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IAENG International Journal of computer Science 2024年第7期51卷 765-784页

作者： Terlapu, Panduranga Vital Prasan, U.D. Kumar, T. Ravi Bendalam, Vijaya Pappu, Sasibhushana Rao Rao, M. Jayanthi Mohitha, Maddula Ratna Jayaram, D. Dept. of Computer Science and Engineering Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Sasibhushana Rao Pappu is an Assistant Professor of Dept. of Department of Computer Science and Engineering GITAM Andhra Pradesh Visakhapatnam India Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Dept. of CSE Aditya Institute of Technology and Management Tekkali Andhra Pradesh Srikakulam532 201 India Department of Information Technology Chaitanya Bharathi Institute of Technology Gandipet Telangana Hyderabad500075 India

Rice category identification by image analysis is essential to ensure the quality and safety of rice production. In this study, we propose an intellectual approach to improve rice category identification using deep transfer learning features and machine learning classifiers. Specifically, we extracted features from three pre-trained models (Inception V3, VGG-19 and VGG-16) using transfer learning techniques. These were used as inputs to train MultiLayerPerceptron (MLP) and support vector machine (SVM) classifiers. The results of our experiments show that the proposed strategic results achieve high accuracy in identifying rice categories. The SVM (polynomial kernel) achieves the second-highest accuracy among all models and features, with an accuracy of 0.9948 using the VGG-19 and 0.9912 using Inception V3. The MLP classifier with (30 30) hidden layers achieve the first high accuracy, with an accuracy of 0.9972 (99.72%) using VGG-19 features. The results also show that the choice of deep transfer learning model and machine learning (ML) classifier can significantly affect the accuracy of rice category identification. Among the three pretrained models, VGG-19 features consistently perform the best, followed by Inception V3 and VGG-16. The choice of MLP hidden layer size also affects the accuracy, with 30 HL neurons achieving the best performance. Our proposed approach using deep TL features and ML classifiers shows promising results in improving rice category identification. Our study provides valuable insights into optimizing ML models for agricultural image analysis. © (2024) International Association of Engineers. All rights reserved.

关键词： Support vector machines

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Design and Performance Analysis of a 38 GHz Microstrip Patch Antenna with Slits Loading for 5G Millimeter-Wave Communications 3

Design and Performance Analysis of a 38 GHz Microstrip Patch...

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3rd International Conference on Emerging Smart Technologies and Applications, eSmarTA 2023

作者： Ali, Mohammed A. M. Gaid, AbdulGuddoos S. A. Saeed, Mamoon M. Saeed, Rashid A. Taiz University Faculty of Engineering & Information Technology Dept. of Communications & Computer Taiz Yemen Faculty of Engineering Dept. of Communications & Electronics Engineering Sana'a Yemen College of Computers and Information Technology Taif University Dept. of Computer Engineering Taif21944 Saudi Arabia

ISBN: (纸本)9798350305333

In this paper, we present a compact, rectangular microstrip patch antenna with small rectangular slits designed for 5G wireless applications in the 38GHz band. The antenna is built on a Roger RT duroid 5880 substrate with a relative permittivity of 2.2, a height of 0.381 mm, and a loss tangent of 0.0009. The microstrip line technique is utilized for power feeding to the radiating patch. To enhance the antenna's performance, three slits are incorporated into the front side of the radiating patch. The design, simulations, and optimizations are conducted using the High-Frequency Structure Simulator (HFSS), while computer Simulation technology (CST) is used for validating the HFSS results. The antenna has a compact overall size of 6.12 × 6 × 0.381 mm3. Moreover, the proposed antenna exhibits impressive characteristics, including a resonance frequency of 38.67 GHz, a reflection coefficient (S11) of - 47.82 dB, and an impedance bandwidth of 2.35 GHz (37.45 GHz - 39.8 GHz). In addition, the antenna's gain varies from 7.84 dBi at the lower frequency of the operational band to 7.9 dBi at the higher frequency, with a maximum gain of 8.037 dBi at the resonance frequency. Likewise, the radiation efficiency is above 99% across the operational band. The results obtained from both simulators are in a good agreement and surpass those of many previously published antennas, indicating that the proposed antenna is a promising solution for 5G wireless communication applications. © 2023 IEEE.

