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3rd International Conference on Embedded systems and Artificial Intelligence, ESAI 2024

作者： McHichou, Ismail Ouchker, Rabab Tahiri, Mohamed Amine Amakdouf, Hicham Jamil, Mohammed Ouazzani Qjidaa, Hassan Sidi Mohamed Ben Abdellah-Fez University Laboratory of Electronic Signals and Systems of Information Dhar El Mahrez Faculty of Science Fez Morocco Sidi Mohamed Ben Abdellah-Fez University Engineering Systems and Applications Laboratory National School of Applied Sciences Fez Morocco Fez Morocco

ISBN: (纸本)9798331522056

This paper presents an innovative approach to real-Time emotion recognition, integrating Canny's edge detection and deep learning on an embedded system, namely the Raspberry Pi 3 board. Our method combines the edge detection capabilities of the Canny algorithm with the robust feature extraction and analytical strengths of advanced deep learning techniques. The pre-processing phase involves applying Canny's edge detection to highlight facial contours and important features, which are subsequently processed by a convolutional neural network (CNN) to determine emotional states. This hybrid approach improves the accuracy of emotion recognition, enabling real-Time application on resource-constrained devices. Extensive experiments on reference datasets demonstrate the effectiveness of our method in real time. This work opens up new perspectives for developing portable and efficient emotion recognition systems for applications in healthcare, and surveillance. © 2024 IEEE.

关键词： Convolutional neural networks

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Comparison Model for Forecasting the Total Population in Bangkok 9

Comparison Model for Forecasting the Total Population in Ban...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Tipwong, Nattanicha Jitpattanakul, Anuchit Phaphan, Wikanda Nontapa, Chalermrat Mekruksavanich, Sakorn King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Applied Statistics Bangkok10800 Thailand King Mongkut's University of Technology North Bangkok Faculty of Applied Science Department of Mathematics Bangkok10800 Thailand Intelligent and Nonlinear Dynamic Innovations Research Center Thailand Chiang Mai University Faculty of Science Department of Statistics Chiang Mai50200 Thailand Research Group in Statistical Learning and Inference Thailand University of Phayao School of Information and Communication Department of Computer Engineering Phayao56000 Thailand

ISBN: (纸本)9798350318241

The primary aim of this article is to investigate, contrast, and formulate a time series model to predict Bangkok's overall population. In this study, we intend to propose a hybrid model that combines the Autoregressive Moving Integrated Average (ARIMA) model and machine learning techniques, utilizing a Multilayer Perceptron (MLP). Our data source comprises monthly data from 2002 to 2022, totaling 252 data points, obtained from the official registration statistics system of the Registration Administration Office, Department of Provincial Administration. This dataset has been divided into two subsets: the first subset covers the years from 2002 to 2021, encompassing 240 data points, which will be used for constructing predictive models. The second subset consists of the data for the year 2022, comprising 12 data points, which will serve as a means to compare the accuracy of various forecasting methods. The evaluation criteria employed for this purpose are the minimum mean absolute error (MAE), the mean square error (MSE) and the mean absolute percentage error (MAPE). Furthermore, we will utilize the Python programming language to facilitate data analysis. © 2024 IEEE.

关键词： Mean square error

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Bang-bang preparation of quantum many-body ground states in two dimensions: Optimization of the algorithm with a two-dimensional tensor network

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Physical Review B 2024年第23期109卷 235124-235124页

作者： Yintai Zhang Jacek Dziarmaga Doctoral School of Exact and Natural Sciences Institute of Theoretical Physics Faculty of Physics Astronomy and Applied Computer Science Mark Kac Center for Complex Systems Research

A bang-bang (BB) algorithm prepares the ground state of a two-dimensional (2D) quantum many-body Hamiltonian H=H1+H2 by evolving an initial product state alternating between H1 and H2. We use the neighborhood tensor update to simulate the BB evolution with an infinite pair-entangled projected state (iPEPS). The alternating sequence is optimized with the final energy as a cost function. The energy is calculated with a tangent space power method for the sake of its stability. The method is benchmarked in the 2D transverse field quantum Ising model near its quantum critical point against a ground state obtained with variational optimization of the iPEPS. The optimal BB sequence differs nonperturbatively from a sequence simulating quantum annealing or adiabatic preparation (AP) of the ground state. The optimal BB energy converges with the number of bangs much faster than the optimal AP energy.

关键词： Quantum control Ising model Tensor network methods

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Optimized deep learning models based-sentiment analysis approach for identifying healthcare rumors over social media 2nd

Optimized deep learning models based-sentiment analysis appr...

