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Archery Algorithm:A Novel Stochastic Optimization Algorithm for Solving Optimization Problems

computers, Materials & Continua 2022年第7期72卷 399-416页

作者： Fatemeh Ahmadi Zeidabadi Mohammad Dehghani Pavel Trojovsky Štěpán Hubálovsky Victor Leiva Gaurav Dhiman Department of Mathematics and Computer Sciences Sirjan University of TechnologySirjanIran Department of Mathematics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic School of Industrial Engineering Pontificia Universidad Católica de ValparaísoValparaíso2362807Chile Department of Computer Science Government Bikram College of CommercePatialaPunjabIndia

Finding a suitable solution to an optimization problem designed in science is a major ***,these must be addressed utilizing proper *** on a random search space,optimization algorithms can find acceptable solutions to *** Algorithm(AA)is a new stochastic approach for addressing optimization problems that is discussed in this *** fundamental idea of developing the suggested AA is to imitate the archer’s shooting behavior toward the target *** proposed algorithm updates the location of each member of the population in each dimension of the search space by a member randomly marked by the *** AA is mathematically described,and its capacity to solve optimization problems is evaluated on twenty-three distinct types of objective ***,the proposed algorithm’s performance is compared *** approaches,including teaching-learning based optimization,marine predators algorithm,genetic algorithm,grey wolf optimization,particle swarm optimization,whale optimization algorithm,gravitational search algorithm,and tunicate swarm *** to the simulation findings,the AA has a good capacity to tackle optimization issues in both unimodal and multimodal scenarios,and it can give adequate quasi-optimal solutions to these *** analysis and comparison of competing algorithms’performance with the proposed algorithm demonstrates the superiority and competitiveness of the AA.

关键词： Archer meta-heuristic algorithm population-based optimization stochastic programming swarm intelligence population-based algorithm Wilcoxon statistical test

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Business Decision making using Data science

Business Decision making using Data Science

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2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems, ICSES 2022

作者： Singh Yadav, Nagendra Goar, Vishal Singh Yadav, Pallavi Chowdhury, Subrata Ninh Bui, Trung Thi Thu, Nguyen Vijayakumar, K. Government Engineering College Rajasthan Bikaner India Maharaja Ganga Singh University Faculty of Commerce Rajasthan Bikaner India Computer Science Department Chittoor India VNU University of Engineering and Technology Hanoi Viet Nam Hanoi University of Industry Department Faculty of Electronic Engineering Hanoi Viet Nam St. Joseph's Institute of Technology OMR Department of Computer Science and Engineering Chennai India

ISBN: (数字)9781665474139

ISBN: (纸本)9781665474139

Several organizations started to adopt the strategy to have data scientist professionals to help them identify the true value of the cost vs benefits and since data science is on fire for a quite long time, adopting the same comes in handy for several factors. i.e., investment, net profit, etc. At the same time, the clarity is missing around what data science is and this tends to introduce the concept of unambiguous theory or assumptions. In this research, we are aiming to pin down the fact of what data science is and how it can lead to crucial decision-making using a data-driven approach. We firmly believe that the accurate limitations of data science scope cannot be defined due to its vast possibilities. If the business wants to align themselves using the data science by understanding the true potential, the following needs to be underlined: 1) understand the importance of related concepts using the relationship approach, 2) identify how fundamental principles of data science can be useful for the different business use case, 3) Identify what the data science can offer depending on our requirement. In this research, we offer a decision-making solution for businesses using the underlying data science principles. © 2022 IEEE.

关键词： Software testing

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Optimizing K-Nearest Neighbor Values Using The Elbow Method

Optimizing K-Nearest Neighbor Values Using The Elbow Method

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International Conference on Informatics and Computing (ICIC)

作者： Bobby Irawan Fahmi Fahmi Elviawaty Muisa Zamzami Faculty of Computer Science and Information Technology Universitas Sumatera Utara Medan Indonesia Department of Electrical Engineering Faculty of Engineering Universitas Sumatera Utara Medan Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Optimizing the K value in the K-Nearest Neighbor (KNN) algorithm is a critical step in enhancing model performance, particularly for tasks related to classification and prediction. The Elbow method has emerged as a widely used technique for determining the optimal K value by analyzing the point where adding more neighbors does not significantly reduce error or improve model performance. This study focuses on the application of the Elbow method for optimizing the K value in the context of classifying BRIMo application reviews on the Google Play platform. By conducting a comparative analysis between the K value determined by the Elbow method and the traditionally chosen K value in KNN, this research demonstrates the advantages of using the Elbow method for improving classification accuracy. The experimental results reveal that the K value derived from the Elbow method (0.8) outperforms the traditional K value (0.78), resulting in a notable improvement in model performance. This superior accuracy highlights the effectiveness of the Elbow method in selecting a more optimal K value, leading to a better fit for the data and more precise predictions. Consequently, the use of the Elbow method for optimizing the K value in the KNN algorithm proves to be a highly effective strategy for improving classification models, especially when applied to real-world data such as application reviews on digital platforms like Google Play.

