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An Innovative Approach Using TKN-Cryptology for Identifying the Replay Assault

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computers, Materials & Continua 2024年第1期78卷 589-616页

作者： Syeda Wajiha Zahra Muhammad Nadeem Ali Arshad Saman Riaz Muhammad Abu Bakr Ashit Kumar Dutta Zaid Alzaid Badr Almutairi Sultan Almotairi department of computer Science Alhamd Islamic UniversityIslamabad44000Pakistan department of computer Science and Technology University of Science and Technology BeijingBeijing100083China department of computer Science National University of TechnologyIslamabad44000Pakistan department of electrical engineering National University of TechnologyIslamabad44000Pakistan department of computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityRiyadh13713Saudi Arabia department of computer Science Faculty of Computer and Information SystemsIslamic University of MadinahMedinah42351Saudi Arabia department of Information Technology College of Computer Sciences and Information Technology CollegeMajmaah UniversityAl-Majmaah11952Saudi Arabia department of Natural and Applied sciences Faculty of Community CollegeMajmaah UniversityMajmaah11952Saudi Arabia

Various organizations store data online rather than on physical servers.As the number of user’s data stored in cloud servers increases,the attack rate to access data from cloud servers also increases.Different researchers worked on different algorithms to protect cloud data from replay attacks.None of the papers used a technique that simultaneously detects a full-message and partial-message replay attack.This study presents the development of a TKN(Text,Key and Name)cryptographic algorithm aimed at protecting data from replay attacks.The program employs distinct ways to encrypt plain text[P],a user-defined Key[K],and a Secret Code[N].The novelty of the TKN cryptographic algorithm is that the bit value of each text is linked to another value with the help of the proposed algorithm,and the length of the cipher text obtained is twice the length of the original text.In the scenario that an attacker executes a replay attack on the cloud server,engages in cryptanalysis,or manipulates any data,it will result in automated modification of all associated values inside the backend.This mechanism has the benefit of enhancing the detectability of replay attacks.Nevertheless,the attacker cannot access data not included in any of the papers,regardless of how effective the attack strategy is.At the end of paper,the proposed algorithm’s novelty will be compared with different algorithms,and it will be discussed how far the proposed algorithm is better than all other algorithms.

关键词： Replay attack malware message attack file encryption cryptology data security

Recent advances in heterogeneous catalysis of solar-driven carbon dioxide conversion

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Journal of Environmental sciences 2024年第6期140卷 165-182页

作者： Jun Xu Farzaneh Arabpour Roghabadi Ying Luo Vahid Ahmadi Qian Wang Zheng Wang Hong He State Key Joint Laboratory of Environment Simulation and Pollution Control Research Center for Eco-Environmental SciencesChinese Academy of SciencesBeijing 100085China College of Chemistry and Chemical engineering Henan Polytechnic UniversityJiaozuoHenan 454000China department of Process engineering Faculty of Chemical EngineeringTarbiat Modares UniversityTehranIran Optoelectronics and Nanophotonics Research Group Faculty of Electrical and Computer EngineeringTarbiat Modares UniversityTehranIran Graduate School of engineering Nagoya UniversityFuro-choChikusa-kuNagoya 464-8603Japan

Solar-driven carbon dioxide(CO_(2))conversion including photocatalytic(PC),photoelectrochemical(PEC),photovoltaic plus electrochemical(PV/EC)systems,offers a renewable and scalable way to produce fuels and high-value chemicals for environment and energy sustainability.This review summarizes the basic fundament and the recent advances in the field of solar-driven CO_(2)conversion.Expanding the visible-light absorption is an important strategy to improve solar energy conversion efficiency.The separation and migration of photogenerated charges carriers to surface sites and the surface catalytic processes also determine the photocatalytic performance.Surface engineering including co-catalyst loading,defect engineering,morphology control,surface modification,surface phase junction,and Z-scheme photocatalytic system construction,have become fundamental strategies to obtain high-efficiency photocatalysts.Similar to photocatalysis,these strategies have been applied to improve the conversion efficiency and Faradaic efficiency of typical PEC systems.In PV/EC systems,the electrode surface structure and morphology,electrolyte effects,and mass transport conditions affect the activity and selectivity of electrochemical CO_(2)reduction.Finally,the challenges and prospects are addressed for the development of solar-driven CO_(2)conversion system with high energy conversion efficiency,high product selectivity and stability.