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2nd International Conference on Next-Generation Computing and information systems, ICNGCIS 2023

作者： Kumar, Pramod Vashishtha, Ela Alghayadh, Faisal Yousef Wasim Bhatt, Mohammed Bala, Suchitra Sreejitha, T.S. Computer Science and Technology Ganga Institute of Technology and Management Haryana Kablana Jhajjar India Lead Analytics Texas Health Resources TX United States Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Model Institute of Engineering and Technology J&K Jammu India ICT & Cognitive Systems Sri Krishna Arts and Science College India St. Marys College Thrissur India

ISBN: (纸本)9781032738659

Research the mechanism of fake news refuting rumors in the real social network environment. Propose methods to evaluate the effect of rumor refuting and explore the factors that affect the impact of rumor refuting. Based on the current research results and assumptions, eight factors that affect the effect of rumor refuting are summed up, such as the proportion of the original fake news content. Experiments were conducted based on the rumor-refuting Weibo data. The experimental analysis showed that the proportion of the original fake news in the rumor-refuting information was negatively correlated with the rumor-refuting effect, and the explanation was positively correlated with the rumor-refuting effect suggestions for refuting rumors, including explaining why the original fake news is wrong to guiding social network fake news denying rumors. © 2025 The Author(s).

关键词： Sentiment analysis

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Comparative sensitivity analysis of single and multiple modifications in multi-criteria decision analysis 28th

Comparative sensitivity analysis of single and multiple modi...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Wieckowski, Jakub Bialon, Pawel Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland

In the area of decision-making, where complexities often exceed experts' analytical capabilities, this study addresses a critical gap by introducing a novel methodology for sensitivity analysis within the Multi-Criteria Decision Analysis (MCDA) problems. Current analytical frameworks struggle to comprehensively consider potential modifications to values within decision matrices, making it challenging to understand their impact in detail. To address this challenge, decision-makers need enhanced tools, and MCDA methods combined with systematic changes in selected elements of the decision matrix stand out as a promising approach. However, existing studies primarily focus on different criteria-weight scenarios, leaving an unexplored gap in the simultaneous modification of multiple values within the decision matrix. This paper integrates sensitivity analysis within the MCDA methods, extending its role in the comprehensive assessment of decision problems. Sensitivity analysis becomes crucial in offering decision-makers a broader perspective, aiding them in navigating the complexities of decision-making in dynamic environments. Recognizing the unexplored potential in sensitivity analysis, the study proposed a novel approach of simultaneous modification of multiple values in a decision matrix, offering an extension of conventional one-at-a-time modifications. As a Proof of Concept (PoC) research work, the study investigated whether the determined approach provides divergent preference scores compared to the traditional single-modification method across ten different MCDA techniques. The results showed that modifying multiple values simultaneously produced different preference scores of alternatives than in the case of a conventional single change, showing that additional insight knowledge could be extracted from this type of sensitivity analysis. © 2024 The Authors.

关键词： Sensitivity analysis

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AI-Based Algorithm for Zero-Day Attack Detection Using Reinforcement Learning

AI-Based Algorithm for Zero-Day Attack Detection Using Reinf...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Ola Nasir Waleed Amer Ala Hamarsheh Ali Mahmoud Ali Amani Abu Zaid Department of Computer Science Faculty of Information Technology Zarqa University Zarqa Jordan Department of Cyber Secyrity Faculty of Information Technology Zarqa University Zarqa Jordan Department of computer science Faculty of Information Technology Arab American University Department of Computer Network Faculty of Engineering Technology Al-Balqa Applied University Amman Jordan

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Zero-Day attacks represent one of the most challenging threats in modern cybersecurity, as they exploit unknown vulnerabilities and bypass traditional detection systems. In this paper, we propose a hybrid threat detection system that integrates three key components: a Random Forest-based supervised classification model for detecting known attack patterns, an autoencoder-based anomaly detection module to uncover novel threats, and a Reinforcement Learning (RL) agent to dynamically optimize response strategies in real-time. The proposed system achieves a detection Accuracy of 97.8% for known attacks. The autoencoder, trained exclusively on normal traffic, employs a dynamically determined threshold (set at 0.8409) to flag anomalous behavior indicative of Zero-Day attacks. Furthermore, the RL agent adapts quickly to evolving threats, effectively filtering out 53.2% of benign traffic while blocking 39.7% of attack traffic and quarantining 7.1% of ambiguous cases for further inspection. Experimental results demonstrate that this integrated approach not only enhances top-level threat prevention but also significantly reduces false alerts.