关键词： Accuracy Reviews Nearest neighbor methods Predictive models Prediction algorithms Data models Internet Classification algorithms Elbow Informatics

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Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

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computers, Materials & Continua 2023年第3期74卷 5467-5482页

作者： Mohammed Maray Badriyya B.Al-onazi Jaber S.Alzahrani Saeed Masoud Alshahrani Najm Alotaibi Sana Alazwari Mahmoud Othman Manar Ahmed Hamza Department of Information Systems College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Language Preparation Arabic Language Teaching InstitutePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Computer Science College of Computing and Information TechnologyShaqra UniversityShaqraSaudi Arabia Prince Saud AlFaisal Institute for Diplomatic Studies RiyadhSaudi Arabia Department of Information Technology College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The recognition of the Arabic characters is a crucial task incomputer vision and Natural Language Processing fields. Some major complicationsin recognizing handwritten texts include distortion and patternvariabilities. So, the feature extraction process is a significant task in NLPmodels. If the features are automatically selected, it might result in theunavailability of adequate data for accurately forecasting the character ***, many features usually create difficulties due to high dimensionality *** this background, the current study develops a Sailfish Optimizer withDeep Transfer Learning-Enabled Arabic Handwriting Character Recognition(SFODTL-AHCR) model. The projected SFODTL-AHCR model primarilyfocuses on identifying the handwritten Arabic characters in the inputimage. The proposed SFODTL-AHCR model pre-processes the input imageby following the Histogram Equalization approach to attain this *** Inception with ResNet-v2 model examines the pre-processed image toproduce the feature vectors. The Deep Wavelet Neural Network (DWNN)model is utilized to recognize the handwritten Arabic characters. At last,the SFO algorithm is utilized for fine-tuning the parameters involved in theDWNNmodel to attain better performance. The performance of the proposedSFODTL-AHCR model was validated using a series of images. Extensivecomparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of theproposed SFODTL-AHCR model over other approaches.

关键词： Arabic language handwritten character recognition deep learning feature extraction hyperparameter tuning

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MIMO Virtual Array Design for mmWave 4D-Imaging Radar Sensors 31

MIMO Virtual Array Design for mmWave 4D-Imaging Radar Sensor...

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31st European Signal Processing Conference, EUSIPCO 2023

作者： Sichani, Nazila Karimian Alaee-Kerahroodi, Mohammad Raei, Ehsan Bhavani Shankar, M.R. Mehrshahi, Esfandiar Ghorashi, Seyyed Ali Department of Telecommunications Faculty of Electrical Engineering Shahid Beheshti University Tehran*** Iran University of Luxembourg Luxembourg Department of Computer Science and Digital Technologies School of Architecture Computing and Engineering University of East London LondonE16 2RD United Kingdom

ISBN: (纸本)9789464593600

The recently developed 4D-Imaging mmWave MIMO radars have significant advantages over conventional radar sensors because they offer a large virtual array in both azimuth and elevation. However, the physical placement of transmit and receive antennas to achieve the desired virtual array, while considering cost and efficiency is not intuitive. In this paper, we propose an optimization framework for optimally placing the transmit and receive array physical elements. The proposed framework obtains a sequence of optimal placements based on the coordinate descent by defining a desired virtual array and number of transmit and receive antenna elements as the objective. The objective function is then monotonically minimized in each iteration of the proposed algorithm, converging to a solution that guarantees the desired number of transmit and receive antenna elements, while the obtained virtual array is as close to the desired virtual array as possible. The simulation results are validated by simulating the virtual array of multiple commercially available 4D-Imaging radar products. © 2023 European Signal Processing Conference, EUSIPCO. All rights reserved.

关键词： Radar imaging

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Deep Reinforcement Learning for Autonomous Drone Navigation in Cluttered Environments

Deep Reinforcement Learning for Autonomous Drone Navigation ...