关键词： Millimeter waves

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Steganographic Encryption and Decryption Using Duality Preserving GANs 22

Steganographic Encryption and Decryption Using Duality Prese...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Shah, Aaryak Kamal, Alok Kumar Neeraj Dept. of Information Technology ABV-IIITM Gwalior Gwalior India Dept. of Electrical/Electronics ABV-IIITM Gwalior Gwalior India Dept. of Computer Science and Engineering Mahindra University Hyderabad India

ISBN: (纸本)9798331527471

Image steganography has been a significant research area in the security sphere. The core idea revolves around embedding secret messages in existing images (covers) such that the resultant image displays no noticeable detectable artifacts of the secret while also being statistically similar to the original cover. Various steganographic techniques have been developed and tested. However, these traditional techniques often exploit some specific property of the image and are often easy to detect with the right tools. In recent times, generative adversarial networks, or GANs for short, have shown massive promise in their capability to modify and synthesize data. They are now ubiquitous in image synthesis and image translation tasks. With various recent advancements in this technology allowing these models to generate, in many cases, human-imperceptible data, GAN-based steganographic systems have become a significant focus of security research. This report analyzes and compares some existing models and attempts to provide a new solution that performs better on multiple well-defined metrics. A DualGAN-based architecture is proposed that trains both the encoder and decoder processes simultaneously while maintaining the underlying dual relationship of these tasks. The proposed scheme reports better results on the selected metrics (SBR, DER, PSNR) at three values of embedding capacity set at 0.3, 0.6, and 0.9. © 2024 IEEE.

关键词： Generative adversarial networks

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Investigating the Impact of Heuristic Algorithms on Cyberthreat Detection 2

Investigating the Impact of Heuristic Algorithms on Cyberthr...

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2nd International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2024

作者： Kalla, Dinesh Mohammed, Abdul Sajid Boddapati, Venkata Nagesh Jiwani, Nasmin Kiruthiga, T. Dept. of Computer Science Colorado Technical University CharlotteNC United States Dept of School of Computer and Information Sciences University of the Cumberlands Kentucky United States Dept. of Computer Science Microsoft CharlotteNC United States Dept. of Information Technology University of The Cumberlands WilliamsburgKY United States Department of ECE Vetri Vinayaha College of Engineering and Technology Tamil Nadu Trichy India

ISBN: (纸本)9798331541217

This paper investigates the effect of heuristic algorithms on cybersecurity hazard detection and mitigation. To be able to do so, the authors analyze the performance of two heuristics-based algorithms-the simple Heuristic set of rules (SHA) and greedy Heuristic set of rules (GHA)-on 4 distinct datasets. The datasets contain malicious and non-malicious packets. Effects of the experiments show that each algorithms exhibit extraordinarily correct predictions of malicious packets while as compared to the floor-reality labels, and showcase an excessive level of detection accuracy in detecting threats. The SHA set of rules tested the strongest performance, achieving an accuracy of as much as 95 and ninety-seven percentage on datasets. The authors further take a look at that once both algorithms are utilized in aggregate, the overall performance progressed. The research findings may be generalized in further contexts and assist the notion that heuristics algorithms may be effective in threat detection and mitigation. © 2024 IEEE.

关键词： Cybersecurity

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Enhancing Diabetes Detection: A Weighted Averaging Approach for Combined Model Accuracy 9

Enhancing Diabetes Detection: A Weighted Averaging Approach ...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Sami, Areesha Naseer, Anum Hussain, Muhammad Zunnurain Hasan, Muhammad Zulkifl Mustafa, Muzzamil Khalid, Aqsa Awan, Rimsha Ashraf, Farhan Khan, Zohaib Ahmed Javaid, Arslan University Lahore Department of Computer Engineering Information Technology Punjab Pakistan Bahria University Dept. of Computer Science Pakistan University of Central Punjab Faculty of Information Technology Department of Computer Science Lahore Pakistan National College of Business Administration And Economics Department of Computer Science Lahore Pakistan University Lahore Information Technology Pakistan

ISBN: (纸本)9798350394474

Diabetes, marked by prolonged high blood sugar levels, poses a significant global health challenge. Precise early prediction is vital but faces hurdles due to limited data and complexities like outliers. Uncontrolled diabetes heightens severe complications, driving research into advanced machine and deep learning techniques. Healthcare professionals recognize the crucial role of Machine Learning Algorithms (MLA) in enhancing disease identification. This study integrates deep and machine learning models through techniques like K-means, CNN, NB, and SVM. Combining their predictions using weighted averaging yields a 79.5% accuracy, improving diabetes prediction globally. © 2024 IEEE.

