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2023 IEEE International Workshop on Electromagnetics: Applications and Student Innovation Competition, iWEM 2023

作者： Qi, Junwei Zhang, Chenwei Li, Yingsong Huang, Zhixiang Loh, Tian Hong Chung, Kwok L. Harbin Engineering University College of Information and Communications Engineering Harbin150001 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education China National Physical Laboratory Electromagnetic and Eletrochemical Technologies Department Middlesex United Kingdom Huizhou University School of Computer Science and Engineering 516007 China

ISBN: (纸本)9798350336740

In this paper, Q-value theory of conventional antennas is used to analyze reasons of low radiation performance of acoustics promoted antennas, where we modify the radiating structure of existing acoustics promoting antenna using the simulations to get the required improvement on radiation performance. The simulation experiments are carried out in the finite element simulation software Comsol Multiphysics to analyze the radiating performance. © 2023 IEEE.

关键词： Antennas

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Privacy protection and data security in intelligent recommendation systems

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Neural Computing and Applications 2025年 1-18页

作者： Zheng, Donghua Alkawaz, Mohammed Hazim Johar, Md Gapar Md School of Information Technology and Engineering Guangzhou College of Commerce Guangdong Guangzhou511363 China School of Graduate Studies Management and Science University Selangor Shah Alam40100 Malaysia Department of Computer Science College of Education for Pure Science University of Mosul Nineveh Mosul41002 Iraq Software Engineering and Digital Innovation Centre Management and Science University Selangor Shah Alam40100 Malaysia

Data security and privacy have emerged as major areas of study for recommendation systems. This is crucial for enhancing system security given the quick advancement of Internet technology and its increased use. However, there are still issues with privacy protection and data security in current intelligent recommendation systems, such as a high probability of privacy leakage, low data security, and poor recommendation performance. To better promote the wider application of intelligent recommendation systems, this article introduces federated learning and differential privacy technology to conduct in-depth research on privacy protection and data security in intelligent recommendation systems. In the article, a federated learning framework is used to construct an intelligent recommendation system model, and the constructed model is trained. Furthermore, a differential privacy mechanism is introduced, which can protect the privacy of samples by analyzing the sensitivity of L1 and L2, gradient descent, gradient pruning, and other methods. Next, the secure multi-party computation (SMC) protocol can be used to achieve model parameter sharing and protect the privacy of participants;subsequently, there is the implementation of federated learning. Finally, this article also evaluates the practical application effects of federated learning and differential privacy technology in privacy protection and data security in intelligent recommendation systems. The study’s findings indicate that the approach suggested in this article has an average data security and privacy leakage risk of 5.35% and 96.64%;federated learning is 16.55% and 85.08%, respectively;differential privacy is 9.65% and 88.47%, respectively;deep learning is 21.08% and 77.92%, respectively;and homomorphic encryption is 10.69% and 82.43%, respectively. The combination of federal learning and differential privacy can effectively protect the privacy and data security of the intelligent recommendation system, reduce

关键词： Sensitive data

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computer Supported Argumentation Learning: Design of a Learning Scenario in Academic Writing by Means of a Conjecture Map 15

Computer Supported Argumentation Learning: Design of a Learn...

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15th International Conference on computer Supported education, CSEDU 2023

作者： Burkhard, Michael Seufert, Sabine Gubelmann, Reto Niklaus, Christina Panjaburee, Patcharin Institute for Educational Management and Technologies University of St.Gallen St. Jakob-Strasse 21 St.Gallen9000 Switzerland Institute of Computer Science University of St. Gallen Rosenbergstrasse 30 St. Gallen9000 Switzerland Faculty of Education Khon Kaen University 123 Thanon Mittraphap Mueang Khon Kaen Thailand

