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Encryption Algorithm for Securing Non-Disclosure Agreements in Outsourcing Offshore Software Maintenance

computers, Materials & Continua 2022年第11期73卷 3827-3845页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Nadeem Iqbal Nazri Kama Azri Azmi Ahmad Salman Khan Yasir Mahmood Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala TerengganuMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyaKuwait

Properly created and securely communicated,non-disclosure agreement(NDA)can resolve most of the common disputes related to outsourcing of offshore software maintenance(OSMO).Occasionally,these NDAs are in the form of *** the work is done offshore,these agreements or images must be shared through the Internet or stored over the *** breach of privacy,on the other hand,is a potential threat for the image owners as both the Internet and cloud servers are not void of *** article proposes a novel algorithm for securing the NDAs in the form of *** an agreement is signed between the two parties,it will be encrypted before sending to the cloud server or travelling through the public network,the *** the image is input to the algorithm,its pixels would be scrambled through the set of randomly generated rectangles for an arbitrary amount of *** confusion effects have been realized through an XOR operation between the confused image,and chaotic ***,5D multi-wing hyperchaotic system has been employed to spawn the chaotic vectors due to good properties of chaoticity it *** machine experimentation and the security analysis through a comprehensive set of validation metric vividly demonstrate the robustness,defiance to the multifarious threats and the prospects for some real-world application of the proposed encryption algorithm for the NDA images.

关键词： Non-disclosure agreement encryption decryption secret key chaoticmap confusion diffusion

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Intelligent DoS Attack Detection with Congestion Control Technique for VANETs

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computers, Materials & Continua 2022年第7期72卷 141-156页

作者： R.Gopi Mahantesh Mathapati B.Prasad Sultan Ahmad Fahd N.Al-Wesabi Manal Abdullah Alohali Anwer Mustafa Hilal Department of Computer Science and Engineering Dhanalakshmi Srinivasan Engineering CollegePerambalur621212India Department of Computer Science and Engineering RajaRajeswari College of EngineeringBengaluru560074India Department of Information Technology Vignan’s Institute of Information TechnologyVisakhapatnam530049India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj11942Saudi Arabia Department of Computer Science College of Science&Arts at MahayilKing Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a15347Yemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia

VehicularAd hoc Network(VANET)has become an integral part of Intelligent Transportation systems(ITS)in today’s *** is a network that can be heavily scaled up with a number of vehicles and road side units that keep fluctuating in real *** is susceptible to security issues,particularly DoS attacks,owing to maximum unpredictability in ***,effective identification and the classification of attacks have become the major requirements for secure data transmission in *** the same time,congestion control is also one of the key research problems in VANET which aims at minimizing the time expended on roads and calculating travel time as well as waiting time at intersections,for a *** this motivation,the current research paper presents an intelligent DoS attack detection with Congestion Control(IDoS-CC)technique for *** presented IDoSCC technique involves two-stage processes namely,Teaching and Learning Based Optimization(TLBO)-based Congestion Control(TLBO-CC)and Gated Recurrent Unit(GRU)-based DoS detection(GRU-DoSD).The goal of IDoS-CC technique is to reduce the level of congestion and detect the attacks that exist in the *** algorithm is also involved in IDoS-CC technique for optimization of the routes taken by vehicles via traffic signals and to minimize the congestion on a particular route instantaneously so as to assure minimal fuel *** is applied to avoid congestion on ***,GRU-DoSD model is employed as a classification model to effectively discriminate the compromised and genuine vehicles in the *** outcomes from a series of simulation analyses highlight the supremacy of the proposed IDoS-CC technique as it reduced the congestion and successfully identified the DoS attacks in network.

关键词： VANET intelligent transportation systems congestion control attack detection dos attack deep learning

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Adaptive Deep Learning Model to Enhance Smart Greenhouse Agriculture

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computers, Materials & Continua 2023年第11期77卷 2545-2564页

作者： Medhat A.Tawfeek Nacim Yanes Leila Jamel Ghadah Aldehim Mahmood A.Mahmood Department of Computer Science College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia Department of Computer Science Faculty of Computers and InformationMenoufia UniversityP.O.Box 6121890MenoufiaEgypt Department of Software Engineering College of Computer and Information SciencesJouf UniversityP.O.Box 72314SkakaSaudi Arabia RIADI Laboratory La Manouba UniversityP.O.Box2010ManoubaTunisia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Information Systems and Technology Faculty of Graduate Studies and ResearchCairo UniversityP.O.Box 3753450GizaEgypt

