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2024 International Conference on Telecommunications and Intelligent Systems, ICTIS 2024

作者： Hamdani, Mostefa Sahnoune, Ahmed Merzougui, Mokhtar Douara, Abdelmalek Khatemi, Fouad University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Mathematics and Computer Science Algeria University El Wancharissi Laboratory of Electronics Computer Science and Applied Mathematics Faculty of Science and Technology Department of Science and Technology Algeria

ISBN: (纸本)9798331527396

The integration of Artificial Intelligence(AI) with Long-Term Evolution (LTE) networks offers substantial potential for improving communication infrastructure. By harnessing AI algorithms, it is possible to dynamically optimize network parameters, proactively predict and mitigate equipment failures, and enhance Quality of Service (QoS) by prioritizing critical services through real-time analysis of key performance indicators. This study focuses on predicting network throughput using Least Square Support Vector Machine (LS-SVM). The predictive models developed can trigger adaptive network reconfigurations, ensuring that the network responds promptly to changing conditions. Simulation results reveal that the integration of LS-SVM significantly improves prediction accuracy, enabling more effective implementation of self-organizing network strategies in real-time. © 2024 IEEE.

关键词： Long Term Evolution (LTE)

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Improved Manta Ray Foraging Optimizer-based SVM for Feature Selection Problems:A Medical Case Study

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Journal of Bionic Engineering 2024年第1期21卷 409-425页

作者： Adel Got Djaafar Zouache Abdelouahab Moussaoui Laith Abualigah Ahmed Alsayat Faculty of informatics University of Science and Technology Houari BoumedieneAlgiersAlgeria LRIA Laboratory University of Science and Technology Houari BoumedieneAlgiersAlgeria Computer Science Department University of Mohamed El Bachir El IbrahimiBordj Bou ArreridjAlgeria Computer Science Department University of Ferhat AbbasSetifAlgeria Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl al-Bayt UniversityMafraq 25113Jordan Department of Computer Science College of Computer and Information SciencesJouf UniversityJoufSaudi Arabia Department of Electrical and Computer Engineering Lebanese American University13-5053ByblosLebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman 19328Jordan MEU Research Unit Middle East UniversityAmman 11831Jordan Applied science research center Applied science private universityAmman 11931Jordan School of Computer Sciences Universiti Sains Malaysia11800 PenangMalaysia School of Engineering and Technology Sunway University Malaysia27500 Petaling JayaMalaysia

Support Vector Machine(SVM)has become one of the traditional machine learning algorithms the most used in prediction and classification ***,its behavior strongly depends on some parameters,making tuning these parameters a sensitive step to maintain a good *** the other hand,and as any other classifier,the performance of SVM is also affected by the input set of features used to build the learning model,which makes the selection of relevant features an important task not only to preserve a good classification accuracy but also to reduce the dimensionality of *** this paper,the MRFO+SVM algorithm is introduced by investigating the recent manta ray foraging optimizer to fine-tune the SVM parameters and identify the optimal feature subset *** proposed approach is validated and compared with four SVM-based algorithms over eight benchmarking ***,it is applied to a disease Covid-19 *** experimental results show the high ability of the proposed algorithm to find the appropriate SVM’s parameters,and its acceptable performance to deal with feature selection problem.

关键词： Support vector machine Parameters tuning Feature selection Bioinspired algorithms Manta ray foraging optimizer

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Drug sales forecasting in the pharmaceutical market using deep neural network algorithms

International Journal of Systematic Innovation

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International Journal of Systematic Innovation 2024年第3期8卷 63-83页

作者： Qassrawi, Noura Azzeh, Mohammad Hijjawi, Mohammad Department of Computer Science Applied Science Private University Amman Jordan Department of Data Science Princess Sumaya University for Technology Amman Jordan