ISBN: (纸本)9789897586415

In academic writing, the competency to argue is important. However, first-year students often have difficulties to construct good arguments. Advances in natural language processing (NLP) have made it possible to better analyze the writing quality of texts. New tools have emerged which can give students individual feedback on their texts and the structure of their arguments. While the use of these argumentation learning support tools can help create better texts, using them in an academic context also carries risks. Learning scenarios are needed that promote argumentation competency using argumentation tools while also making students aware of their limitations. To address this issue, this paper investigates how a learning design with an argumentation learning support tool can be developed to increase the argumentation competency of first-year students. The conjecture-mapping technique was used, to visualize our assumptions and illustrate the developed learning design. As part of a first design cycle, the learning design was tested with 80 students in seven academic writing classes at the University of *** in Switzerland. Preliminary findings suggest that the learning design might be helpful to improve the argumentation competency as well as the data-literacy of students (in relation to argumentation tools). However, further research is necessary to confirm or reject our hypotheses. Copyright © 2023 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Students

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Unveiling Android Malware: A Deep Learning Perspective Using RNN and LSTM

Unveiling Android Malware: A Deep Learning Perspective Using...

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Information Technology (ACIT)

作者： Abdullah Alqammaz Mohammad Rasmi Al-Mousa Ali Abu Zaid Mahmoud Asassfeh Mohammad Abdulrazzaq Rajab Sherien Dajah Abdulla M F Alali Mohammed Khouj Cyber Security Department Zarqa University AzZarqa Jordan Computer Science Department University of Anbar Anbar Iraq Management Department The World Islamic Sciences & Education University Amman Jordan Computer Scince Department Zarqa University AzZarqa Jordan Electrical Engineering Department University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

The advent and increasing popularity of Android devices have brought about undesirable mobile malware and its attendant applications that constitutes a danger to society in terms of privacy and information leakage. Because the Android open-source OS is attractive to criminals, it seems impossible for old methods of virus detection to be effective as they used to be. The present study tackles this vital problem by offering an innovative enhancement that uses deep learning algorithms: recurrent neural networks and long short-term memory networks for android malware detection. New solid classification schema was created which focused on the meta-information of applications in order to differentiate entre benign and malicious apps. In our experiments, both RNN and LSTM models yielded improvements in performance metrics which suggests that they can be used to counter the current computer malware challenges. This study adds to the body of knowledge available on the detection of malware and lays the foundation for other studies designed to employ other couplings of deep learning algorithms in further boosting the security of Android applications.

关键词： Deep learning Measurement Privacy Recurrent neural networks Solids Information leakage Malware Information technology Long short term memory Viruses (medical)

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Predictive Modeling for Disease Progression in Chronic Conditions Using Machine Learning

Predictive Modeling for Disease Progression in Chronic Condi...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： T. S. Umamaheswari Amol Dattatray Dhaygude Omprakash Dewangan T. Krishnan Poonam Yerpude Suman Kumar Swarnkar Department of Computer Applications Jayagovind Harigopal Agarwal Agarsen College Chennai India Department of Data Science University of Washington Seattle USA Department of CS & IT Kalinga University Raipur Chhattisgarh India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vijayawada Andhra Pradesh India Department of Computer Science & Engineering Indian Institute Of Technology Bhilai India Department of Computer Science and Engineering Shri Shankaracharya Institute of Professional Management and Technology Raipur India

Predictive modeling of disease progression in chronic conditions is a crucial task in healthcare, as it enables early identification and personalized intervention for patients at risk of adverse outcomes. In recent times, there has been a notable advancement in the field of machine learning techniques, which has demonstrated considerable potential in accurately forecasting the evolution of diseases. This development has proven to be highly beneficial for healthcare professionals, as it equips them with vital insights that can significantly enhance the management of patients. This research paper provides a thorough examination and comparative evaluation of different machine learning algorithms in the context of forecasting the course of diseases in chronic situations. We explore the use of diverse datasets containing longitudinal patient records, clinical variables, and disease-specific markers to train and evaluate predictive models. The study also investigates the impact of feature selection and data preprocessing techniques on the model's performance. Moreover, we assess the generalizability and robustness of the models across different chronic diseases to determine their potential for broader clinical applications. The results demonstrate that machine learning models can effectively predict disease progression, with certain algorithms outperforming others in specific scenarios. The research findings presented in this study offer significant contributions to the understanding and implementation of machine learning techniques for predicting disease development. The aforementioned realizations have the potential to be of substantial assistance to medical professionals in terms of making well-informed decisions, which will ultimately lead to better outcomes for patients.

