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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Živanović, Nikolina Jeličič, Zoran D. University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798331515799

This study investigates optimal control problems described by fractional differential equations, with the control vector components subject to algebraic constraints. Two case studies are analyzed: an illustrative example designed to test the algorithm's effectiveness and a biologically significant model of acute myeloid leukemia (AML), which applies optimal control theory to design therapeutic strategies under medical constraints. The methodology transforms fractional differential equations into equivalent ordinary differential equations using the framework of Atanacković and Stanković. L.S. Pontryagin's maximum principle is then applied to derive control functions that satisfy the algebraic constraints, followed by numerical optimization of the Hamiltonian function. We formally parsed the problem of dynamic optimization into a static optimization problem using the Forward-Backward Sweep Method (FBSM). The approach is validated through comprehensive numerical simulations, demonstrating its robustness and applicability to real-world problems. © 2025 IEEE.

关键词： Ordinary differential equations

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Image cyberbullying detection and recognition using transfer deep machine learning

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International Journal of Cognitive computing in engineering 2024年第1期5卷 14-26页

作者： Almomani, Ammar Nahar, Khalid Alauthman, Mohammad Al-Betar, Mohammed Azmi Yaseen, Qussai Gupta, Brij B. School of Computing Skyline University College University City of Sharjah P.O. Box 1797 Sharjah United Arab Emirates I.T. department Al-Huson University College AlBalqa Applied University Irbid Jordan Computer Science Department Faculty of I.T. Yarmouk University Irbid Jordan Department of Information Security Faculty of Information Technology University of Petra Amman Jordan Ajman University Ajman United Arab Emirates Department of Computer Science and Information Engineering Asia University Taichung413 Taiwan Symbiosis International University Pune India Department of Electrical and Computer Engineering Lebanese American University Beirut1102 Lebanon Dehradun India

Cyberbullying detection on social media platforms is increasingly important, necessitating robust computational methods. Current approaches, while promising, have not fully leveraged the combined strengths of deep learning and traditional machine learning for enhanced performance. Moreover, online content complexity requires models that can capture nuanced contexts beyond text, which many current methods lack. This research proposes a novel hybrid approach using deep learning models as feature extractors and machine learning classifiers to improve cyberbullying detection. Extracting features using pre-trained deep learning models like InceptionV3, ResNet50, and VGG16, then feeding them into classifiers like Logistic Regression and Support Vector Machines, enhances understanding of the complex contexts where cyberbullying occurs. Experiments on an image dataset showed that combining deep learning and machine learning achieved higher accuracy than using either approach alone. This novel framework bridges the gap in existing literature and contributes to broader efforts to combat cyberbullying through more nuanced, context-aware detection methods. The hybrid technique demonstrates the potential of blending deep learning's representation learning strengths with machine learning's sample efficiency and interpretability. © 2023

关键词： Learning systems

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Hybrid Ensemble-Learning Approach for Renewable Energy Resources Evaluation in Algeria

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computers, Materials & Continua 2022年第6期71卷 5837-5854页

作者： El-Sayed M.El-Kenawy Abdelhameed Ibrahim Nadjem Bailek Kada Bouchouicha Muhammed A.Hassan Basharat Jamil Nadhir Al-Ansari Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansouraEgypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura 35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansouraEgypt Energies and Materials Research Laboratory Faculty of Sciences and TechnologyUniversity of Tamanghasset10034TamanghassetAlgeria URERMS Centre de Developpement des Energies Renouvelables(CDER)01000AdrarAlgeria Mechanical Power Engineering Department Faculty of EngineeringCairo UniversityGiza12613GizaEgypt Department Computer Sciences Universidad Rey Juan CarlosMostoles28933MadridSpain Department of Civil Environmental and Natural Resources EngineeringLulea University of Technology97187LuleaSweden

