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2nd International Conference on Smart computing and Electronic Enterprise, ICSCEE 2021

作者： Baker El-Ebiary, Yousef A. Aseh, Khairi Bamansoor, Samer Pande, Bishwajeet Abu-Ulbeh, Waheeb Yusoff, M. Hafiz Pathmanathan, P. Ravindran Jusoh, Julaily Aida Al Moaiad, Yazeed UniSZA Faculty of Informatics and Computing Malaysia International Institute of Applied Science Swiss School of Management Netherlands MEDIU Faculty of Computer and Information Technology Malaysia Gyancity Research Consultancy Pvt Ltd Motihari India Faculty of Computer Science and Informatics Cybersecurity Department Jordan Albukhary International University School of Computing and Informatic Kedah Malaysia Faculty of Informatics and Computing UniSZA Malaysia

ISBN: (纸本)9781665432221

The purpose of this study is to discuss on opportunities and threats of Foodpanda as an online E-commerce platform including their strength and weaknesses. Electronic Commerce commonly known as E - Commerce means buying and selling of goods and services through internet. E-commerce is a way of conducting business online. Though e-commerce is facing challenges in the market but it has scope to outsource the traditional markets. Nowadays, E-commerce is a very big and potential platform in business organization. There are so many platform of E-commerce and one of them is Foodpanda. Foodpanda is one of the leading online food delivery marketplaces and is spread out globally. Its main job is to enable users to place orders at nearby restaurants with the assistance of its website or via its mobile app. A study on Foodpanda will show on opportunities and threads of Foodpanda as one of the biggest online E-Commerce platform. © 2021 IEEE.

关键词： Mobile commerce

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Utilization of Blockchain technology in Human Resource Management 2

Utilization of Blockchain Technology in Human Resource Manag...

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2nd IEEE International Conference on AI in Cybersecurity, ICAIC 2023

作者： Anaam, Elham Ghazal, Taher M. Haw, Su-Cheng Alzoubi, Haitham M. Alshurideh, Muhammad Turki Mamun, Abdullah Al Faculty of Information Science and Technology Bangi Selangor43600 Malaysia School of IT Skyline University College Sharjah United Arab Emirates Multimedia University Jalan Multimedia Faculty of Computing and Informatics Cyberjaya63100 Malaysia School of Business Skyline University College Sharjah United Arab Emirates School of Business The University of Jordan Department of Marketing Amman11942 Jordan Graduate School of Management Universiti Kebangsaan Malaysia Bangi43600 Malaysia

ISBN: (纸本)9798350396966

With the advancement of time and the advancement scientific knowledge, the blockchain technology has expanded in various applications. The security and data privacy of employee recruitment system has been compromised. Therefore, hiring managers can enhance job satisfaction by adopting blockchain technology to secure human resource data management. This paper provides current research status, issues and a deep investigation on the blockchain technology's potential use in the management of human resources. © 2023 IEEE.

关键词： Human resource management

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Identifying Gamified Teaching Elements in Computer Science Course 9

Identifying Gamified Teaching Elements in Computer Science C...

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9th International Conference on Platform technology and Service, PlatCon 2023

作者： Tanalol, Hasnah Hashim, Hazmi Turumogan, Punitha Noor, Noorsidi Aizuddin Mat Baharum, Aslina Deris, Farhana Diana Universiti Malaysia Sabah Preparatory Center for Science & Technology Kota Kinabalu Malaysia University Malaysia Sabah Faculty of Computing and Informatics Kota Kinabalu Malaysia Tunku Abdul Rahman University of Management and Technology Department of Computing and Information Technology Penang Malaysia Universiti Teknologi Malaysia Mass Appraisal Housing and Planning Research Group Centre for Real Estate Studies Faculty of Built Environment and Surveying Real Estate Department Johor Johor Bahru81310 Malaysia School of Engineering and Technology Sunway University Computing and Information System Bandar Sunway Selangor Malaysia Universiti Teknologi Malaysia Universiti Malaysia Sabah Faculty of Social Sciences and Humanities Johor Johor Bahru81310 Malaysia

ISBN: (纸本)9798350305999

Traditional education methods, having said, lack motivation and self-regulation. The 5G and 6G technology have improved education;one such is gamification. Gamification applies gaming mechanisms to non-gaming platforms. Sadly, the use of gamification is still limited, especially in Malaysia. Gamification affects user experience, so it is intriguing to see if it can solve education problems. A study was conducted to identify elements of gamification. A systematic literature review (SLR) determines the gamification elements. Results show the list of identified 30 elements as guidelines for developing gamified teaching in Computer Science Courses. Thus, it is hoped that the improved teaching and learning process can affect students' performance. © 2023 IEEE.