The trend towards smart greenhouses stems from various factors,including a lack of agricultural land area owing to population concentration and housing construction on agricultural land,as well as water *** study proposes building a full farming adaptation model that depends on current sensor readings and available datasets from different agricultural research *** proposed model uses a one-dimensional convolutional neural network(CNN)deep learning model to control the growth of strategic crops,including cucumber,pepper,tomato,and *** proposed model uses the Internet of Things(IoT)to collect data on agricultural operations and then uses this data to control and monitor these operations in real *** helps to ensure that crops are getting the right amount of fertilizer,water,light,and temperature,which can lead to improved yields and a reduced risk of crop *** dataset is based on data collected from expert farmers,the photovoltaic construction process,agricultural engineers,and research *** experimental results showed that the precision,recall,F1-measures,and accuracy of the one-dimensional CNN for the tested dataset were approximately 97.3%,98.2%,97.25%,and 97.56%,*** new smart greenhouse automation system was also evaluated on four crops with a high turnover *** system has been found to be highly effective in terms of crop productivity,temperature management and water conservation.

关键词： Greenhouse wireless sensor network deep learning Internet of Things strategic crops monitoring smart irrigation

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A* Search Algorithm for an Optimal Investment Problem in Vehicle-Sharing systems 12th

A* Search Algorithm for an Optimal Investment Problem in ...

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12th International Conference on Computational Data and Social Networks, CSoNet 2023

作者： Le, Ba Luat Martin, Layla Demir, Emrah Vu, Duc Minh ORLab and Faculty of Computer Science Phenikaa University Hanoi Viet Nam Department of Industrial Engineering and Eindhoven AI Systems Institute Eindhoven University of Technology Eindhoven Netherlands Cardiff Business School Cardiff University Cardiff United Kingdom

ISBN: (纸本)9789819706686

We study an optimal investment problem that arises in the context of the vehicle-sharing system. Given a set of locations to build stations, we need to determine i) the sequence of stations to be built and the number of vehicles to acquire in order to obtain the target state where all stations are built, and ii) the number of vehicles to acquire and their allocation in order to maximize the total profit returned by operating the system when some or all stations are open. The profitability associated with operating open stations, measured over a specific time period, is represented as a linear optimization problem applied to a collection of open stations. With operating capital, the owner of the system can open new stations. This property introduces a set-dependent aspect to the duration required for opening a new station, and the optimal investment problem can be viewed as a variant of the Traveling Salesman Problem (TSP) with set-dependent cost. We propose an A* search algorithm to address this particular variant of the TSP. Computational experiments highlight the benefits of the proposed algorithm in comparison to the widely recognized Dijkstra algorithm and propose future research to explore new possibilities and applications for both exact and approximate A* algorithms. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Traveling salesman problem

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A Multimodal Learning-based Approach for Autonomous Landing of UAV

A Multimodal Learning-based Approach for Autonomous Landing ...

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IEEE/ASME International Conference on Mechtronic and Embedded systems and Applications, MESA

作者： Francisco S. Neves Luís M. Branco Maria I. Pereira Rafael M. Claro Andry M. Pinto Department of Electrical and Computer Engineering (DEEC) Faculty of Engineering of University of Porto (FEUP) Institute for Systems and Computer Engineering - Technology and Science (INESC TEC) Porto Portugal

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

In the field of autonomous Unmanned Aerial Vehicles (UAVs) landing, conventional approaches fall short in delivering not only the required precision but also the resilience against environmental disturbances. Yet, learning-based algorithms can offer promising solutions by leveraging their ability to learn the intelligent behaviour from data. On one hand, this paper introduces a novel multimodal transformer-based Deep Learning detector, that can provide reliable positioning for precise autonomous landing. It surpasses standard approaches by addressing individual sensor limitations, achieving high reliability even in diverse weather and sensor failure conditions. It was rigorously validated across varying environments, achieving optimal true positive rates and average precisions of up to 90%. On the other hand, it is proposed a Reinforcement Learning (RL) decision-making model, based on a Deep Q-Network (DQN) rationale. Initially trained in simulation, its adaptive behaviour is successfully transferred and validated in a real outdoor scenario. Furthermore, this approach demonstrates rapid inference times of approximately 5ms, validating its applicability on edge devices.