关键词： Support vector machines

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Enhancing Early Detection of Pancreatic Cancer: A Machine Learning Approach with Explainable AI Insights

Enhancing Early Detection of Pancreatic Cancer: A Machine Le...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Pattanashetti, Neeraj V Mayur, R. Prakash, Adarsh Adiga, Tryambaka B Kodipalli, Ashwini Rao, Trupthi Global Academy of Technology Dept. of Computer Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Information Science & Engineering Karnataka Bangalore India Global Academy of Technology Dept. of Artificial Intelligence & Data Science Karnataka Bangalore India

ISBN: (纸本)9798350376876

Among the most deadly and complex illnesses in the world, pancreatic cancer is typified by its early asymptomatic stage and subsequent discovery of the disease when it has progressed to an advanced stage. Getting the patients in a correct treatment path at the earliest point is important for suitable intervention and patient outcomes in the long run. We utilized advanced algorithms and model prediction techniques includes, feature extraction, hyperparameter tuning and explainability methods which aims to enhance both the comprehension and predictive accuracy of models employed. The goal is to assess patient health status and diagnose pancreatic diseases (cancer vs. non-cancer) by developing a learning algorithm. We obtained a dataset containing demographic information and urine biomarkers to start the investigation. First stage of our analysis employs a single classifier and later we apply bagging and boosting algorithms to achieve ensemble models. Hyperparameter fine-tuning has got a positive outcome, which has Gradient Boosting and Cat Boosting as its top performing models. The ensemble model produced excellent results (92.02 %), demonstrating the usefulness of the suggested approach. LIME and SHAP techniques play the key role in model outcome interpretability providing an insight of algorithmic decision-making process. Such a wide range of combinations of biomarkers have a potential of pancreatic cancer detection in early stages and thus will help shape diagnostics strategies in near future. © 2024 IEEE.

关键词： Diseases

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Heads-Up Multitasker: Simulating Atention Switching On Optical Head-Mounted Displays

Heads-Up Multitasker: Simulating Atention Switching On Optic...

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2024 CHI Conference on Human Factors in Computing Sytems, CHI 2024

作者： Bai, Yunpeng Ikkala, Aleksi Oulasvirta, Antti Zhao, Shengdong Wang, Lucia J. Yang, Pengzhi Xu, Peisen Dept. of Computer Science National University of Singapore Singapore Dept. of Computer Science Aalto University Espoo Finland Dept. of Information and Communications Engineering Aalto University Helsinki Finland School of Creative Media Dept. of Computer Science City University of Hong Kong Hong Kong Hong Kong Dept. of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge United States Dept. of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft Netherlands Dept. of Computer Science IPAL National University of Singapore Singapore

ISBN: (纸本)9798400703300

Optical Head-Mounted Displays (OHMDs) allow users to read digital content while walking. A better understanding of how users allocate attention between these two tasks is crucial for improving OHMD interfaces. This paper introduces a computational model for simulating users' attention switches between reading and walking. We model users' decision to deploy visual attention as a hierarchical reinforcement learning problem, wherein a supervisory controller optimizes attention allocation while considering both reading activity and walking safety. Our model simulates the control of eye movements and locomotion as an adaptation to the given task priority, design of digital content, and walking speed. The model replicates key multitasking behaviors during OHMD reading while walking, including attention switches, changes in reading and walking speeds, and reading resumptions. © 2024 Copyright held by the owner/author(s)

关键词： Reinforcement learning

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An Overview of Protocols-Based Security Threats and Countermeasures in WLAN 4

An Overview of Protocols-Based Security Threats and Counterm...