关键词： Curricula

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XorshiftH128+: A hybrid random number generator for lightweight IoT

XorshiftH128+: A hybrid random number generator for lightwei...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Sandakly, Serge Salem, Charbel Challita, Khalil Dass, Shuvalaxmi Azar, Joseph Abdo, Jacques Bou Demerjian, Jacques LaRRIS Faculty of Sciences Lebanese University Fanar Lebanon Department of Computer Science University of Warwick Coventry United Kingdom School of Computing and Informatics University of Louisiana at Lafayette LA United States FEMTO-ST Institute UMR 6174 CNRS Universite de Franche-Comte France School of Information Technology University of Cincinnati OH United States Jounieh Lebanon

ISBN: (纸本)9798350322347

The main purpose of this paper is to develop a Hybrid Random Number Generator for resource-constrained slim nodes operating within a lightweight Internet of Things network. Our algorithm extends the lightweight Xorshift128+ random number generator by developing a randomized seed generation algorithm that leverages the network's random features. Simulation of the network was conducted using Omnet++ software followed by the validation process of cryptographic security of our proposal using NIST SP 800-22, Diehard, and TestU01 statistical tests. © 2023 IEEE.

关键词： Internet of things

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RAT-Cycle-GAN for Text to Image

RAT-Cycle-GAN for Text to Image

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作者： Chenlong Yin Weiheng Su Zheyong Lin Yuyang Xu School of Computer Science and Technology University of Science and Technology of China School of Informatics Computingand Cyber SystemsNorthern Arizona University School of Geodesy and Geomatics Wuhan University Division of General Studies University of Illinois at Urbana Champaign

We present a method that enhances the RATGAN using the idea of CycleGAN to prevent the model from *** generator with the proposed recurrent affine transformation for text-to-image synthesis and the fusion blocks are connected by an RNN during the generation of fake images to assure the global assignment of text *** transformation from the phony images to the text is then accomplished using a CNN and encoder with LSTM.A comparison of the generated text with the original text ensures the generator's *** order to achieve the state of the art,we evaluated both unconditional and conditional generation's effectiveness in relation to the accepted standards.

关键词： RAT-Gan Cycle Gan Text-to-Image

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of Advanced Computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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Classification of SEMG Signal Based Hand Movements by Using Support Vector Machine

Classification of SEMG Signal Based Hand Movements by Using ...

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IEEE Symposium on Computers & informatics (ISCI)

作者： Syeda Rabail Zahra Sadia Ismail Muhammad Danish Ali Muhammad Amir Khan Mohamad Yusof Darus Tahseen Mazhar Institute of Computing & Information Technology GOMAL University Dera Ismail Khan Pakistan Computer Science COMSATS University Islamabad Abbottabad Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Shah Alam Selangor Malaysia Department of Computer Science Virtual University of Pakistan

ISBN: (数字)9798350353853

ISBN: (纸本)9798350353860

This paper explores the application of Surface Electromyography (SEMG) for hand movement classification using machine learning techniques. SEMG signals play a crucial role in sports medicine, rehabilitation, prosthesis control, and medical diagnosis. The study addresses a multi-class problem, extending the dataset with limited prior exploration. Nine features from SEMG signal's time domain are extracted, including Maximum Amplitude, Root Mean Square, Slope Sign Change, Variance, Simple Square Integral, Zero Crossing, Waveform Length, and Willison Amplitude. Linear Discriminant Analysis is employed for dimensionality reduction as a feature selection technique. The proposed algorithm achieves an overall classification accuracy of 72% for the classification of six different hand movements performed by five healthy subjects using Support Vector Machines. This research contributes to the advancement of SEMG-based hand movement classification in various practical applications.

