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A textile right angle slot antenna with dualband circularly polarization 18

A textile right angle slot antenna with dualband circularly ...

18th International Conference on Electrical engineering/electronics, computer, Telecommunications and Information technology, ECTI-CON 2021

作者： Kesorn, Suthisa Wongsin, Norakamon Jangjing, Thinnawat Mahatthanajatuphat, Chatree Rakluea, Paitoon Torrungrueng, Danai Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Thanyaburi12110 Thailand King Mongkut's University of Technology North Bangkok Department of Electrical and Computer Engineering Bangkok10800 Thailand

ISBN: (纸本)9780738111278

In this paper, A Textile Right Angle Slot Antenna with Dual-band Circularly Polarization, the improved dual-band circularly polarized (CP) omni-directional based on the right-angle slot in textile is presented. This technique uses different slot sizes appropriately in various positions. The right-angle slot, which consists of the two smaller right-angle slots and two large right-angle slots, produces a circularly polarized (CP) and dual-band resonance. This paper presents an analysis of right-angle slot microstrip antennas to obtain dual-frequency, achieving ± 45 linear polarization in a slant. The presented textile right angle slot antenna covers the 2.45 GHz and 5.25 GHz frequency bands supporting the wireless local area network. A prototype of a model antenna and experiment is made. The measured results are shown to be reasonable for simulated results. Moreover, the antenna's maximum gain at 2.45 GHz and 5.25 GHz was around 5.2 dBi and 5.5 dBi, respectively. © 2021 IEEE.

关键词： Slot antennas

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Benchmarking Cryptographic Protocols for (IoT) Malware Defence

Benchmarking Cryptographic Protocols for (IoT) Malware Defen...

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International Symposium on Applied Machine Intelligence and Informatics (SAMI)

作者： Khalil Ben Khaled Dönìz Borsos Jozsef Katona Kandó Kalmán Faculty of Electrical Engineering Institute of Microelectronics and Technology Obuda University Budapest Hungary Kándó Kálmán Faculty of Electrical Engineering Institute of Electronics and Communication Systems Obuda University Budapest Hungary Department of Software Development and Application Institute of Computer Engineering University of Dunaujvaros Dunaujvaros Hungary GAMF Faculty of Engineering and Computer Science John Von Neumann University Kecskemet Hungary Department of Applied Quantitative Methods Faculty of Finance and Accountancy Budapest Business University Budapest Hungary

ISBN: (数字)9798350379365

ISBN: (纸本)9798350379372

This study presents a comprehensive benchmarking analysis of cryptographic protocols for Internet of Things (IoT) malware defense. The framework was specifically tailored to evaluate cryptographic protocols such as AES-128, AES-256, ChaCha20, RSA (1024-bit, 2048-bit, and 4096-bit), SHA256, SHA512, and HMAC-SHA256 were tested in both standalone and emulated environments. The primary objective was to evaluate the performance and resource consumption of these protocols, focusing on their encryption and hashing efficiency. Key innovations include the comparative analysis of resource consumption and performance efficiency across diverse cryptographic operations, under both real-world and emulated conditions. By identifying protocols like ChaCha20 for high efficiency and minimal resource usage, and RSA 4096-bit for enhanced security at higher computational costs, this study provides actionable insights into the trade-offs between security and performance. These findings offer a foundational reference for selecting optimized cryptographic protocols, advancing IoT malware defense strategies through informed decision-making.

关键词： Performance evaluation Technological innovation Benchmark testing Malware Encryption Internet of Things Reliability Informatics Machine intelligence Cryptographic protocols

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Comparative study for different types of mppt algorithms using direct control method 1

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2nd International Conference on Electrical and electronics engineering, ICEEE 2021

作者： Shams, Immad Mohammed, Karam Khairullah Mekhilef, Saad Tey, Kok Soon Power Electronics and Renewable Energy Research Laboratory Department of Electrical Engineering Faculty of Engineering Kuala Lumpur50603 Malaysia Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru81310 Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology MelbourneVIC3122 Australia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

ISBN: (数字)9789811607493

ISBN: (纸本)9789811607486

Maximum power point tracking (MPPT) controllers achieve higher importance due to the high expansion rate of photovoltaic systems. The probability of partial shading conditions (PSCs) is also increased which drastically reduced the maximum power extraction capability from PV systems. In this paper, different types of MPPT algorithms using direct control method have been examined under the same test conditions. The current research trend on MPPT controllers has also been highlighted. This paper gives a brief description to engineers and practitioners working in the area of MPPT controllers. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021.