In order to achieve a highly accurate estimation of solar energy resource potential,a novel hybrid ensemble-learning approach,hybridizing Advanced Squirrel-Search Optimization Algorithm(ASSOA)and support vector regression,is utilized to estimate the hourly tilted solar irradiation for selected arid regions in ***-term measured meteorological data,including mean-air temperature,relative humidity,wind speed,alongside global horizontal irradiation and extra-terrestrial horizontal irradiance,were obtained for the two cities of Tamanrasset-and-Adrar for two *** computational algorithms were considered and analyzed for the suitability of *** two new algorithms,namely Average Ensemble and Ensemble using support vector regression were developed using the hybridization *** accuracy of the developed models was analyzed in terms of five statistical error metrics,as well as theWilcoxon rank-sum and ANOVA *** the previously selected algorithms,K Neighbors Regressor and support vector regression exhibited good ***,the newly proposed ensemble algorithms exhibited even better *** proposed model showed relative root mean square errors lower than 1.448%and correlation coefficients higher than *** was further verified by benchmarking the new ensemble against several popular swarm intelligence *** is concluded that the proposed algorithms are far superior to the commonly adopted ones.

关键词： Renewable energy resources hybrid modeling tilted solar irradiation arid region

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Evaluating Extended Kalman Filter and Unscented Kalman Filter for accurate trajectory estimation 24

Evaluating Extended Kalman Filter and Unscented Kalman Filte...

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24th International Symposium INFOTEH-JAHORINA, INFOTEH 2025

作者： Golić, Anastasija Radović, Mirna University of Novi Sad Faculty of Technical Sciences Department of Computing and Control Engineering Novi Sad Serbia

ISBN: (纸本)9798331515799

This paper evaluates two common methods for trajectory estimation: the Extended Kalman Filter (EKF), the Unscented Kalman Filter (UKF). The EKF and UKF are well-established recursive filtering techniques commonly used for nonlinear state *** performance of these methods is compared through their implementation on a standard trajectory estimation problem, with a focus on assessing their accuracy. © 2025 IEEE.

关键词： Extended Kalman filters

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Building a neural speech recognizer for quranic recitations

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International Journal of Speech Technology 2023年第4期26卷 1131-1151页

作者： Al-Issa, Suhad Al-Ayyoub, Mahmoud Al-Khaleel, Osama Elmitwally, Nouh Department of Computer Engineering Jordan University of Science and Technology Irbid22110 Jordan Department of Computer Science Jordan University of Science and Technology Irbid22110 Jordan School of Computing and Digital Technology Birmingham City University BirminghamB4 7XG United Kingdom Department of Computer Science Faculty of Computers and Artificial Intelligence Cairo University Giza12613 Egypt

This work is an effort towards building Neural Speech Recognizers system for Quranic recitations that can be effectively used by anyone regardless of their gender and age. Despite having a lot of recitations available online, most of them are recorded by professional male adult reciters, which means that an ASR system trained on such datasets would not work for female/child reciters. We address this gap by adopting a benchmark dataset of audio records of Quranic recitations that consists of recitations by both genders from different ages. Using this dataset, we build several speaker-independent NSR systems based on the DeepSpeech model and use word error rate (WER) for evaluating them. The goal is to show how an NSR system trained and tuned on a dataset of a certain gender would perform on a test set from the other gender. Unfortunately, the number of female recitations in our dataset is rather small while the number of male recitations is much larger. In the first set of experiments, we avoid the imbalance issue between the two genders and down-sample the male part to match the female part. For this small subset of our dataset, the results are interesting with 0.968 WER when the system is trained on male recitations and tested on female recitations. The same system gives 0.406 WER when tested on male recitations. On the other hand, training the system on female recitations and testing it on male recitation gives 0.966 WER while testing it on female recitations gives 0.608 WER. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Statistical tests

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Evaluating Security of Big Data Through Fuzzy Based Decision-Making Technique

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computer Systems Science & engineering 2023年第1期44卷 859-872页

作者： Fawaz Alassery Ahmed Alzahrani Asif Irshad Khan Kanika Sharma Masood Ahmad Raees Ahmad Khan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif 21944Saudi Arabia Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Applications Mangalmay Institute of Engineering&TechnologyGreater Noida201310Uttar PradeshIndia Department of Information Technology Babasaheb Bhimrao Ambedkar UniversityLucknow226025Uttar PradeshIndia