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Recent Trends in computer science and Technology (ICRTCST), International Conference on

作者： A Chandrashekhar Amit Rawate A. Dhanamathi Rakeshnag Dasari Rajeshri Pravin Shinkar Pavithra G Department of Mechanical Engineering Faculty of Science & Technology Icfai Foundation for Higher Education Hyderabad Telangana Department of Electronics and Computer Engineering Maharashtra Institute of Technology Aurangabad Maharashtra Department Of Computer Science and Engineering Roever Engineering College Perambalur Tamilnadu Department of CSE Acharya Nagarjuna University Nagarjuna Nagar Guntur Department of Computer Science SIES College of Arts Science and Commerce Nerul Navi Mumbai Department of Electronics & Communication Engineering Dayananda Sagar College of Engineering Bangalore Karnataka

ISBN: (数字)9798350351378

ISBN: (纸本)9798350351385

The application of Deep Reinforcement Learning (DRL) to enhance the autonomous navigation capabilities of drones in environments that are complex and congested. The complications involved with guiding drones across restricted places, overcoming obstructions, and navigating among clutter are addressed in this article. Through the utilization of DRL techniques, the framework that has been proposed gives drones the ability to learn and alter their navigation strategies on their own through the process of trial and error, hence optimization of real-time decision-making. The incorporation of deep neural networks for the processing of sensory data makes it possible for drones to comprehend their environment, which in turn makes it easier for them to make educated judgments with regard to safe navigation. In order to demonstrate the potential of DRL in boosting the autonomy and robustness of drone navigation systems in demanding environments, the effectiveness of the technique is evaluated using simulations and experiments conducted in the real world. The findings make a contribution to the development of autonomous drone technology, which has implications for a variety of applications including environmental monitoring, search and rescue operations, and surveillance.

关键词： Navigation Surveillance Decision making Deep reinforcement learning Market research Robustness Real-time systems

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Combination of Fuzzy-Weighted Zero-Inconsistency and Fuzzy Decision by Opinion Score Methods in Pythagorean m -Polar Fuzzy Environment: A Case Study of Sign Language Recognition Systems

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International Journal of Information Technology and Decision Making 2023年第4期22卷 1341-1369页

作者： Albahri, O.S. Alsattar, H.A. Garfan, Salem Qahtan, Sarah Zaidan, A.A. Ahmaro, Ibraheem Y. Y. Alamoodi, A.H. Zaidan, B.B. Albahri, A.S. Al-Samarraay, Mohammed S. Jasim, Ali Najm Baqer, M.J. Department of Computing Faculty of Arts Computing and Creative Industry Universiti Pendidikan Sultan Idris Tanjung Malim 35900 Malaysia Department of Computer Center Middle Technical University's Baghdad Iraq Faculty of Engineering & It British University in Dubia Dubai United Arab Emirates Computer Science Department College of Information Technology Hebron University Hebron Palestine Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan Baghdad Iraq Department of Business Administration College of Administrative Science The University of Mashreq Baghdad10021 Iraq

In the fuzzy multicriteria decision-making approach, a committee of decision-makers is usually involved in the assessment of the suitability of different alternatives based on the evaluation criteria by using linguistic terms and their equivalent fuzzy numbers. In this context, researchers have developed the Pythagorean fuzzy set (PFS) to overcome the limitation of intuitionistic fuzzy set in the description of decision-maker information such as imposing restrictions on the representation of membership and nonmembership grades. On the one hand, PFS still does not have sufficient ability and flexibility to deal with such issues. On the other hand, multipolar technology is used to operate large-scale systems in real-life situations, especially in dealing with dissatisfaction and indeterminacy grades for the alternatives of the reference set. Thus, m-polar fuzzy set is utilized and applied with other fuzzy sets because of its remarkable ability as a tool for depicting fuzziness and uncertainty under multipolar information in many circumstances. With the practical features of m-polar fuzzy set in combination with PFS, this paper employs it to extend two considerable MCDM methods, namely, fuzzy decision by opinion score method and fuzzy-weighted zero inconsistency. Such extensions, called Pythagorean m-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) method and Pythagorean m-polar fuzzy decision by opinion score method (Pm-PFDOSM), are formulated to weight the evaluation criteria followed by alternative ranking progressively. The research methodology is presented as follows. Firstly, the mechanisms of Pm-PFWZIC and Pm-PFDOSM are formulated and integrated into the development phase. Secondly, the description of the real-world case study of the evaluation and benchmarking of the sign language recognition systems is adapted and presented. The result of Pm-PFWZIC shows that the criterion of 'finger movements' has the highest weight amongst the rest of the criteria, wherea

关键词： Decision making

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A Variation-Aware and Energy-Efficient Spintronic True Random Number Generator

A Variation-Aware and Energy-Efficient Spintronic True Rando...