关键词： Solar-driven CO_(2)reduction Photocatalytic system Photoelectrochemical system Photovoltaic plus electrochemical system Surface/interface engineering

Machine-learned wearable sensors for real-time hand-motion recognition: toward practical applications

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National Science Review 2024年第2期11卷 231-263页

作者： Kyung Rok Pyun Kangkyu Kwon Myung Jin Yoo Kyun Kyu Kim Dohyeon Gong Woon-Hong Yeo Seungyong Han Seung Hwan Ko department of Mechanical engineering Seoul National University IEN Center for Human-Centric Interfaces and engineering Institute for Electronics and NanotechnologyGeorgia Institute of Technology School of electrical and computer engineering Georgia Institute of Technology department of Chemical engineering Stanford University department of Mechanical engineering Ajou University George W.Woodruff School of Mechanical engineering Georgia Institute of Technology Institute of Advanced Machinery and Design(SNU-IAMD) Seoul National University

Soft electromechanical sensors have led to a new paradigm of electronic devices for novel motion-based wearable applications in our daily lives. However, the vast amount of random and unidentified signals generated by complex body motions has hindered the precise recognition and practical application of this technology. Recent advancements in artificial-intelligence technology have enabled significant strides in extracting features from massive and intricate data sets, thereby presenting a breakthrough in utilizing wearable sensors for practical applications. Beyond traditional machine-learning techniques for classifying simple gestures, advanced machine-learning algorithms have been developed to handle more complex and nuanced motion-based tasks with restricted training data sets. Machine-learning techniques have improved the ability to perceive, and thus machine-learned wearable soft sensors have enabled accurate and rapid human-gesture recognition, providing real-time feedback to users. This forms a crucial component of future wearable electronics, contributing to a robust human–machine interface. In this review, we provide a comprehensive summary covering materials, structures and machine-learning algorithms for hand-gesture recognition and possible practical applications through machine-learned wearable electromechanical sensors.

关键词： wearable sensor soft electronics artificial intelligence machine learning human–machine interfaces gesture recognition

An Enhanced Hybrid Model Based on CNN and BiLSTM for Identifying Individuals via Handwriting Analysis

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computer Modeling in engineering & sciences 2024年第8期140卷 1689-1710页

作者： Md.Abdur Rahim Fahmid Al Farid Abu Saleh Musa Miah Arpa Kar Puza Md.Nur Alam Md.Najmul Hossain Sarina Mansor Hezerul Abdul Karim department of computer Science and engineering Pabna University of Science and TechnologyPabna6600Bangladesh Faculty of engineering Multimedia UniversityCyberjaya63100Malaysia department of computer Science and engineering Bangladesh Army University of Science and TechnologySaidpur5311Bangladesh department of Mathematics Pabna University of Science and TechnologyPabna6600Bangladesh department of electrical Electronic and Communication EngineeringPabnaUniversity of Science and TechnologyPabna6600Bangladesh department of electrical and Communication engineering Pabna University of Science and TechnologyPabna6600Bangladesh