关键词： Support vector machines Feature extraction Vectors Classification algorithms Medical diagnosis Time-domain analysis Root mean square

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Obfuscated Malware Detection: Impacts on Detection Methods 15th

Obfuscated Malware Detection: Impacts on Detection Methods

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15th International scientific conferences on research and applications in the field of intelligent information and database systems, ACIIDS 2023

作者： Gorment, Nor Zakiah Selamat, Ali Krejcar, Ondrej Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia School of Computing Faculty of Engineering Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia Universiti Teknologi Malaysia Johor Bahru Johor81310 Malaysia College of Computing and Informatics Universiti Tenaga Nasional Jalan IKRAM-UNITEN Selangor Kajang43000 Malaysia Center for Basic and Applied Research Faculty of Informatics and Management University of Hradec Kralove Hradec Kralove50003 Czech Republic

ISBN: (纸本)9783031424298

Obfuscated malware poses a challenge to traditional malware detection methods as it uses various techniques to disguise its behavior and evade detection. This paper focuses on the impacts of obfuscated malware detection techniques using a variety of detection methods. Furthermore, this paper discusses the current state of obfuscated malware, the methods used to detect it, and the limitations of those methods. The impact of obfuscation on the effectiveness of detection methods is also discussed. An approach for the creation of advanced detection techniques based on machine learning algorithms is offered, along with an empirical examination of malware detection performance assessment to battle obfuscated malware. Overall, this paper highlights the importance of staying ahead of the constantly evolving threat landscape to safeguard computer networks and systems. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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A Steganographic Method for Color Images using the Salp Swarm Algorithm and Chaotic Maps

A Steganographic Method for Color Images using the Salp Swar...

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Conference on Local Computer Networks (LCN)

作者： Aya Jaradat Moad Mowafi Eyad Taqieddin Network Engineering and Security Dept Jordan University of Science and Technology Irbid Jordan School of Computing and Informatics Al Hussein Technical University Amman Jordan

The availability of high resolution images allows for using steganography to hide the presence of secret information. An issue of importance is to guarantee that the hidden data neither affects the quality of the images nor is it visually perceived. Another factor to consider is the capacity of embedding in the cover images without detection. In this paper we employ the Discrete Cosine Transform compression with quantization for lossy compression. We use the Salp Swarm Algorithm in conjunction with chaotic maps to find optimal locations in the cover images for data embedding in the LSBs. When chaotic maps are inserted into the SSA algorithm environment, the proposed method has achieved satisfactory results with minimal impact on the PSNR.

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RiskQ: risk-sensitive multi-agent reinforcement learning value factorization 23

RiskQ: risk-sensitive multi-agent reinforcement learning val...

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Siqi Shen Chennan Ma Chao Li Weiquan Liu Yongquan Fu Songzhu Mei Xinwang Liu Cheng Wang Fujian Key Laboratory of Sensing and Computing for Smart Cities School of Informatics Xiamen University (XMU) China and Key Laboratory of Multimedia Trusted Perception and Efficient Computing XMU China School of Computer National University of Defense Technology China

Multi-agent systems are characterized by environmental uncertainty, varying policies of agents, and partial observability, which result in significant risks. In the context of Multi-Agent Reinforcement Learning (MARL), learning coordinated and decentralized policies that are sensitive to risk is challenging. To formulate the coordination requirements in risk-sensitive MARL, we introduce the Risk-sensitive Individual-Global-Max (RIGM) principle as a generalization of the Individual-Global-Max (IGM) and Distributional IGM (DIGM) principles. This principle requires that the collection of risk-sensitive action selections of each agent should be equivalent to the risk-sensitive action selection of the central policy. Current MARL value factorization methods do not satisfy the RIGM principle for common risk metrics such as the Value at Risk (VaR) metric or distorted risk measurements. Therefore, we propose RiskQ to address this limitation, which models the joint return distribution by modeling quantiles of it as weighted quantile mixtures of per-agent return distribution utilities. RiskQ satisfies the RIGM principle for the VaR and distorted risk metrics. We show that RiskQ can obtain promising performance through extensive experiments. The source code of RiskQ is available in https://***/xmu-rl-3dv/RiskQ.

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