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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Ding, Xiao Feng, Botao Rao, Liwei Gu, Wenzhe Deng, Li Zeng, Qingsheng Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Huizhou University School of Computer Science and Engineering Huizhou China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing210016 China

ISBN: (纸本)9798350384437

This paper suggests a base-station antenna unit designed for 2G/3G/LTE frequencies, featuring ±45° polarization, high gain, and low cross-polarization. The antenna is excited by two circular arc probes and uses two orthogonal rectangular ring dipoles to achieve a wider bandwidth. In order to achieve higher gain and better cross-polarization level, four parasitic elements are added around the dual dipole. The simulation results show that |S11|27 dB), higher gain (9.6±1.4 dBi), front-to-back ratio(>18 dB) and a beamwidth of 57.5±7.5° in the desired bandwidth range. The antenna can be used in the broadband base station of the communication system. © 2024 IEEE.

关键词： Bandwidth

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A Polarization Reconfigurable Magneto-Electric Dipole Antenna for 2.2-2.7 GHz Band 7

A Polarization Reconfigurable Magneto-Electric Dipole Antenn...

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Zhou, Xuhang Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Zeng, Qingsheng Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou516007 China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing210016 China

ISBN: (纸本)9798350384437

In this article, a polarization-reconfigurable antenna based on magneto-electric (ME) dipoles is proposed. This antenna has three polarized states: linear polarization (LP), right-hand circular polarization (RHCP), and left-hand circular polarization (LHCP), which are mainly achieved through the cross strips with a bridge structure on top of the ME dipole. By changing the on/off states of diodes D1 and D2, the connections of the cross strips can be altered, thereby changing the surface current on the horizontal patches to achieve polarization reconfiguration. The proposed antenna is fed through a microstrip line and an H-shaped slot. The selected operating frequency band is 2.2-2.7 GHz. In all states, the bandwidth and 3-dB axial ratio bandwidth cover the operating band, and the gain is greater than 7 dBi within the operating band. This indicates that the antenna achieves polarization reconfiguration while maintaining good overall performance. Moreover, the reconfiguration method using diodes is easy to manufacture, making its application in wireless communication systems feasible. © 2024 IEEE.

关键词： Bandwidth

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Creating Realities: An In-Depth Study of AI-Driven Image Generation with Generative Adversarial Networks 15

Creating Realities: An In-Depth Study of AI-Driven Image Gen...

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

作者： Dhand, Raman Sidana, Sagar Sinha, Anurag Kumar, Biresh Kumari, Shreya Madhukar Rao, G. Raj, Petheru Al-Khayyat, Ahmed Maheswaran, S. Guru Tegh Bahadur Institute of Technology Department of computer science and engineering India Maharshi Dayanand University Department of computer science and engineering Haryana Rohtak India IGNOU India School of Computing and information Science New Delhi India Amity University Department of computer science and It Jharkhand India Koneru Lakshmaiah Education Foundation Department of computer science and engineering India Reliance Jio Platforms Ltd Bangalore India Islamic University Department of computer science and engineering Iraq Kongu Engineering College Department of Electronics and Communication Engineering Erode Perundurai638060 India

ISBN: (纸本)9798350370249

The field of artificial intelligence has witnessed significant advancements with the advent of Generative Adversarial Networks, revolutionizing image generation techniques. The literature review covers seminal works and recent advancements, highlighting the evolution of image generation techniques. Taxonomy is developed to classify methods based on their underlying principles and applications across various domains including art, fashion, healthcare, and entertainment. Comparative analysis evaluates the effectiveness, efficiency, and applicability of different models, utilizing metrics corresponding Inception Score and Frechet Inception Distance. By conducting a thorough review of existing literature and technologies, we explore the evolution of GAN architectures, from their inception to the latest innovations. We examine various GAN models, including DCGAN, StyleGAN, and CycleGAN, assessing their effectiveness in generating high-fidelity images across diverse applications. In addition to this comprehensive analysis, we present our own implementation of a GAN model tailored for specific image generation tasks. Our model is tested on multiple datasets to evaluate its performance, highlighting improvements in training stability and image quality. We discuss the architectural choices, optimization techniques, and training procedures employed in our implementation. Furthermore, we address the challenges faced in the field, such as mode collapse, training instability, and the need for largescale datasets. Our findings offer valuable insights into optimizing GAN performance and propose potential directions for future research. This comprehensive analysis serves as a foundational reference for researchers and practitioners aiming to leverage GANs for advanced image generation tasks, pushing the boundaries of what is visually conceivable through artificial intelligence. © 2024 IEEE.