关键词： Training Wind Image edge detection Decision making Detectors Reinforcement learning Robot sensing systems Autonomous aerial vehicles Transformers Resilience

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Pothole Detection in Asphalt Roads: A Comprehensive Approach for Enhanced Road Maintenance and Safety with AlexNet Model

Pothole Detection in Asphalt Roads: A Comprehensive Approach...

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2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Abdelmalak, Mark Emad Sobhi Khodadadi, Nima Zaki, Ahmed Mohamed Eid, Marwa M. Rizk, Faris H. Ibrahim, Abdelhameed Abdelhamid, Abdelaziz A. Abualigah, Laith El-Kenawy, El-Sayed M. Delta Higher Institute of Engineering and Technology Department of Communications and Electronics Mansoura35111 Egypt University of Miami Department of Civil and Architectural Engineering Coral GablesFL United States Computer Science and Intelligent Systems Research Center BlacksburgVA24060 United States Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Bahrain Polytechnic Faculty of Engineering Isa Town Bahrain Ain Shams University Faculty of Computer and Information Sciences Department of Computer Science Cairo11566 Egypt Al al-Bayt University Computer Science Department Mafraq25113 Jordan Middle East University MEU Research Unit Amman11831 Jordan Jadara University Jadara Research Center Irbid Jordan

ISBN: (纸本)9798350351408

The research article described in this paper puts forward a novel method of using an integrated software approach and high-end hardware devices for adaptive and intelligent detection of potholes on asphalt roads. The Pothole Detection Dataset is used for the dataset analysis, and we put VGG19Net, ResNet-50, GoogLeNet, and AlexNet among the computer vision models to analyze the applicability of these models. Different types of networks were compared, and AlexNet showed the best results as it achieved 92.15% accuracy, 91.38 % sensitivity (TPR), and a surprisingly high F-score, which reached 96.52%. Furthermore, by using its time of 279.35 seconds, which might be considered very fast, AlexNet shows many strengths in helping to do this, as well as identifying road anomalies, making it a perfect candidate for real-world utilization. This research demonstrates the emergence of sophisticated integrated pothole repair solutions, emphasizing the importance of both software and hardware in developing sophisticated pothole detection. Practices and this research could be an example for further surveying road inspection technologies. © 2024 IEEE.

关键词： Digital storage

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An Improved Approximation Algorithm for the Max-3-Section Problem 31

An Improved Approximation Algorithm for the Max-3-Section Pr...

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31st Annual European Symposium on Algorithms, ESA 2023

作者： Katzelnick, Dor Pillai, Aditya Schwartz, Roy Singh, Mohit The Henry and Marilyn Taub Faculty of Computer Science Technion Haifa Israel H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology AtlantaGA United States

ISBN: (纸本)9783959772952

We consider the Max-3-Section problem, where we are given an undirected graph G = (V,E) equipped with non-negative edge weights w : E → R+ and the goal is to find a partition of V into three equisized parts while maximizing the total weight of edges crossing between different parts. Max-3-Section is closely related to other well-studied graph partitioning problems, e.g., Max-Cut, Max-3-Cut, and Max-Bisection. We present a polynomial time algorithm achieving an approximation of 0.795, that improves upon the previous best known approximation of 0.673. The requirement of multiple parts that have equal sizes renders Max-3-Section much harder to cope with compared to, e.g., Max-Bisection. We show a new algorithm that combines the existing approach of Lassere hierarchy along with a random cut strategy that suffices to give our result. © Dor Katzelnick, Aditya Pillai, Roy Schwartz, and Mohit Singh

关键词： Approximation algorithms

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Arabic Abstractive Summarization Using the Multilingual T5 Model

Arabic Abstractive Summarization Using the Multilingual T5 M...

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Computing and Informatics (ICCI), International Conference on

作者： Abdelrhman A. Karim Mohammed Usama Manar A. Ibrahim Youssef Hatem Waleed Wael Ahmad T. Mazrua Ghada K. El-Monayer Magy Elbanhawy Khaled Foad Ibrahim F. Moawad Artificial Intelligence Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer Engineering Department Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Faculty of Computer Science and Engineering New Mansoura University Dakhlia Egypt Computer and System Department Faculty of Engineering Portsaid University Egypt Information Systems Faculty of Computers and Artificial Intelligence Benha University Egypt Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