关键词： Controllers

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Fortifying Database Security: Integrating Advanced Honeypot technology for Resilient SQL Injection Defense

Fortifying Database Security: Integrating Advanced Honeypot ...

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Communication, computer Sciences and engineering (IC3SE), International Conference on

作者： Kumaran U Thammisetty Swetha G. S. N. Santhosh Reddy Harini Ala Senthil Murugan Nagarajan B. Umah Department of Computer Science & Engineering Amrita School of Computing Bengaluru India Department of Computer Science & Engineering (Data Science) Madanapalle Institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka India Department of Mathematics Faculty of Science Technology and Medicine University of Luxembourg Luxembourg Department of Computer Science & Engineering Amrita School of Engineering Amrita Vishwa Vidyapeetham Bengaluru Karnataka India

ISBN: (数字)9798350366846

ISBN: (纸本)9798350366853

The dynamic nature of cyber threats necessitates the development of novel approaches to strengthen database system security. The novel method for fortifying defenses against SQL injection attacks—a pervasive and ongoing danger in the field of cybersecurity—is presented in this study. The suggested methodology creates a strong and adaptable defense mechanism by seamlessly integrating cutting-edge honeypot technology with current SQL injection mitigation techniques. Our approach, which we have named HoneySQLGuard, uses carefully con- structed honeypot tables inside the database system. These tables serve as sly bait, drawing potential attackers’ attention away from genuine data and toward themselves. By utilizing the most recent developments in technology, Honey SQL Guard surpasses the limitations of conventional signature-based detection techniques and is able to adjust to changing attack strategies via anomaly and machine learning. The honeypot tables are cleverly incorporated within the database architecture, making them invisible to an outside observer when compared to real tables. The technology immediately recognizes and isolates unusual behavior suggestive of SQL injection attempts by closely monitoring and analyzing query patterns. Furthermore, HoneySQLGuard has real-time reporting and alerting functions that guarantee an early reaction to any possible intrusions. Extensive testing is conducted to validate the effectiveness of the proposed solution, which shows it can dramatically lower the success rate of SQL injection attacks while keeping false-positive rates low. This study advances the subject by providing a thorough and flexible method for fortifying databases against SQL injection attacks, in line with the industry's increasing demand for robust cybersecurity defenses.

关键词： Protocols Prevention and mitigation Machine learning SQL injection Observers Database systems Real-time systems

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Apple Leaf Disease Identification Using Resnet50

Apple Leaf Disease Identification Using Resnet50

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

作者： S. Karthigaiveni R. Archana Vasanth Ravi P Salma Khatoon Department of Electronics and Communication Engineering Chennai Institute of Technology Chennai Department of Artificial intelligence and Data science St. Joseph's Institute of technology OMR Chennai Department of Computer Science and Engineering Faculty of Engineering and Technology Jain Deemed to be University Jain Global Campus Department of CSE-DS MLR Institute of Technology Hyderabad India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Apple leaf disease is a primary issue limiting apple yield and quality. The standard diagnostic approach requires considerable time for illness detection, causing farmers to frequently miss the optimal period for prevention and treatment. The detect of leaf diseases through leaf imagery is a vital study area in computer vision, with the primary objective being to devise an effective method for representing images of sick leaves. The efficient diagnosis and categorization of plant diseases constitutes a significant research aim in agriculture. This paper proposes a novel deep learning, ResNet. To evaluate the efficacy of the ResNet model, a number of comparative experiments were performed with leading transfer learning methodologies. This demonstrates that the proposed method is both possible and successful.

关键词： Plant diseases Accuracy Limiting Prevention and mitigation Transfer learning Feature extraction Mobile handsets Reliability Standards Residual neural networks

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Exhaled volatile organic compounds for cholangiocarcinoma diagnosis

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Liver Research 2022年第3期6卷 191-197页

作者： Nanicha Siriwong Thanikan Sukaram Rossarin Tansawat Terapap Apiparakoon Thodsawit Tiyarattanachai Sanparith Marukatat Rungsun Rerknimitr Roongruedee Chaiteerakij Department of Medicine Faculty of MedicineChulalongkorn UniversityBangkokThailand Division of Gastroenterology Department of MedicineFaculty of MedicineChulalongkorn Universityand King Chulalongkorn Memorial HospitalThai Red Cross SocietyBangkokThailand Department of Food and Pharmaceutical Chemistry Faculty of Pharmaceutical SciencesChulalongkorn UniversityBangkokThailand Department of Computer Engineering Faculty of EngineeringChulalongkorn UniversityBangkokThailand Clinical Sciences PhD International Program Faculty of MedicineChulalongkorn UniversityThailand Image Processing and Understanding Team Artificial Intelligence Research GroupNational Electronics and Computer Technology Center(NECTEC)Thailand Center of Excellence for Innovation and Endoscopy in Gastrointestinal Oncology Chulalongkorn UniversityBangkokThailand