关键词： AI computer vision Deep Learning GANs Image generation ML

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Advanced Machine Learning Techniques for Enhancing Data Security in Cloud Computing Systems 5

Advanced Machine Learning Techniques for Enhancing Data Secu...

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5th IEEE International Conference on Computing, Power, and communication Technologies, IC2PCT 2024

作者： Dhinakaran, M. Sundhari, M. Ambika, S. Balaji, V. Rajasekaran, R.Thalapathi Galgotias College of Engineering & Technology Department of Electronics and Communication Engineering Uttar Pradesh Greater Noida India Grt Institute of Engineering and Technology Dept. of Computer Science and Engineering Tamil Nadu Tiruttani India E. G. S. Pillay Engineering College Department of Information Technology Tamil Nadu Nagapattinam India Chennai India Saveetha School of Engineering Simats Dept. of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350383522

In the age of cloud computing, data stored and processed in the cloud must be secure. This study examines how powerful machine learning can secure cloud computing data. Three trials assessed different machine learning models in this setting. Experiment 1 used a Random Forest model and achieved 95% accuracy, 0.92 precision, 0.96 recall, and 0.94 F1 Score. This shows that the model can accurately categorize security threats with a good balance of true and false positives. A Deep Neural Network (DNN) improved accuracy to 97% in Experiment 2. Precision, recall, and F1 Score values of 0.94, 0.98, and 0.96 demonstrate the DNN's ability to discriminate threats from normal activity. The model captures complicated patterns well, making it a powerful cloud security tool. Security analysis using reinforcement learning, specifically Q-learning, was introduced in Experiment 3. The model's 88% detection rate showed its capacity to identify threats, but its 0.05 false positive rate created a tradeoff between true and false positives. With the 0.12 false negative rate, one can infer improvements in threat detection accuracy. These results indicate that state-of-the-art machine learning is capable of protecting cloud data. The Random Forest and Deep Neural Network models have very high accuracy balanced with reasonable precision-recall trade-offs, whereas reinforcement learning through Qlearning shows promise but needs modification to improve the model's performance in terms of both accuracy and false positive rates. While rising threats need constant adaptation and learning, the model should also fulfill security needs. This study helps formulate a secure cloud computing infrastructure that can stand threats of change. © 2024 IEEE.

关键词： Cloud Computing Data Security Machine learning Q-Learning

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A Broadband Array Antenna Based on E-Shaped Folded Strip Elements for Millimeter-Wave N259/N260 Applications 7

A Broadband Array Antenna Based on E-Shaped Folded Strip Ele...

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Wu, Xi Feng, Botao Ding, Xiao Gu, Wenzhe Deng, Li Zeng, Qingsheng Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Huizhou University School of Computer Science and Engineering Huizhou516007 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing210016 China

ISBN: (纸本)9798350384437

A low-profile, wideband linearly-polarization array antenna based on broadband E-shaped folded strip elements is proposed. The antenna unit consists of a 2×2 array of E-shaped folded strip elements and four metalized through-holes. The microstrip line, located on the underside of an extremely thin dielectric substrate, utilizes a cross-shaped slot for its coupled feeding mechanism. This configuration markedly enhances the compatibility with circuits at millimeter-wave frequencies and also offers the advantage of a low-profile design. The simulated performance of the antenna module shows an impedance bandwidth covering 71%, extending from 21.43 GHz to 45 GHz, along with a peak gain of 5.925 dBi. Moreover, it maintains a cross-polarization discrimination level that is greater than 25 dB. To demonstrate the wideband and high-gain characteristics, a 2 × 2 array of linearly polarized antennas has been crafted, featuring a straightforward microstrip feed system and eliminating the necessity for an extra enclosure made of dielectric material. The simulated data reveal commendable characteristics regarding impedance correlation, gain, and the patterns of radiation, rendering the array well-suited for deployment in millimeter-wave applications designated as N259 and N260. © 2024 IEEE.

关键词： Millimeter waves

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Design of a Forked Eander Slot Antenna Suitable for LTE 5G Frequency Band 7

Design of a Forked Eander Slot Antenna Suitable for LTE 5G F...