Handwriting is a unique and significant human feature that distinguishes them from one another.There are many researchers have endeavored to develop writing recognition systems utilizing specific signatures or symbols for person identification through verification.However,such systems are susceptible to forgery,posing security risks.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.In response to these challenges,we propose an innovative hybrid technique for individual identification based on independent handwriting,eliminating the reliance on specific signatures or symbols.Our innovative method is intricately designed,encompassing five distinct phases:data collection,preprocessing,feature extraction,significant feature selection,and classification.One key advancement lies in the creation of a novel dataset specifically tailored for Bengali handwriting(BHW),setting the foundation for our comprehensive approach.Post-preprocessing,we embarked on an exhaustive feature extraction process,encompassing integration with kinematic,statistical,spatial,and composite features.This meticulous amalgamation resulted in a robust set of 91 features.To enhance the efficiency of our system,we employed an analysis of variance(ANOVA)F test and mutual information scores approach,meticulously selecting the most pertinent features.In the identification phase,we harnessed the power of cutting-edge deep learning models,notably the Convolutional Neural Network(CNN)and Bidirectional Long Short-Term Memory(BiLSTM).These models underwent rigorous training and testing to accurately discern individuals based on their handwriting characteristics.Moreover,our methodology introduces a groundbreaking hybrid model that synergizes CNN and BiLSTM,capitalizing on fine motor features for enhanced individual classifications.Crucially,our experimental results underscore the superiority of our approach.The CNN,B

关键词： Bengali handwriting(BHW) person identification convolutional neural network(CNN) BiLSTM

Human Gait Recognition for Biometrics Application Based on Deep Learning Fusion Assisted Framework

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computers, Materials & Continua 2024年第1期78卷 357-374页

作者： Ch Avais Hanif Muhammad Ali Mughal Muhammad Attique Khan Nouf Abdullah Almujally Taerang Kim Jae-Hyuk Cha department of electrical engineering HITEC UniversityTaxila47080Pakistan department of computer Science and Mathematics Lebanese American UniversityBeirutLebanon department of computer Science HITEC UniversityTaxila47080Pakistan department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of computer Science Hanyang UniversitySeoul04763Korea

The demand for a non-contact biometric approach for candidate identification has grown over the past ten years.Based on the most important biometric application,human gait analysis is a significant research topic in computer vision.Researchers have paid a lot of attention to gait recognition,specifically the identification of people based on their walking patterns,due to its potential to correctly identify people far away.Gait recognition systems have been used in a variety of applications,including security,medical examinations,identity management,and access control.These systems require a complex combination of technical,operational,and definitional considerations.The employment of gait recognition techniques and technologies has produced a number of beneficial and well-liked applications.Thiswork proposes a novel deep learning-based framework for human gait classification in video sequences.This framework’smain challenge is improving the accuracy of accuracy gait classification under varying conditions,such as carrying a bag and changing clothes.The proposed method’s first step is selecting two pre-trained deep learningmodels and training fromscratch using deep transfer learning.Next,deepmodels have been trained using static hyperparameters;however,the learning rate is calculated using the particle swarmoptimization(PSO)algorithm.Then,the best features are selected from both trained models using the Harris Hawks controlled Sine-Cosine optimization algorithm.This algorithm chooses the best features,combined in a novel correlation-based fusion technique.Finally,the fused best features are categorized using medium,bi-layer,and tri-layered neural networks.On the publicly accessible dataset known as the CASIA-B dataset,the experimental process of the suggested technique was carried out,and an improved accuracy of 94.14% was achieved.The achieved accuracy of the proposed method is improved by the recent state-of-the-art techniques that show the significance of this work.

关键词： Gait recognition covariant factors biometric deep learning fusion feature selection

A Hybrid Cybersecurity Algorithm for Digital Image Transmission over Advanced Communication Channel Models

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computers, Materials & Continua 2024年第4期79卷 201-241页

作者： Naglaa F.Soliman Fatma E.Fadl-Allah Walid El-Shafai Mahmoud I.Aly Maali Alabdulhafith Fathi E.Abd El-Samie department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia department of Electronics and Communications Faculty of EngineeringZagazig UniversityZagazig44519Egypt Security engineering Lab Computer Science DepartmentPrince Sultan UniversityRiyadh11586Saudi Arabia department of Electronics and electrical Communications engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt

The efficient transmission of images,which plays a large role inwireless communication systems,poses a significant challenge in the growth of multimedia technology.High-quality images require well-tuned communication standards.The Single Carrier Frequency Division Multiple Access(SC-FDMA)is adopted for broadband wireless communications,because of its low sensitivity to carrier frequency offsets and low Peak-to-Average Power Ratio(PAPR).Data transmission through open-channel networks requires much concentration on security,reliability,and integrity.The data need a space away fromunauthorized access,modification,or deletion.These requirements are to be fulfilled by digital image watermarking and encryption.This paper ismainly concerned with secure image communication over the wireless SC-FDMA systemas an adopted communication standard.It introduces a robust image communication framework over SC-FDMA that comprises digital image watermarking and encryption to improve image security,while maintaining a high-quality reconstruction of images at the receiver side.The proposed framework allows image watermarking based on the Discrete Cosine Transform(DCT)merged with the Singular Value Decomposition(SVD)in the so-called DCT-SVD watermarking.In addition,image encryption is implemented based on chaos and DNA encoding.The encrypted watermarked images are then transmitted through the wireless SC-FDMA system.The linearMinimumMean Square Error(MMSE)equalizer is investigated in this paper to mitigate the effect of channel fading and noise on the transmitted images.Two subcarrier mapping schemes,namely localized and interleaved schemes,are compared in this paper.The study depends on different channelmodels,namely PedestrianAandVehicularA,with a modulation technique namedQuadratureAmplitude Modulation(QAM).Extensive simulation experiments are conducted and introduced in this paper for efficient transmission of encrypted watermarked images.In addition,different variants of SC-FDMA based on the Discrete Wavelet Transform(DWT),Discre

关键词： Cybersecurity applications image transmission channel models modulation techniques watermarking and encryption

AI for science: Predicting infectious diseases

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Journal of Safety Science and Resilience 2024年第2期5卷 130-146页

作者： Alexis Pengfei Zhao Shuangqi Li Zhidong Cao Paul Jen-Hwa Hu Jiaojiao Wang Yue Xiang Da Xie Xi Lu Institute of Automation Chinese Academy of SciencesUniversity of Chinese Academy of SciencesBejing 100090China Systems engineering Cornll UniversityIthacaNY 14853USA David Eccles School of Business the University of UtahUtahUSA Collge of electrical engineering Sichuan UniversityChina department of electrical engineering Shanghai Jiao Tong UniversityShanghai 200240China School of electrical engineering Southeast UniversityNanjing 210096China

The global health landscape has been persistently challenged by the emergence and re-emergence of infectious diseases.Traditional epidemiological models,rooted in the early 2oth century,have provided foundational in-sights into disease dynamics.However,the intricate web of modern global interactions and the exponential growth of available data demand more advanced predictive tools.This is where AI for Science(AI4S)comes into play,offering a transformative approach by integrating artificial intelligence(Al)into infectious disease pre-diction.This paper elucidates the pivotal role of AI4s in enhancing and,in some instances,superseding tradi-tional epidemiological methodologies.By harnessing AI's capabilities,AI4S facilitates real-time monitoring,sophisticated data integration,and predictive modeling with enhanced precision.The comparative analysis highlights the stark contrast between conventional models and the innovative strategies enabled by AI4S.In essence,Al4S represents a paradigm shift in infectious disease research.It addresses the limitations of traditional models and paves the way for a more proactive and informed response to future outbreaks.As we navigate the complexities of global health challenges,Al4S stands as a beacon,signifying the next phase of evolution in disease prediction,characterized by increased accuracy,adaptability,and efficiency.