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Enhancement of Vendor-Managed Inventory Planning through Deep Reinforcement Learning 24

Enhancement of Vendor-Managed Inventory Planning through Dee...

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Proceedings of the Winter Simulation Conference

作者： Marco Ratusny Jee Hyung Kim Hajime Sekiya Maximilian Schiffer Hans Ehm Infineon Technologies AG Neubiberg BAVARIA GERMANY Department of Mathematics and Computer Science University of Hagen Hagen NORTH RHINE-WESTPHALIA GERMANY School of Management Technical University Munich Munich BAVARIA GERMANY

ISBN: (纸本)9798331534202

We explore the application of Twin Delayed Deep Deterministic Policy Gradient (TD3), a Deep Reinforcement Learning (DRL) algorithm, for optimizing Vendor-Managed Inventory (VMI) systems in the semiconductor industry. We introduce a novel multi-scenario DRL algorithm with a continuous action space, designed to effectively manage diverse product/customer combinations, thereby improving VMI performance. We evaluate our algorithm's efficacy on three distinct products as well as 100 product/customer combinations for the multi-scenario approach. A sensitivity analysis examines the effects of varying shipment penalties on the percentage of no violations (PNV) and shipments. Our findings indicate that our DRL-based VMI model significantly surpasses existing policies used in the semiconductor industry by five percentage points.

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Leveraging Neural Networks with BiLSTM and CNN Models for Enhanced HR management Practices 3

Leveraging Neural Networks with BiLSTM and CNN Models for En...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Kalva, Yamini Parasar, Anuradha Pujar, Rajlaxmi Pramod Balasubramanian, K. Pasha, Zameer Ramya, S. Sri Ramachandra Institute of Higher Education and Research Department of Computer Science and Engineering Chennai India School of Liberal Education Galgotias University Dankaur India Indira School of Business Studies Pgdm Department of Marketing Pune India Kalasalingam Academy of Research and Education-Deemed to be University Department of Computer Applications Krishnankoil India Department of Management Bengaluru India M.Kumarasamy College of Engineering Department of Artificial Intelligence and Machine Learning Karur India

ISBN: (纸本)9798331508456

The most valuable asset of any business is its employees. Even with technological advances, organizations rely heavily on human resources for system administration. An increasingly competitive sector makes managing and retaining talented workers difficult. An organization's ability to develop, survive, and compete depends on its employees' skills. These challenges can be overcome using preprocessing, feature extraction, and model training. Data preparation for machine learning is necessary yet time-consuming. Features selection is critical since it enhances accuracy and reduces overfitting. CNN-BiLSTM trained the model. The CNN- BiLSTM model outperformed BiLSTM and CNN. The model's 90.62 percent accuracy showed it could handle HR tasks. For effective human resource management, this study recommends sophisticated machine learning models. Today's businesses need reliable tools for managing and keeping people, and the CNN-BiLSTM model helps. © 2025 IEEE.

关键词： Personnel selection

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Toward Zero-Touch Provisioning in Smart Green Agriculture Driven by Mobile Sensor Stations

Toward Zero-Touch Provisioning in Smart Green Agriculture Dr...

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International Conference on Information Networking

作者： Tri Nguyen Dang Khanh Duy Truong S. M. Ahsan Kazmi Tuong Tri Nguyen School of Engineering and Technology Hue University Hue Vietnam Institute of Open Education and Information Technology Hue University Hue Vietnam Department of Computer Science and Creative Technologies University of the West of England Bristol UK

ISBN: (数字)9798331506940

ISBN: (纸本)9798331506957

Smart Green Agriculture (SGA) is currently under-going a substantial transformation facilitated by the integration of cutting-edge technologies, such as 5G, Artificial Intelligence (AI), and Internet of Things (IoT) devices, including sensors and Unmanned Vehicle Area (UAV). This integration is revolutionizing agricultural practices, making them more sustainable and efficient. For instance, sensors gather crucial data like temperature, humidity, and wind levels, while UAVs capture images and videos of crop fields. These devices can seamlessly communicate via a 5 G model, reducing the need for extensive human intervention in SGA operations. We propose a system model that harmoniously coordinates and integrates various technologies in such a case. This includes leveraging UAV and its mobility to deploy a set of sensors for gathering sensing data. Our study illustrates the potential of orchestrated in integrated systems by cooperating among computing server, UAV, sensors and human. This approach is a promising solution for zero-touch system for the next generation of green agriculture. In such a case, our study demonstrates the significance of smart green agriculture driven by mobile sensors station as a viable solution for orchestration in agriculture. It also sets the stage for further advancements at the intersection of technology and sustainable farming practices, emphasizing the pivotal role of these innovations in shaping the future of agriculture.