In recent years,it has been observed that the disclosure of information increases the risk of *** restricting the accessibility of information,providing security is ***,there is a demand for time tofill the gap between security and accessibility of *** fact,security tools should be usable for improving the security as well as the accessibility of *** security and accessibility are not directly influenced,some of their factors are indirectly influenced by each *** play an important role in bridging the gap between security and *** this paper,we identify the key attributes of accessibility and security that impact directly and indirectly on each other,such as confidentiality,integrity,availability,and *** significance of every attribute on the basis of obtained weight is important for its effect on security during the big data security life cycle *** calculate the proposed work,researchers utilised the Fuzzy Analytic Hierarchy Process(Fuzzy AHP).Thefindings show that the Fuzzy AHP is a very accurate mechanism for determining the best security solution in a real-time healthcare *** study also looks at the rapidly evolving security technologies in healthcare that could help improve healthcare services and the future prospects in this area.

关键词： Information security big data big data security life cycle fuzzy AHP

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AI Based Optimization of Fault Tolerance in Multipath Routing for Medical Application 4

AI Based Optimization of Fault Tolerance in Multipath Routin...

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Prasad, Lakkakula Hari Shobana, G. Bhardwai, Aniul Lakshmi Sireesha, C.H. Bais, Parikshit Kumar Kothapalli, Balakrishna Anurag Engineering College Department of ECE India Loyola Icam College of Engineering and Technology Department of It India Uic - University Institute of Computing Chandigarh University India Cbit Hyderabad Department of Mathematics Hyderabad India LNCT University Bhopal Computer Application Faculty of Engineering India School of Engineering Sr University Department of Eee Warangal India

ISBN: (纸本)9798350300888

Artificial Intelligence (AI) has had a substantial influence on the process of pharmaceutical research and development over the course of the last decade. Several applications that are connected to drug discovery, such as virtual screening and drug design, have made use of a number of AI's varied approaches. One of these applications is drug discovery. In the first section of this study, we are going to present an overview of drug discovery and discuss applications that are relevant to this field. These applications may be divided into two major tasks, which are synthesizing new compounds and making predictions about the characteristics of existing molecules. After that, this study will discuss about the standard data resources, different representations of molecules, and benchmark platforms. As a crucial component of the investigation, the methodologies of artificial intelligence are dissected into a variety of model architectures and learning paradigms. The reviewed papers have been arranged in a chronological manner to demonstrate the development of the technical features of AI in drug discovery throughout the course of time. According to our anticipations, the results of this study should provide a comprehensive analysis of the use of AI in drug development. An additional instructional resource that we make accessible to users is a repository that can be found on GitHub and is comprised of a variety of publications (and codes, if relevant). This archival storage space is regularly upgraded. © 2023 IEEE.

关键词： Expert systems

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Trust-Based Blockchain for Improving Supply Chain Communication in Internet of Vehicles 1

Trust-Based Blockchain for Improving Supply Chain Communicat...

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1st IEEE International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022

作者： Shaheen, H. MohanRaj, A. Shobha Rani, Meesala Sreeraj, S. Amutha, M. Kannadhasan, S. UWL-RAK Faculty of Computing and Engineering United Arab Emirates Sri Eshwar College of Engineering Department of Computer Science and Engineering Coimbatore India MVJ College of Engineering Department of Computer Science and Engineering Bangalore India Sri Krishna College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India Hindusthan College of Engineering Technical Campus Department of Computer Science and Engineering Coimbatore India Study World Collge of Engineering Department of Electronics and Communication Engineering Coimbatore India

ISBN: (纸本)9781665456876

The motto behind the keenness of urban areas has become unmistakable in current cities because of the rise of installed and associated shrewd gadgets, frameworks, and innovations. It is possible to connect every object to the Internet. As a result, in the impending Internet of Things era, the 'Internet of Vehicles-IoV' will play a critical role in newly developed dazzling urban communities. The IoV can possibly address different traffic and street wellbeing issues successfully to forestall deadly crashes. In any case, a specific test of IoV, particularly in 'Vehicle-to-Vehicle' as well as 'Vehicle-to-Infrastructure' communications, guarantees quick, in order to transmit securely and exact performance of the information. The above effort is modifying Blockchain innovation for continuous application called RTA to meet 'Vehicle-to-Every thing-V2x' communications problems in order to overcome these challenges. As a result, the main goal of the study is to develop a Blockchain-based IoT framework for establishing communication security and creating a completely decentralized computing platform. Research methodology used here is divided into two major sections. In the part 1 presented below, authors discuss the traceability and optimization over Merkle trees. The second section deals with implementing an actual blockchain with our optimized Merkle tree as the underlying technology to represent a distributed trust based ledger. © 2022 IEEE.