Objectives:The difficulties in the early detection consequent to the lack of sensitive biomarkers render patients with cholangiocarcinoma(CCA)to have poor ***,sensitive and specific volatile organic compounds(VOCs)were identified in several ***,the VOC profiles in CCA are not ***,we investigated the VOC profiles in exhaled breath of CCA patients and ***:We prospectively collected exhaled breath samples from 30 consecutive patients newly diagnosed with CCA and 30 controls who did not have CCA(seven had benign biliary strictures and 23 had other medical conditions).Exhaled VOCs were identified using gas chromatography mass spectrometry Triple Quadrupoles *** of the significant differences in VOCs between cases and controls was conducted using supervised multivariate regression *** validation was performed for these VOCs in another cohort of 18 CCA patients and 22 ***:Levels of six compounds were significantly different between CCA patients and controls,namely,acetone,isopropyl alcohol,dimethyl sulfide,1,4-pentadiene,allyl methyl sulfide,and N,*** and dimethyl sulfide were independently associated with CCA as demonstrated in the multivariate *** the cut-off value of 8.59107 arbitrary unit(AU),acetone had a sensitivity and specificity of 82.1%and 75.8%,respectively,with an area under the receiving operator curve(AUROC)of 0.85 for the CCA *** level was also significantly different between cases and controls in the validation *** the same cut-off value,the sensitivity,specificity,and AUROC was 59.1%,66.7%,and 0.85,***:Breath analysis may potentially be useful for CCA diagnosis.A cohort of patients with earlystage CCA in further studies is needed to confirm the ability of exhaled VOCs for the early detection of CCA.

关键词： Cholangiocarcinoma(CCA) Bile duct cancer Volatile organic compounds(VOCs) Biomarker Cancer screening Diagnostic model

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Algorithm for Resource Allocation and Computing Offloading in 6G Networks: Deep Reinforcement Learning-based

Algorithm for Resource Allocation and Computing Offloading i...

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International Conference on Mechatronics (ICOM)

作者： Mamoon M. Saeed Rashid A. Saeed Elmustafa Sayed Ali Rania A. Mokhtar Othman O. Khalifa Department of Communications and Electronics Engineering Faculty of Engineering University of Modern Sciences (UMS) Yemen Department of Computer Engineering College of Computers and Information Technology Taif University Taif Saudi Arabia Department of Electrical & Electronic Engineering Faculty of Engineering Red Sea University PortSudan Sudan Department of Electronics Engineering Faculty of Engineering Sudan University of Science and Technology Khartoum Sudan Dept of Elec and Comp Eng Inter. Islamic Uni. Malaysia KL Malaysia Libyan Center for Engineering Research and Info. Tech. Bani Walid Libya

ISBN: (数字)9798350349788

ISBN: (纸本)9798350349795

The emergence of the sixth generation (6G) wireless networks brings new challenges and opportunities for efficient computing offloading and resource allocation. This paper proposes a novel Deep Reinforcement Learning-based Computing Offloading and Resource Allocation (DRL-CORA) algorithm for 6G networks. The algorithm leverages the power of deep reinforcement learning to dynamically determine the optimal computing offloading decisions and resource allocation strategies. The Deep Reinforcement Learning-based DCORA algorithm for computation offloading and resource allocation is effective, as demonstrated by our simulations. When compared directly, the suggested DCORA algorithm performs 15% better than other baseline systems.