Natural language processing (NLP) is a branch of artificial intelligence (AI) that enables computers to comprehend, generate, and manipulate human language. Natural language processing can interrogate the data with natural language text or voice. Abstractive Text Summarization is based on the Natural Language Processing technique that tries to provide new and more concise textual summaries for huge texts. Artificial intelligence and deep learning techniques are used in abstractive summarization to examine the text's essential information and create a new summary that conveys the content more concisely and accurately. This type of summary differs from normal extractive summarizing in that it can generate new summaries beyond simply extracting key lines from the source text. This study presents an abstractive Arabic summarization based on the Multilingual T5 (MT5) model AASMT5 to address these concerns. This technique is based on deep neural networks and models like transformers, which have changed the ability to summarize text. This research has explored the various types of summarizations and highlighted the significance of two prominent techniques: abstractive and extractive summarization. This research describes a complete process for developing an Arabic abstractive summarization model using the MT5 architecture. Experiments on different datasets show that this model achieves state-of-the-art results across MT5.

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A novel myocarditis detection combining deep reinforcement learning and an improved differential evolution algorithm

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CAAI Transactions on Intelligence Technology 2024年第6期9卷 1347-1360页

作者： Jing Yang Touseef Sadiq Jiale Xiong Muhammad Awais Uzair Aslam Bhatti Roohallah Alizadehsani Juan Manuel Gorriz Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala LumpurMalaysia Centre for Artificial Intelligence Research(CAIR) Department of Information and Communication TechnologyUniversity of AgderGrimstadNorway Department of Creative Technologies Air UniversityIslamabadPakistan School of Information and Communication Engineering Hainan UniversityHaikouHainanChina Institute for Intelligent Systems Research and Innovation(IISRI) Deakin UniversityWaurn PondsVictoriaAustralia Data Science and Computational Intelligence Institute University of GranadaGranadaSpain

Myocarditis is a serious cardiovascular ailment that can lead to severe consequences if not promptly *** is triggered by viral infections and presents symptoms such as chest pain and heart *** detection is crucial for successful treatment,and cardiac magnetic resonance imaging(CMR)is a valuable tool for identifying this ***,the detection of myocarditis using CMR images can be challenging due to low contrast,variable noise,and the presence of multiple high CMR slices per *** overcome these challenges,the approach proposed incorporates advanced techniques such as convolutional neural networks(CNNs),an improved differential evolution(DE)algorithm for pre-training,and a reinforcement learning(RL)-based model for *** this method presented a significant challenge due to the imbalanced classification of the Z-Alizadeh Sani myocarditis dataset from Omid Hospital in *** address this,the training process is framed as a sequential decision-making process,where the agent receives higher rewards/penalties for correctly/incorrectly classifying the minority/majority ***,the authors suggest an enhanced DE algorithm to initiate the backpropagation(BP)process,overcoming the initialisation sensitivity issue of gradient-based methods like back-propagation during the training *** effectiveness of the proposed model in diagnosing myocarditis is demonstrated through experimental results based on standard performance ***,this method shows promise in expediting the triage of CMR images for automatic screening,facilitating early detection and successful treatment of myocarditis.

关键词： classification differential evolution myocarditis reinforcement learning

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Performance Improvements of Vehicular PKI Protocol for the Security of V2X Communications 46

Performance Improvements of Vehicular PKI Protocol for the S...

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46th International Conference on Telecommunications and Signal Processing, TSP 2023

作者： Farran, Hassan Khoury, David Budapest University of Technology and Economics Faculty of Electrical Engineering and Informatics Department of Networked Systems and Services BudapestH-1111 Hungary American University of Science and Technology Department of Computer Science and Information and Communication Technology Beirut Lebanon

ISBN: (纸本)9798350303964

Vehicle-to-Everything (V2X) technology allows vehicles to communicate with numerous transportation system components. To provide safe communication between vehicles and other entities, V2X depends on Public Key Infrastructure (PKI). However, the present PKI employed in V2X has certain drawbacks, including scalability and high computational cost. This article offers an enhancement to the current PKI in V2X that tackles these constraints by managing and distributing keys using blockchain technology. The suggested Public Key Infrastructure enhancement is intended to be secure, efficient, and scalable, providing a more solid and trustworthy foundation for V2Xcommunication. The article explains the suggested PKI enhancement and examines its advantages and disadvantages. Overall, the suggested Public Key Infrastructure enhancement has the potential to increase the security and dependability of Vehicle-to-Everything communication, allowing for a secure and more effective transportation system. © 2023 IEEE.

关键词： Vehicle to Everything

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