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4th International Conference for Emerging technology, INCET 2023

作者： Ojha, Nitish Kumar Baray, Elyas Indian Institute of Information Technology Dept. of Information Technology Allahabad211012 India Sharda University Knowledge Park - III Dept. of Computer Science and Engineering Greater Noida201310 India

ISBN: (纸本)9798350335750

WLAN security protocols are the most important section for wireless networks which controls all the security-related issues by securing the network with some pre-defined rules made by wireless-protected access organizations, further which makes it the main target of hacking and threats by interested parties and to be safe from such threats. There should be countermeasures for such threats where there will be a solution for any threat encountered and to mention. WIFI or wireless local area Network is a cost-effective easy deployment Network that makes users take advantage of vast data shared on the internet and by the network without the need for any cable for connections. Here in this overview paper by reviewing many researchers' techniques and works on the WLAN security topic, there are shared threats to WLAN and the ways of taking precautions against those threats as countermeasures and for which the tables are used. For comparison of techniques used by authors and some famous and highly dangerous threats are also defined further. WPA3's new security protocol is also defined with its newly found vulnerabilities and countermeasures except there is a conclusion on the downgrade attack problem with WPA3. Further, there are also countermeasures for that as well, nowadays people think the network is secured with standards defined by the IEEE organization but some know this is not the case so it makes it a more important area for research. © 2023 IEEE.

关键词： Wireless local area networks (WLAN)

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CapsiScan: Autonomous Robotic System for Monitoring and Managing Greenhouse-Based Capsicum Farms 5

CapsiScan: Autonomous Robotic System for Monitoring and Mana...

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5th International Conference on Advancements in Computing, ICAC 2023

作者： Jayakody, E.R.T. Rabel, W.T.T. Estrop, R.D.V. Herath, K.G.L.P. Harshanath, S.M.B. Abeygunawardhana, Pradeep K. W. Sri Lanka Institute Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka Sri Lanka Institute of Information Technology Dept. of Software Engineering Colombo Sri Lanka

ISBN: (纸本)9798350358131

The automation of processes in greenhouses provides a significant advantage to the agricultural sector. Due to the rapid growth of the population, increasing demand for food and labor shortage make the autonomous monitoring and management of agricultural work more important. Capsicum is a largely consumed vegetable in South Asia, which can be efficiently grown in greenhouses. Although there are many capsicum farms, they lack quality and efficient production to match the increasing demand. This paper proposes a robotic system for use in capsicum greenhouses that can navigate and monitor the plants to identify diseases, track the yield, and take remediation actions. The robot navigates to each plant in the greenhouse autonomously by using the SLAM approach with the use of laser range sensor data. The 4 DoF robotic arm of the robot has a camera and a sprayer attached to its end to investigate plants and spray remedies to diseased plants. The robotic arm moves around the plant to capture the images to detect deceases and count the pods to estimate the yield. The sprayer of the robot sprays remedies precisely to targeted plants to control pests and diseases. The system was successfully implemented and tested with the proposed functions. © 2023 IEEE.

关键词： Greenhouses

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Breathing+: A Game-based System for Enhancing Respiratory Training and Motivation 6

Breathing+: A Game-based System for Enhancing Respiratory Tr...

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6th IEEE International Conference on Knowledge Innovation and Invention, ICKII 2023

作者： Lin, Chang Yu Chu, Edward T.-H. National Yunlin University of Science and Technology Dept. of Computer Science and Information Engineering Yunlin Taiwan

ISBN: (纸本)9798350323535

Breathing difficulties, such as shortness of breath or rapid breathing, can significantly harm health and diminish the quality of life. Breathing exercises are widely recognized for their effectiveness in resolving such difficulties. They increase lung function, ease discomfort, and alleviate stress. However, current breathing-assisted methods, such as the incentive spirometer, do not accurately track the duration and frequency of training despite their advantages. Furthermore, the process of performing breathing exercises is tedious over time, making users feel bored and reducing their motivation. Thus, we presented Breathing+, a breathing video game designed to address these limitations by providing a series of game challenges. Breathing+ incorporated sensors to detect breathing signals and provided users with real-time feedback on a screen. According to the experimental results, Breathing+ achieved a breathing recognition accuracy of 95%. In addition, the response time to the user's breathing was around 0.23 s. © 2023 IEEE.

关键词： Motivation

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