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7th IEEE International Conference on Electronic information and communication Technology, ICEICT 2024

作者： Xiao, Gengbo Feng, Botao Ding, Xiao Deng, Li Gu, Wenzhe Zeng, Qingsheng Shenzhen University College of Electronics and Information Engineering Shenzhen518060 China Macau University of Science and Technology Faculty of Humanities and Arts Macau999078 China Beijing University of Posts and Telecommunications College of Information and Communication Engineering Beijing China Huizhou University School of Computer Science and Engineering Huizhou516007 China Nanjing University of Aeronautics and Astronautics College of Astronautics Nanjing210016 China

ISBN: (纸本)9798350384437

In this paper, an improved design of a miniaturized, broadband, meander fork-shaped microstrip antenna (MFSMA) with a radiating patch is proposed. By folding the elongated patch, the bandwidth is widened, while also solving the problem of increased board size and higher manufacturing costs caused by an overly long patch. Adding the asymmetric patch to the ground plane causes the lower edge of the bandwidth to decrease. Utilizing the above characteristics, a miniaturized antenna model was designed. The measurement results of this antenna show acceptable radiation characteristics, with a bandwidth of 1.31 GHz - 3.74 GHz (96.24%) in the condition of VSWR © 2024 IEEE.

关键词： Slot antennas

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Intelligent personalised learning system based on emotions in e-learning

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Personal and Ubiquitous Computing 2023年第6期27卷 2211-2223页

作者： Karthika, R. Jesi, V. Elizabeth Christo, Mary Subaja Deborah, L. Jegatha Sivaraman, Audithan Kumar, Sanjeev Department of Computer Science and Engineering University College of Engineering Tindivanam Tamil Nadu Melpakkam604001 India Department of Networking and Communications School of Computing College of Engineering SRM Institute of Science and Technology Chennai Kattankulathur603203 India Department of Information Technology Netaji Subhas University of Technology New Delhi110078 India Department of Electronics and Communication Engineering Netaji Subhas University of Technology New Delhi110078 India

One of the greatest challenges in the success of a personalised e-learning system lies in the behaviour identification of the learners during the learning and evaluation phase. The content delivery in an e-learning system must be modified and updated periodically according to the preference and behaviour of the learners. Usually, the behaviour of the learners drastically changes according to their affective states during the learning phase. The accurate identification of the learner’s negative emotions and addressing such emotions carefully in a positive sense can greatly provide success to the learners. In this paper, the learner’s emotions especially frustration emotion are automatically and accurately estimated using the information received from learning management systems (LMS) using the Takagi sugeno fuzzy inference engine. Based on this estimation, several motivational messages are distributed according to the identified emotions which had greatly helped them to succeed during e-learning. The motivational messages that are used for sending to the learners are based on regulatory fit theory. Several kinds of statistical tests are used in this paper for deep analysis. Two sets of learners, namely, control and experimental sets, are identified from undergraduate students. Experimental results are shown for these two sets to reveal the increase in their emotional strength after receiving the motivational messages. Statistical analysis t-test is also applied to the two sets, and the experimental results have shown that there is a significant difference between the two groups which shows the dominance of the proposed system. © 2023, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

关键词： E-learning

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Enhanced Student Stress Prediction in Higher Education Using a TriBranch CNN Framework 3

Enhanced Student Stress Prediction in Higher Education Using...

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

作者： Kunwar, Fateh Bahadur Srivastava, Satyajee Dhivya, R. Gulati, Monika Amudha, K. Raj, D. Antony Arul Geetanjali Institute of Technical Studies Department of Computer Science and Engineering Udaipur India School of Engineering and Technology Manav Rachna International Institute of Research and Studies Department of Computer Science and Engineering Faridabad India M.Kumarasamy College of Engineering Department of Information Technology Karur India Mullana Ambala India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Trichy India Psg College of Arts & Science Department of Software Systems Coimbatore India

ISBN: (纸本)9798331508456

Students' stress levels have a significant impact on their academic performance in higher education, thus it's important to identify signs of stress early on and intervene to improve academic performance. Machine learning has evolved into a powerful tool for stress prediction, allowing for proactive support of at-risk youngsters. In order to better anticipate college students' stress levels, this study presents an enhanced TriBranch CNN framework. In order to provide the best possible performance, the technique includes data preparation, feature extraction, and model training. The dimensionality and redundancy are reduced using Principal Component Analysis (PCA). After being trained and tested against other models, the Fully Conditional Temporal Bayesian Network (Fully CTBN) outperformed them all with an accuracy of 93.29%, beating both traditional CNN and FCN approaches. These findings demonstrate that the Fully CTBN approach is useful for identifying students who may be struggling academically and providing schools with the information they need to implement data-driven interventions that boost student well-being and performance. © 2025 IEEE.