关键词： Temperature sensors 5G mobile communication Computational modeling Autonomous aerial vehicles Agriculture Sensor systems Data models Artificial intelligence Intelligent sensors Videos

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Learnware Specification via Label-Aware Neural Embedding 39

Learnware Specification via Label-Aware Neural Embedding

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Chen, Wei Mao, Jun-Xiang Zhang, Min-Ling School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education China Information Technology and Data Management Department of China Mobile Communications Group Zhejiang Co. Ltd China

ISBN: (纸本)157735897X

The learnware paradigm aims to establish a learnware dock system of numerous well-trained machine learning models, enabling users to reuse existing helpful models for their tasks instead of starting from scratch. Each learnware in the system is a well-established model submitted by its developer, associated with a specification generated by the learnware dock system. The specification characterizes the specialty of the corresponding model, enabling it to be identified accurately for new task requirements. Existing specification generation methods are mostly based on the Reduced Kernel Mean Embedding (RKME) technique, which uses the Maximum Mean Discrepancy (MMD) in the Reproducing Kernel Hilbert Space (RKHS) to seek a reduced set that characterizes the model’s capabilities. However, existing RKME-based methods mainly utilize feature information to generate specifications by assuming the existence of the ground-truth labeling function, while leaving the label information, which is capable of providing rich semantic characterization, untouched. Furthermore, the quality of the generated specifications heavily relies on the choice of the kernels, which makes it prohibitive to adapt to all real-world scenarios. In this paper, to overcome the above limitations, we propose a novel specification approach named LANE, i.e., Label-Aware Neural Embedding. In LANE, the neural embedding space is utilized to replace the RKHS, effectively circumventing the step of kernel selection and thereby addressing the dependency on kernels in existing RKME-based specification methods. More importantly, LANE uses the label information as additional supervision to enhance the generation process, resulting in specifications of superior quality. Extensive experiments demonstrate the effectiveness and superiority of the proposed LANE approach in the learnware paradigm. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Hilbert spaces

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Novel Applications of Cloud Computing and Deep Learning Techniques for Data Analysis

Novel Applications of Cloud Computing and Deep Learning Tech...

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International Conference on Computing and Networking Technology (ICCNT)

作者： V.Aravinda Rajan Devesh Tiwari H. Summia Parveen Aditya Verma Rishi Prakash Shukla Vikas V Pawar Department of Computer Science and Engineering (School of Computing) Kalasalingam Academy of Research and Education Krishnankoil Srivilliputhur Tamil Nadu Computer Science and Engineering Graphic Era Hill University Dehradun Uttarakhand India Department of Computer Science Engineering Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Jaipuriya Institute of Management Jaipur India Dr. D Y Patil Vidyapeeth Centre for Online Learning Pune

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In recent years, cloud computing and deep getting to know strategies have emerged as effective gear for information analysis. This combination permits for the efficient processing of large amounts of statistics, permitting researchers to gain precious insights and make informed selections. This technical abstract will talk the unconventional packages of cloud computing and deep learning strategies for statistics evaluation and how they’ve revolutionized the sphere. Cloud computing has made it feasible to save and get admission to big amounts of statistics from any region, eliminating the need for costly and cumbersome hardware. This has opened up new possibilities for data evaluation as researchers can now access and examine information from a couple of resources: social media, medical studies, and monetary statistics. Deep studying techniques, then again, have proven terrific fulfillment in processing complicated and unstructured statistics. Via mimicking the shape and functions of the human brain, deep mastering fashions can identify styles and traits in information that traditional methods may additionally leave out.

关键词： Deep learning Cloud computing Accuracy Social networking (online) Shape Scalability Organizations Prediction algorithms Hardware Information analysis

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