Drug sales and price forecasting have become an attractive investigation topic due to their important role in the pharmaceutical industry, A sales forecast helps every business to make better business decisions in overall business planning, budgeting, marketing, and risk management. The traditional forecasting method focuses on a conventional statistical model, which highly depends on the availability of historical sales data. However, for new drug entities, where not enough historical data is available, new methods of Machine Learning are applied. The aim of this paper is to identify an efficient Deep Neural Network algorithm suitable to forecast drug sales and pricing by applying Deep Neural Network Algorithms such as Multilayer Perceptron, Convolutional Neural Network, and Long Short-Term Memory, which are expected to perform well on this issue. The results are carried out to determine the efficiency of these algorithms by evaluating the performances of the models using MAE and RMSE performance metrics to identify the best algorithm for Drug Sales and Price Forecasting. The accepted accuracy should be more than 80\% of the actual value for quantity which is less than three thousand by unit and less than two dollars (USD) for price, Based on the results of the experiments Long Short Term Memory performed better than MLP and CNN for generating predictions with average Root Mean Square Error of for sales is 1.28(k) and Mean Absolute Error of about 0.85(k), and with average Root Mean Square Error for USD Prices is about 0.75, and Mean Absolute Error is about 0.44. The forecasts are then used to adjust stock levels according to the predictions. © (2024), (Society of Sytematic Innovation). All rights reserved.

关键词： Convolutional Neural Network Drug sales Forecasting Long Short-Term Memory Multilayer Perceptron

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Suspicious transaction alert and blocking system for cryptocurrency exchanges in metaverse’s social media universes: RG-guard

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Neural Computing and Applications 2024年第30期36卷 18825-18840页

作者： Gürfidan, Remzi Computer Programming Department Yalvaç Technical Sciences Vocational School Isparta University of Applied Science Isparta Turkey

In this study, we propose an effective system called RG-Guard that detects potential risks and threats in the use of cryptocurrencies in the metaverse ecosystem. In order for the RG-Guard engine to detect suspicious transactions, Ethereum network transaction information and phishing wallet addresses were collected, and a unique dataset was created after the data preprocessing process. During the data preprocessing process, we manually distinguished the features within the original dataset that contained potential risk indicators. The learning process of the RG-Guard engine in risk classification was achieved by developing a deep learning model based on LSTM + Softmax. In the training process of the model, RG-Guard was optimised for maximum accuracy, and optimum hyperparameters were obtained. The reliability and dataset performance of the preferred LSTM + Softmax model were verified by comparing it with algorithms used in risk classification and detection applications in the literature (Decision tree, XG boost, Random forest and light gradient boosting machine). Accordingly, among the trained models, LSTM + Softmax has the highest accuracy with an F1-score of 0.9950. When a cryptocurrency transaction occurs, RG-Guard extracts the feature vectors of the transaction and assigns a risk level between 1 and 5 to the parameter named βrisk. Since transactions with βrisk > = 3 are labelled as suspicious transactions, RG-Guard blocks this transaction. Thus, thanks to the use of the RG-Guard engine in metaverse applications, it is aimed to easily distinguish potential suspicious transactions from instant transactions. As a result, it is aimed to detect and prevent instant potential suspicious transactions with the RG-Guard engine in money transfers, which have the greatest risk in cryptocurrency transactions and are the target of fraud. The original dataset prepared in the proposed study and the hybrid LSTM + Softmax model developed specifically for the model are expected to c

关键词： Decision trees

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Artificial Intelligence into Multimedia Deepfakes Creation and Detection

Artificial Intelligence into Multimedia Deepfakes Creation a...

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2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Khder, Moaiad Ahmed Shorman, Samer Aldoseri, Duaij Thabit Saeed, Mohammed Mansoor Applied Science University Department of Computer Science Bahrain

ISBN: (纸本)9781665463720

Artificial intelligence has enabled deepfakes, or fake videos that closely resemble real ones, commercially viable (AI). As according to our research, people are less likely to believe social media news, but they are also more likely to be dubious than to be fooled by deepfakes. This finding combines ideas about how effective visual communication is and how uncertainty weakens public discourse confidence. The methodology for this article will based on reviewing the related articles to explore the main components and solutions in the deepfakes. In this article will study and review the deepfakes and it impacts in different multimedia with AI tools. Therefore, that conclude that deepfakes may increase already existing hazards to countries and online civic cultures by increasing general mistrust and doubt in the most of online resources. © 2023 IEEE.