关键词： AI for science(AI4S) Data integration Global healthchallenges Infectious disease prediction Predictive modeling Real-timemonitoring

Identification of Software Bugs by Analyzing Natural Language-Based Requirements Using Optimized Deep Learning Features

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computers, Materials & Continua 2024年第3期78卷 4379-4397页

作者： Qazi Mazhar ul Haq Fahim Arif Khursheed Aurangzeb Noor ul Ain Javed Ali Khan Saddaf Rubab Muhammad Shahid Anwar department of computer Science and engineering and International Bachelor’s Program in Informatics Yuan Ze UniversityZhongliTaoyuan320315Taiwan department of computer Science National University of PakistanRawalpindiPunjab46000Pakistan department of computer Software engineering MCSNational University of Sciences and TechnologyIslamabad44000Pakistan department of computer engineering College of Computer and Information SciencesKing Saud UniversityP.O.Box 51178Riyadh11543Saudi Arabia department of computer Science School of PhysicsEngineering and Computer ScienceUniversity of HertfordshireHatfieldAL109ABUK department of computer engineering College of Computing and InformaticsUniversity of SharjahSharjah27272United Arab Emirates department of AI and Software Gachon UniversitySeongnam-SI13120South Korea

Software project outcomes heavily depend on natural language requirements,often causing diverse interpretations and issues like ambiguities and incomplete or faulty requirements.Researchers are exploring machine learning to predict software bugs,but a more precise and general approach is needed.Accurate bug prediction is crucial for software evolution and user training,prompting an investigation into deep and ensemble learning methods.However,these studies are not generalized and efficient when extended to other datasets.Therefore,this paper proposed a hybrid approach combining multiple techniques to explore their effectiveness on bug identification problems.The methods involved feature selection,which is used to reduce the dimensionality and redundancy of features and select only the relevant ones;transfer learning is used to train and test the model on different datasets to analyze how much of the learning is passed to other datasets,and ensemble method is utilized to explore the increase in performance upon combining multiple classifiers in a model.Four National Aeronautics and Space Administration(NASA)and four Promise datasets are used in the study,showing an increase in the model’s performance by providing better Area Under the Receiver Operating Characteristic Curve(AUC-ROC)values when different classifiers were combined.It reveals that using an amalgam of techniques such as those used in this study,feature selection,transfer learning,and ensemble methods prove helpful in optimizing the software bug prediction models and providing high-performing,useful end mode.

关键词： Natural language processing software bug prediction transfer learning ensemble learning feature selection

Template-assisted growth of Co-BaTiO_(3) vertically aligned nanocomposite thin films with strong magneto-optical coupling effect

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Nano Research 2024年第4期17卷 3130-3138页

作者： Zedong Hu Juanjuan Lu Hongyi Dou Jianan Shen James PBarnard Juncheng Liu Xinghang Zhang Haiyan Wang School of electrical and computer engineering Purdue UniversityWest LafayetteIN 47906USA School of Materials engineering Purdue UniversityWest LafayetteIN 47906USA

Oxide-metal based nanocomposite thin films have attracted great interests owing to their unique anisotropic structure and physical properties.A wide range of Au-based oxide-metal nanocomposites have been demonstrated,while other metal systems are scarce due to the challenges in the initial nucleation and growth as well as possible interdiffusions of the metallic nanopillars.In this work,a unique anodic aluminum oxide(AAO)template was used to grow a thin Co seed layer and the following self-assembled metal-oxide(Co-BaTiO_(3))vertically aligned nanocomposite thin film layer.The AAO template allows the uniform growth of Co-seeds and successfully deposition of highly ordered Co pillars(with diameter<5 nm and interval between pillars<10 nm)inside the oxide matrix.Significant magnetic anisotropy and strong magneto-optical coupling properties have been observed.A thin Au-BaTiO_(3) template was also later introduced for further enhanced nucleation and ordered growth of the Co-nanopillars.Taking the advantage of such a unique nanostructure,a large out-of-plane(OP)coercive field(Hc)of~5000 Oe has been achieved,making the nanocomposite an ideal candidate for high density perpendicular magnetic tunneling junction(p-MTJ).A strong polar magneto-optical Kerr effect(MOKE)has also been observed which inspires a novel optical-based reading method of the MTJ states.

关键词： vertically aligned nanocomposite(VAN) Co BaTiO_(3) thin film magneto-optical coupling magnetic anisotropy