关键词： Blockchain

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Enhancing IoT Data Security: Integrating Elliptic Galois Cryptography with Matrix XOR Steganography and Adaptive Firefly Optimization 8

Enhancing IoT Data Security: Integrating Elliptic Galois Cry...

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8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2024

作者： Chinnasamy, P. Ayyasamy, Ramesh Kumar Anuradha, K. Alam, Irfan Nair, Deveendra Menon Narayanan Kiran, Ajmeera Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Faculty of Information and Communication Technology Kampar Malaysia Telangana India Scai Vit Bhopal University Cyber Security and Digital Forensics Division Kothari Kalan Sehore India Mlr Institute of Technology Department of Computer Science and Engineering Hdyerabad India

ISBN: (纸本)9798350376425

Securing the uninterrupted data flow is crucial in the fast-advancing Internet of Things (IoT) market. This work investigates the incorporation of elliptic Galois cryptography and matrix XOR coding steganography to improve data security. The suggested solution employs the elliptic Galois cryptography protocol to encrypt sensitive data from diverse medical records. The private information is embedded within images through the matrix XOR encoding steganography algorithm. The Adaptive Firefly optimization approach is used to improve the efficiency of utilizing cover blocks for steganography. The effectiveness of the proposed technique is evaluated using various modern approaches. We analyze the efficiency and security of the proposed approach by comparing it with a previous hybrid method employing the New York State department of Health dataset. The results unequivocally validate that our approach offers enhanced safety in Internet of Things (IoT) applications. © 2024 IEEE.

关键词： Steganography

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Robust Length of Stay Prediction Model for Indoor Patients

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computers, Materials & Continua 2022年第3期70卷 5519-5536页

作者： Ayesha Siddiqa Syed Abbas Zilqurnain Naqvi Muhammad Ahsan Allah Ditta Hani Alquhayz M.A.Khan Muhammad Adnan Khan Department of Mechatronics and Control Engineering University of Engineering and TechnologyLahore54000Pakistan Department of Information Sciences Division of Science and TechnologyUniversity of EducationLahore54000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnam13557Korea

Due to unforeseen climate change,complicated chronic diseases,and mutation of viruses’hospital administration’s top challenge is to know about the Length of stay(LOS)of different diseased patients in the *** management does not exactly know when the existing patient leaves the hospital;this information could be crucial for hospital *** could allow them to take more patients for *** a result,hospitals face many problems managing available resources and new patients in getting entries for their prompt ***,a robust model needs to be designed to help hospital administration predict patients’LOS to resolve these *** this purpose,a very large-sized data(more than 2.3 million patients’data)related to New-York Hospitals patients and containing information about a wide range of diseases including Bone-Marrow,Tuberculosis,Intestinal Transplant,Mental illness,Leukaemia,Spinal cord injury,Trauma,Rehabilitation,Kidney and Alcoholic Patients,HIV Patients,Malignant Breast disorder,Asthma,Respiratory distress syndrome,*** been analyzed to predict the *** selected six Machine learning(ML)models named:Multiple linear regression(MLR),Lasso regression(LR),Ridge regression(RR),Decision tree regression(DTR),Extreme gradient boosting regression(XGBR),and Random Forest regression(RFR).The selected models’predictive performance was checked using R square andMean square error(MSE)as the performance evaluation *** results revealed the superior predictive performance of the RFRmodel,both in terms of RS score(92%)and MSE score(5),among all selected *** Exploratory data analysis(EDA),we conclude that maximumstay was between 0 to 5 days with the meantime of each patient 5.3 days and more than 50 years old patients spent more days in the *** on the average LOS,results revealed that the patients with diagnoses related to birth complications spent more days in the hospital than other *** finding coul

关键词： Length of stay machine learning robust model random forest regression

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