关键词： Visual communication

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Employing Artificial Intelligence Methods for the Diagnosis of Autism Spectrum Disorder in Children 5

Employing Artificial Intelligence Methods for the Diagnosis ...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Sridevi, R. Joy, Helen K Karthikeyan, K.J. Gopika, S.S. Biju, Neha Seirah Shriniha, P.A. Department of Computer Science Bangalore India PSG Institute of Technology and Applied Research Department of Computer Science Coimbatore India

ISBN: (纸本)9798331518097

Accurate and timely diagnosis of disorder known as autism spectrum disorder (ASD) is not an easy task due to the complicated neurodevelopmental condition's high clinical presentation variation. In order to improve the diagnostic procedure for ASD in pediatric patients, machine learning (ML) techniques have come to light as potential approaches. The previous surveys about the practice of ML algorithms for diagnosing ASD in children has been thoroughly reviewed and summarized. The supervised and unsupervised learning, feature selection, and ensemble methods used in ASD research are among the many ML techniques that is methodically examined. The necessity of large-scale, diverse datasets, cross-validation methods, and interpretability are emphasized over the advantages, disadvantages, and potential future directions of ML-based ASD diagnostic models. This study attempts to offer insights for researchers, clinicians, and other stakeholders in the field of ASD diagnosis by critically analyzing the current status of ML in ASD Diagnosis. © 2024 IEEE.

关键词： Self-supervised learning

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Online Games Data Storing Methods, Vulnerabilities, and Solutions

Online Games Data Storing Methods, Vulnerabilities, and Solu...

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2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Khder, Moaiad Ahmed Shorman, Samer Ali, Ahmed F. Al-Mudaifa, Mohammed O. Applied Science University Department of Computer Science Bahrain

ISBN: (纸本)9781665463720

online games provide a competitive environment where most players compete and try to gain advantages by efficiently using game mechanics and resources provided by the gameplay loop. This competitive nature of online games sometimes goes above and beyond where some players resort to using unethical methods such as: Cheating, exploiting bugs and vulnerabilities which provide them with unfair advantages over most normal players. This results in normal players leaving the game as they lose the desire to continue playing in an unfair environment. Failure to combat such behaviour causes huge damage to the player base, game itself, company revenue and reputation. Persistent Massively Multiplayer Online Games can contain thousands of players sharing the same world view. the large number of updates performed each second has made storing the game state a big challenge, the game state should not be lost when a crash or failure to the server happen, commonly implemented solution fixes this problem by trading consistency with efficiency due to the database limited capability of handling large amount of data. This paper aims to shed a light on current popular user data storage methods, their vulnerabilities and propose a solution.. This paper aims to shed a light on current popular user data storage methods, their vulnerabilities and propose a solution © 2023 IEEE.

关键词： Digital storage

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Review Study of the Impact of Artificial Intelligence on Cyber Security

Review Study of the Impact of Artificial Intelligence on Cyb...

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2023 International Conference on IT Innovation and Knowledge Discovery, ITIKD 2023

作者： Khder, Moaiad Ahmed Shorman, Samer Showaiter, Dana Anwar Zowayed, Areej Salah Zowayed, Sara Isa Applied Science University Department of Computer Science Bahrain

ISBN: (纸本)9781665463720

Data breaches, identity theft, and data espionage are all consequences of cyber-attacks, which harm millions of individuals and organizations. Due to a lack of cybersecurity personnel with up-to-date data, as result, new security concerns develop as technology improves. Human interaction is just insufficient for attack analysis and suitable reaction;similarly, the support of senior experts and security firms is insufficient for cyber-attack prevention. However, the strain on specialists can be minimized by using AI to counter cyber-attacks. Because the application of AI has the potential to improve cyber security, for example, by mining data to establish the origins of assaults or even prevent them in a variety of ways, also it enables malware detection by benefiting from the data of earlier cyber-attacks. In this study, we try to review the impact of artificial intelligence on cyber security by examining various recent published studies from different resources. © 2023 IEEE.