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IEEE computer Society Annual Symposium on VLSI

作者： Saeed Mehri Arefe Amirany Milad Tanavardi Nasab Kian Jafari Mohammad Hossein Moaiyeri Faculty of Electrical Engineering Shahid Beheshti University Tehran Iran Faculty of Electrical and Computer Engineering University of Kashan Kashan Iran Department of Electrical Engineering and Computer Science University of Tennessee Knoxville TN USA Faculty of Engineering Université de Sherbrooke Sherbrooke QC Canada

ISBN: (数字)9798350354119

ISBN: (纸本)9798350354126

This paper proposes a spintronic true random number generator (TRNG) optimized for low power, reliability, and sensitivity to variations. It uses the random behavior of magnetic tunnel junctions in a low critical current switching regime for generating numbers. An internal post-processing unit enhances randomness without major hardware costs. It's proven robust against process, voltage, and temperature changes via Monte-Carlo simulations, meeting the National Institute of Standards and Technology randomness criteria even with these variations. The proposed TRNG also shows a 38% energy efficiency improvement over its state-of-the-art counterparts.

关键词： Power demand Switches Voltage NIST Very large scale integration Generators Energy efficiency

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Exploring Welded Joint Integrity Using Microstructural Characterization Using Deep Encoders 15

Exploring Welded Joint Integrity Using Microstructural Chara...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Muthumari, A. Sushama, C. Grandhi, Suresh Kumar Kaushal, Ashish Nurtanto, Muhammad Sundaram, Mohana K. Department of Computer Science & Engineering Anna University Regional Campus Tamil Nadu Madurai India Department of CSE School of Computing Mohan Babu University Erstwhile Sree Vidyanikethan Engineering College AP Tirupathi India Faculty of Science & Technology ICFAI Foundation for Higher Education Telangana Hyderabad India Jindal Global Business School O P Jindal Global University Haryana Sonipat India Department of Mechanical Engineering Polytechnic State Jakarta Demak Indonesia Department of CSE R.M.D. Engineering College Tamil Nadu India

ISBN: (纸本)9798350370249

The integrity of welded joints is a crucial influence in the field of structural engineering, as it has significant implications for the dependability and safety of diverse sectors, including aerospace, automotive, and construction. In order to ensure the optimal performance of welded joints, it is important to evaluate their microstructural features. This paper explains a novel methodology for investigating the integrity of welded joints by employing microstructural characterization with the use of a deep encoder. The deep encoder is a neural network design that has exhibited exceptional skills in applications related to image processing and feature extraction. In the course of our study, we modified this particular framework to examine microstructural pictures of welded joints, facilitating the acquisition of significant information pertaining to their structural soundness. The methodology employed in this study entails the acquisition of high-resolution images of welded connections, often obtained by the utilization of scanning electron microscopy or comparable imaging techniques. The findings of our investigation exhibit promise, as they offer a more precise and effective approach for evaluating the integrity of welded joints in contrast to conventional manual techniques. ©2024 IEEE.

关键词： Welding

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Characterization of Traffic in the Internet of Vehicles Using Microscopic Modelling

Characterization of Traffic in the Internet of Vehicles Usin...

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Advanced Telecommunication and Networking Technologies (ATNT), IEEE International Conference on

作者： Kobby Asare Obeng Sharifah H.S. Ariffin Nor Aishah Muhammad Muhammad Ariff Baharudin Faculty of Electrical Engineering Universiti Teknologi Malaysia Department of Computer Science Koforidua Technical University Faculty of Electrical Engineering Universiti Teknologi Malaysia Johor Malaysia

ISBN: (数字)9798350353501

ISBN: (纸本)9798350353518

The emergence of the Internet of Vehicles (IoV) as a driver for the new age of Intelligent Transportation Systems (ITS) provides an opportunity for a wide range of services and applications driven by the various interactions that exist between a vehicle and its environment. However, the nature of road networks affects real-time vehicular movements with respect to the departure, delay, speed and arrival of vehicles. In this paper, we present a data-driven, traffic theory and microscopic traffic simulation approach that can be used in modelling the underlying vehicular network of the IoV. Using the traffic status from the A4 highway dataset in Luxembourg, we obtain vehicular volumes from the vehicle flow rate, define thresholds for traffic status using data scaling and employ microscopic modeling to create traffic scenarios. The analysis from the results of the traffic scenarios shows unique vehicular patterns due to the nature of the road network providing insights that ultimately affect the design and implementation of IoV-based services and applications.

关键词： Microscopy Roads Traffic control Road traffic Real-time systems Data models Internet Next generation networking Intelligent transportation systems Internet of Vehicles

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