关键词： 6G mobile communication Energy consumption Mechatronics Heuristic algorithms Wireless networks Quality of service Deep reinforcement learning

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VanillaKD: Revisit the Power of Vanilla Knowledge Distillation from Small Scale to Large Scale

arXiv

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arXiv 2023年

作者： Hao, Zhiwei Guo, Jianyuan Han, Kai Hu, Han Xu, Chang Wang, Yunhe School of information and Electronics Beijing Institute of Technology China School of Computer Science Faculty of Engineering The University of Sydney Australia Huawei Noah’s Ark Lab

The tremendous success of large models trained on extensive datasets demonstrates that scale is a key ingredient in achieving superior results. Therefore, the reflection on the rationality of designing knowledge distillation (KD) approaches for limited-capacity architectures solely based on small-scale datasets is now deemed imperative. In this paper, we identify the small data pitfall that presents in previous KD methods, which results in the underestimation of the power of vanilla KD framework on large-scale datasets such as ImageNet-1K. Specifically, we show that employing stronger data augmentation techniques and using larger datasets can directly decrease the gap between vanilla KD and other meticulously designed KD variants. This highlights the necessity of designing and evaluating KD approaches in the context of practical scenarios, casting off the limitations of small-scale datasets. Our investigation of the vanilla KD and its variants in more complex schemes, including stronger training strategies and different model capacities, demonstrates that vanilla KD is elegantly simple but astonishingly effective in large-scale scenarios. Without bells and whistles, we obtain state-of-the-art ResNet-50, ViT-S, and ConvNeXtV2-T models for ImageNet, which achieve 83.1%, 84.3%, and 85.0% top-1 accuracy, respectively. PyTorch code and checkpoints can be found at https://***/Hao840/vanillaKD. Copyright © 2023, The Authors. All rights reserved.

关键词： Distillation

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Investigating the Potential of Machine Learning for Enhancing Real-Time Data Analysis

Investigating the Potential of Machine Learning for Enhancin...

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Smart Generation Computing, Communication and Networking (SMART GENCON), International Conference on

作者： Khushboo Singh V. Mahalakshmi Pranali G. Chavhan Prateek Garg Girija Shankar Sahoo Sukumar R Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Electrical and Electronics Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Computer Engineering Vishwakarma Institute of Information Technology Pune INDIA Centre of Interdisciplinary Research in Business and Technology Chitkara University Institute of Engineering and technology Chitkara University Punjab India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Faculty of Engineering and Technology JAIN (Deemed-to-be University) Ramnagar Karnataka India

This abstract discusses the capability of system learning to serve as a device for real-time facts evaluation and its ability to decorate the accuracy of the resulting insights. Specifically, it examines the advantages of computerized function engineering and the way system gaining knowledge of primarily based tools may be leveraged to offer businesses with actionable data insights extra efficaciously than conventional strategies. It additionally looks at a few rising technologies, inclusive of deep neural networks, and their capability to push the limits of information analysis in real-time, and also discusses how improvements in allotted computing can similarly supplement the impact of gadget studying gear. Eventually, this summary additionally affords some demanding situations associated with actual-time evaluation of massive volumes of facts, and some advised answers.

关键词：

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Optimizing Inventory Management using Demand Forecasting based Ensemble Stacking Model with Hyperparameter Tuning

Optimizing Inventory Management using Demand Forecasting bas...

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Intelligent Systems and Computational Networks (ICISCN), International Conference on

作者： V. Pavithra Haayder M. Abbas Manjula B M P. K. Chidambaram Jothilakshmi S Department of Computer Science and Applications Faculty of Science and Humanities SRM Institute of Science and Technology Chennai India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Electronics and Communication Engineering Nitte Meenakshi Institute of Technology Bengaluru India Department of Mechanical Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India Department of Information Technology Dhanalakshmi Srinivasan College of Engineering and Technology Mamallapuram India

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

In recent days, the effective inventory management relies on accurate demand forecasting for effective management of business balance stocks. However, the traditional forecasting methods often struggled with different complexities such as variability, overstocking and wasted resources. In this research, the Ensemble Stacking Model with Hyperparameter Tuning (En-Stacking-HT) is proposed for effective optimized inventory management. Initially, the data is collected from M5 accuracy completion dataset and further preprocessed with data imputation, z-score normalization. These preprocessing techniques handles the missing values in data and standardizes the data in significant range. Then, Seasonal and Trend Decomposition (STL) is employed for extracting seasonality and trend features from preprocessed data by using Locally Estimated Scatterplot Smoothing (LOESS). Finally, the proposed En-Stacking-HT is utilized for inventory management prediction and optimized through hyperparameter tuning for predicting effective optimized inventory management for demand forecasting. From the results, the proposed En-Stacking-HT attained better results in terms of Mean Absolute Error (MAE) (16.82), Root Mean Square Error (RMSE) (22.85) and Mean Absolute Percentage Error (MAPE) (4.97%) when compared to existing Random Forest (RF).

关键词： Accuracy Computational modeling Stacking Demand forecasting Inventory management Predictive models Market research Feature extraction Imputation Tuning

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