *** this,an overlapping factor is used to determine the number of data blocks for every ***,considering only those data blocks that have minimum signal power,the use of DSLM and DPTS are required to eliminate the segment’s *** results reveal that the suggested hybrid technique proves to be better than the conventional PTS ***,the power saving performance of FBMC and NOMA is compared with the Orthogonal Frequency Division Multiplexing(OFDM)waveform.

关键词： 5G PAPR D-SLM D-PTS

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Optimal Deep Reinforcement Learning for Intrusion Detection in UAVs

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computers, Materials & Continua 2022年第2期70卷 2639-2653页

作者： V.Praveena A.Vijayaraj P.Chinnasamy Ihsan Ali Roobaea Alroobaea Saleh Yahya Alyahyan Muhammad Ahsan Raza Department of Computer Science and Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India Department of Information Technology Vignan’s Foundation for ScienceTechnology&ResearchGuntur522213India Department of Information Technology Sri Shakthi Institute of Engineering and TechnologyCoimbatore641062India Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversity of MalayaKuala Lumpur50603Malaysia Department of Computer Science College of Computers and Information TechnologyTaif UniversityTaif21944Saudi Arabia Department of Computer Science Community College in DwadmiShaqra University11961Saudi Arabia Department of Information Technology Bahauddin Zakariya UniversityMultan60000Pakistan

In recent years,progressive developments have been observed in recent technologies and the production cost has been continuously *** such scenario,Internet of Things(IoT)network which is comprised of a set of Unmanned Aerial Vehicles(UAV),has received more attention from civilian tomilitary *** network security poses a serious challenge to UAV networks whereas the intrusion detection system(IDS)is found to be an effective process to secure the UAV *** IDSs are not adequate to handle the latest computer networks that possess maximumbandwidth and data *** order to improve the detection performance and reduce the false alarms generated by IDS,several researchers have employed Machine Learning(ML)and Deep Learning(DL)algorithms to address the intrusion detection *** this view,the current research article presents a deep reinforcement learning technique,optimized by BlackWidow Optimization(DRL-BWO)algorithm,for UAV *** addition,DRL involves an improved reinforcement learning-based Deep Belief Network(DBN)for intrusion *** parameter optimization of DRL technique,BWO algorithm is *** helps in improving the intrusion detection performance of UAV *** extensive set of experimental analysis was performed to highlight the supremacy of the proposed *** the simulation values,it is evident that the proposed method is appropriate as it attained high precision,recall,F-measure,and accuracy values such as 0.985,0.993,0.988,and 0.989 respectively.

关键词： Intrusion detection UAV networks reinforcement learning deep learning parameter optimization

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