关键词： Crime

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Developing a Secure Framework Using Feature Selection and Attack Detection Technique

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computers, Materials & Continua 2023年第2期74卷 4183-4201页

作者： Mahima Dahiya Nitin Nitin Department of Electrical Engineering and Computer Science College of Engineering and Applied ScienceUniversity of Cincinnati2600 Clifton AveCincinnatiOH 45221USA

Intrusion detection is critical to guaranteeing the safety of the data in the *** though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion *** challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,*** to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection *** data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as ***,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack *** on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the ***,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced *** selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter ***,the selected features are trained and tested for detecting attacks using *** Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the

关键词： Cyber security data mining intrusion detection system(DataMIDS) marginal likelihood fisher information matrix(MLFIM) absolute median deviation based robust scalar(AMD-RS) functional perturbation(FP) inverse chi square based flamingo search optimization(ICS-FSO) hyperparameter tuned threshold based decision tree(HpTT-DT) Xavier normal distribution based relief(XavND-relief) and Bengio Nesterov momentum-based tuned generative adversarial network(BNM-tGAN)

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Efficient Communication in Wireless Sensor Networks Using Optimized Energy Efficient Engroove Leach Clustering Protocol

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Tsinghua science and Technology 2024年第4期29卷 985-1001页

作者： N.Meenakshi Sultan Ahmad A.V.Prabu J.Nageswara Rao Nashwan Adnan Othman Hikmat A.M.Abdeljaber R.Sekar Jabeen Nazeer SRM Institute of Science and Technology KattankulathurChennai 603203India Department of Computer Science College of Computer Engineering and SciencesPrince Sattam Bin Abdulaziz UniversityAlkharj 11942Saudi Arabia University Center for Research and Development(UCRD) Department of Computer Science and EngineeringChandigarh UniversityGharuanMohali 140413India Department of ECE Koneru Lakshmaiah Education FoundationVaddeswaramGuntur 522502India Department of Computer Science and Engineering Lakireddy Balireddy College of Engineering(A)NTR District 521230India Department of Computer Engineering College of EngineeringKnowledge UniversityErbil 44001Iraq Department of Computer Science Faculty of Information TechnologyApplied Science Private UniversityAmman 11937Jordan Department of Electronics and Communication Engineering(ECE) School of EngineeringPresidency UniversityBangalore 834001India

The Wireless Sensor Network(WSN)is a network that is constructed in regions that are inaccessible to human *** widespread deployment of wireless micro sensors will make it possible to conduct accurate environmental monitoring for a use in both civil and military *** make use of these data to monitor and keep track of the physical data of the surrounding environment in order to ensure the sustainability of the *** data have to be picked up by the sensor,and then sent to the sink node where they may be *** nodes of the WSNs are powered by batteries,therefore they eventually run out of *** energy restriction has an effect on the network life span and environmental *** objective of this study is to further improve the Engroove Leach(EL)protocol’s energy efficiency so that the network can operate for a very long time while consuming the least amount of *** lifespan of WSNs is being extended often using clustering and routing *** Meta Inspired Hawks Fragment Optimization(MIHFO)system,which is based on passive clustering,is used in this study to do *** cluster head is chosen based on the nodes’residual energy,distance to neighbors,distance to base station,node degree,and node *** on distance,residual energy,and node degree,an algorithm known as Heuristic Wing Antfly Optimization(HWAFO)selects the optimum path between the cluster head and Base Station(BS).They examine the number of nodes that are active,their energy consumption,and the number of data packets that the BS *** overall experimentation is carried out under the MATLAB *** the analysis,it has been discovered that the suggested approach yields noticeably superior outcomes in terms of throughput,packet delivery and drop ratio,and average energy consumption.

关键词： wireless sensor networks energy efficient engroove leach protocol meta inspired Hawks fragment optimization heuristic wing antfly optimization

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