关键词： Heuristic algorithms Prevention and mitigation Autoencoders Reinforcement learning Threat assessment Real-time systems computer security Anomaly detection Random forests Testing

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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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Predictions of Gender from the Analysis of the Images of Iris and Ocular Regions

Predictions of Gender from the Analysis of the Images of Iri...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Masoud Husayn Mohamed A.E. Abdalla Dept. of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Dept. of Artificial Intelligence and Computer Systems Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

the investigation of biological measurements has become a powerful for classifications. The analysis of eye images for gender classification is proven that it is one of the reliable form identification. This paper examines use of support vector machines and CNN for gender predictions based on analyzing images of the ocular and iris regions for determining which one provides more accurate results. Feature extracted by HOG and LBP methods are examined by the SVM classifier. Whilst, pertained CNN is examined with considering the architecture of AlexNet and ResNet18. The proposed models are then evaluated on the MMU2 dataset which contains 995 images. The results of CNN with AlexNet has shown that the eye region yields an accuracy rate of 94.19% higher than the iris with an accuracy of 90.46%. This indicates the feasibility for applications in security, forensics, marketing, customer service improvement.

关键词： Support vector machines Biometrics Iris Accuracy Image analysis Forensics Machine learning Feature extraction Security Reliability

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Heart Disease Risk Prediction Expending of Classification Algorithms

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computers, Materials & Continua 2022年第12期73卷 6595-6616页

作者： Nisha Mary Bilal Khan Abdullah A.Asiri Fazal Muhammad Salman Khan Samar Alqhtani Khlood M.Mehdar Hanan Talal Halwani Muhammad Irfan Khalaf A.Alshamrani City University of Science and Information Technology Peshawar25000Pakistan Radiology Science Department College of Applied Medical Science Najran University Kingdom of Saudi Arabia Department of Electrical Engineering University of Engineering and TechnologyMardan23200Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Electrical Engineering Department College of EngineeringNajran University Saudi ArabiaNajran61441Saudi Arabia

Heart disease prognosis(HDP)is a difficult undertaking that requires knowledge and expertise to predict early *** failure is on the rise as a result of today’s *** healthcare business generates a vast volume of patient records,which are challenging to manage *** it comes to data mining and machine learning,having a huge volume of data is crucial for getting meaningful *** methods for predictingHDhave been used by researchers over the last few decades,but the fundamental concern remains the uncertainty factor in the output data,aswell as the need to decrease the error rate and enhance the accuracy of HDP assessment ***,in order to discover the optimal HDP solution,this study compares multiple classification algorithms utilizing two separate heart disease datasets from the Kaggle repository and the University of California,Irvine(UCI)machine learning *** a comparative analysis,Mean Absolute Error(MAE),Relative Absolute Error(RAE),precision,recall,fmeasure,and accuracy are used to evaluate Linear Regression(LR),Decision Tree(J48),Naive Bayes(NB),Artificial Neural Network(ANN),Simple Cart(SC),Bagging,Decision Stump(DS),AdaBoost,Rep Tree(REPT),and Support Vector Machine(SVM).Overall,the SVM classifier surpasses other classifiers in terms of increasing accuracy and decreasing error rate,with RAE of 33.2631 andMAEof 0.165,the precision of 0.841,recall of 0.835,f-measure of 0.833,and accuracy of 83.49 percent for the dataset gathered from *** SC improves accuracy and reduces the error rate for the Kaggle dataset,which is 3.30%for RAE,0.016 percent for MAE,0.984%for precision,0.984 percent for recall,0.984 percent for f-measure,and 98.44%for accuracy.

关键词： Heart disease heart disease datasets evaluation measures classification techniques

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A Comparative Study on the Efficiency of the Modified COMET in Decision-Making 28th

A Comparative Study on the Efficiency of the Modified COMET ...

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28th International Conference on Knowledge Based and Intelligent information and Engineering systems, KES 2024

作者： Paradowski, Bartosz Olender, Pawel Salabun, Wojciech Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Zolnierska 49 Szczecin71-210 Poland National Institute of Telecommunications ul. Szachowa 1 Warsaw04-894 Poland

Decision-making is a high-risk area and, simultaneously, a process that cannot be ignored. Risks often arise under challenging situations where ranking the options under consideration is a complex problem. For such purposes, multi-criteria decision-making methods are resorted to, which, despite continuous improvement, still have some weaknesses, often due to their characteristics. One such method that has seen many modifications in the literature is the Characteristic Objects METhod (COMET). It allows a simplified comparison of alternatives considered in a given problem, compared to the Analytic Hierarchy Process (AHP), but has a relatively high computational complexity. In this study, we present a modification of the COMET method that simplifies the procedure for assigning preference values to characteristic objects. Furthermore, a comparison with the classical approach is given regarding the obtained preference values, rankings, and criteria weights, which occur implicitly in this method. The study showed that the proposed modification has grounds for broader use and offers similar results to classical COMET while reducing the number of required operations to obtain preference values. © 2024 The Authors.

关键词： Comets

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