关键词： Students

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An Adaptive Deep Learning Framework for Prediction of Agricultural Yield 2

An Adaptive Deep Learning Framework for Prediction of Agricu...

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2nd IEEE International Conference on Integrated Circuits and communication Systems, ICICACS 2024

作者： Rao, K Rama Swapna Kumari, Manthena Eklarker, Ravindra Shaker Reddy, Pundru Chandra Muley, Kirti Burugari, Vijay Kumar Geethanjali College of Engineering and Technology Electronics and Communication Engineering Telangana Hyderabad India Hyderabad India Vardhaman College of Engineering Department of Ai &ds Telangana Hyderabad India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India Bharati Vidyapeeth's Institute of Management and Information Technology Master of Computer Applications Navi Mumbai India Koneru Lakshmaiah Education Foundation Computer Science and Engineering Andhra Pradesh Vaddeswaram India

ISBN: (纸本)9798350317558

Agriculture and its large yields are vital to human survival. Recently, machine learning and deep learning have been applied to determine agricultural output. This study estimated apple production utilizing a unique deep-learning(DL)-based technique. A study-designed convolutional neural network (CNN) classified golden and royal gala apple photos. Classifying CNN-extracted pictures with traditional machine-learning(ML) methods was a hybrid application. Datasets A is used to evaluate the performance of the proposed model. Based on Dataset A accuracy findings, the CNN-SVM hybrid model performed best. CNN-SVM and CNN-Gradient Boosting hybrid models performed best and accurately for Dataset A. The more balanced distribution of train, test, and validation sizes, the suggested hybrid CNN model, and the model's assessment made dataset C the best choice. The hybrid model was 99.70% accurate for Dataset A. Precision, recall, f-measure, and Cohen kappa were 99%. The study found that hybrid models could accurately predict apple fruit yield using golden and royal gala photos. © 2024 IEEE.

关键词： DL Ensemble-Methods ML Smart-farms Yield-Prediction

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BER Improvement of Cooperative Spectrum Sharing of NOMA in 5G Network 3

BER Improvement of Cooperative Spectrum Sharing of NOMA in 5...

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3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Hassan, Mohamed Saeed, Rashid A. Mokhtar, Rania A. Hamid, Khalid Ali, Elmustafa Sayed Odeh, Nidhal Singh, Manwinder Khalifa, Othman O. Islam, Shayla Lovely Professional University Department of Wireless Communication Punjab Phagwara144001 India Taif University Department of Computer Engineering College of Computers and Information Technology Taif21944 Saudi Arabia University of Science & Technology Department of Communication Systems Engineering Omdurman Sudan Department of Electronics Engineering Khartoum Sudan University of Wollongong School of Electrical Computer and Telecommunications Engineering Australia International Islamic University Malaysia Department of Electrical and Computer Engineering Gombak Malaysia Libyan Center for Engineering Resarch and Information Technology Bani Waleed Libya Institute of Computer Science and Digital Innovation Ucsi University Malaysia Department of Computer Science Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350319897

Non-orthogonal multiple access is one of the best methods for addressing the needs of 5G wireless services (NOMA). The proliferation of mobile devices in recent years has increased the importance of cooperative spectrum sharing and utilization in wireless communication due to increases in the bit error rate (BER) brought on by collisions and interference. In this study, MIMO and Massive MIMO (M-MIMO) in the downlink (DL) NOMA power domain (PD) in conjunction with the Cooperative Cognitive Radio Network are proposed as two new ways for improving and evaluating BER in the 5G network (CCRN). Customers of NOMA compete for available channels on the CCRN in this first strategy. The second approach creates a dedicated channel for NOMA users. Three scenarios with varying distances, power location coefficients, and transmission power are used to evaluate the proposed methods in the MATLAB software program. It is assumed that four users will share a 90 MHz BW using QPSK modulation in all three scenarios. When evaluating the performance of the suggested system under the presumption of frequency-selective Rayleigh fading, concurrent interference cancellation (SIC) and unstable channel conditions are also considered. © 2023 IEEE.

关键词： 5G mobile communication systems

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