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9th International Electrical Engineering Congress, iEECON 2021

作者： Worasutr, Aman Worasawate, Denchai Pongthavornkamol, Tiwat Fukawa, Kazuhiko Kasetsart University Faculty of Engineering Department of Electrical Engineering Bangkok Thailand National Electronic and Computer Technology Center Location and Automatic Identification System Research Team Pathum Thani Thailand Tokyo Institute of Technology Department of Communications and Computer Engineering Tokyo Japan

ISBN: (纸本)9781728195841

This paper proposes the improved indoor human detection algorithm from data provided by W-Band FMCW radar by using k-mean technique. The data provided by the radar contain both humans and nun-human objects. To identify non-human objects, the obtained data are filtered by the velocity and RCS of the detected objects. The K-means algorithm is applied to the filtered dataset to estimate the positions of humans. The estimated positions of humans are much closed to the actual ones. © 2021 IEEE.

关键词： K-means clustering

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FMCW Radar-Based Enhancing Moving Target Positioning Accuracy in Spectrogram Mapping

FMCW Radar-Based Enhancing Moving Target Positioning Accurac...

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International Conference on Electrical Engineering/Electronics, computer, Telecommunications and Information technology, ECTI-CON

作者： Nattakarn Shutimarrungson Seksan Eiadkaew Akkarat Boonpoonga Krit Athikulwongse Suramate Chalermwisutkul Department of Business Computer Mahasarakham Business School Mahasarakham University Maha Sarakham Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering Research Center of Innovation Digital and Electromagnetic Technology (iDEMT) King Mongkut's University of Technology North Bangkok Bangkok Thailand National Electronics and Computer Technology Center National Science and Technology Development Agency Pathumthani Thailand Communication and Smart System Engineering The Sirindhorn International Thai- German Graduate School of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798350381559

ISBN: (纸本)9798350381566

In this paper, we propose a methodology aimed to enhancing the accuracy of moving target positioning using FMCW radar within spectrogram mapping. Utilizing a 10 GHz operating frequency, the FMCW radar effectively detects moving targets. However, the interpretation of intermediate frequency (IF) signals posed challenges due to multiple reflections and resulting ambiguous distances between objects. For solving this problem, we propose two steps of solution: employing an enveloping method and normalization of the IF signal. This process can extract maximum amplitude peaks specific to the target. Experimental validation involved detecting a drone moving away from the radar. Comparative analysis pre and post signal processing in spectrogram terms demonstrate the efficacy of our technique. Furthermore, verification of the detection range of radar was conducted using the GSM-015 GNSS speed meter. Results confirm the accuracy of the positioning capabilities of radar, emphasizing the effectiveness of the proposed approach.

关键词： Visualization Accuracy Refining Radar detection Radar Radar antennas Reflection

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AI Bot to Increase the Accuracy and Efficiency of Hiring Process of Business Organizations

AI Bot to Increase the Accuracy and Efficiency of Hiring Pro...

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

作者： Dilusha N.H.A.S Weerasinghe E.J.K.D Chamuditha S.M.A Wijekoon K.W.M Samantha Thelijjagoda Nishantha Giguruwa Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Dept. of Computer System Engineering Sri Lanka Institute of Information Technology Malabe Sri Lanka College of Sustainability and Tourism Ritsumeikan Asia Pacific University Beppu Oita Prefecture Japan

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

Recruitment in most organizations is normally a rather inefficient process where it takes quite a long time to match the right candidate to a given job. In this research, AI-based hiring system is recommended to improve the efficiency and accuracy of the hiring process with the help of NLP and machine learning algorithms. Employment enabling processes such as CV filtering, skill evaluation and personality profiling as well as job matching are achieved by the system. An NLP-based tool is used for short listing of CVs, assessment quizzes are used to evaluate different skills and qualifications of the candidates and chatbot enables communication between candidates and HRs, while an AI bot is used to complete different document verifications' processes. Recruitment operation is made easier and efficient, and matching of candidates to the available jobs is done better since it is less time-consuming and energy-demanding, giving a solution that may be implemented on a large scale. Further improvements are to be made in the fine tuning of algorithms, enlargement of comprehensiveness of systems and the implementation of actual sample cases before the introduction into the marketplace.

关键词： Accuracy Machine learning algorithms Filtering Organizations Machine learning Chatbots Organizational aspects Tuning Recruitment Qualifications

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Performance Analysis of IDS_MDL Algorithm to Predict Intrusion Detection for IoT Applications

Performance Analysis of IDS_MDL Algorithm to Predict Intrusi...

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National Conference on Radio Science (NSRC)

作者： Walaa Adel Mahmoud Mohamad Fathi Hesham El-Badawy Rowayda Sadek Information System Department College of Computing & Information Technology Arab Academy for Science Technology and Maritime Transport Cairo Egypt National Telecommunication Institute Cairo Egypt Faculty of Computer and Artificial Intelligence Helwan University Cairo Egypt

In the last year, many modern attacks have targeted Internet of Things (IoT) devices. IoT botnet attacks are the most common. Despite the plethora of detection and prevention systems for cyberattacks, a security mechanism for IoT settings is still required since these devices have certain restrictions that make it difficult to properly implement the detection system. These memory resources are limited to processing, storing, and calculating. An effective defense against cyberattacks is an intrusion detection system (IDS). To develop the rules for IDS_MDL-IoT, we used the contemporary botnet attack dataset. Because IoT devices have limited resources, it is difficult to employ all attack patterns in the initial anomaly dataset in traditional anomaly-based IDS. Therefore, in this study, the tested ML model resulting in NB has proven to be the best classification algorithm to classify the risk of attack with 99.7% accuracy predicting from a time-speed of 3 seconds, and the tested DL model resulting in RNN has proven to be the best classification algorithm to classify the risk of attack with 99% accuracy predicting from a time-speed of 8 seconds.

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Weighted Mask Recurrent-Convolutional Neural Network based Plant Disease Detection using Leaf Images 7

Weighted Mask Recurrent-Convolutional Neural Network based P...

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7th International Conference on Intelligent Computing and Control systems, ICICCS 2023

作者： Sharmila, R. Kamalitta, R. Moorthy Singh, Devesh Pratap Chauhan, Amit Acharjee, Purnendu Bikash Dhanalakshmi Srinivasan Engineering College Department of MCA Tamilnadu Perambalur India Information Technology K.Ramakrishnan College of Engineering Tamilnadu Trichy India Electronics Engineer Production Dhaksha Unmanned System Pvt Ltd Tamilnadu Chennai India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttrakhand Dehradun India Department of Life Sciences Karnataka Bengaluru India CHRIST University Faculty Member Computer Science Maharashtra Pune India

ISBN: (纸本)9798350397253

Large losses in output, money, and quality/quantity of agricultural goods are incurred due to plant diseases. Seventy percent of India's GDP is tied to the agricultural sector, thus protecting plants from diseases is crucial. For this reason, it is important to keep an eye on plants from the moment they sprout. The usual approach for this omission is naked eye inspection, which is more time-consuming, costly, and requires significant skill. Thus, automating the method for detecting diseases is necessary to speed up this process. It is imperative that image processing methods be used in the creation of the illness detection system. Disease detection involves a number of processes, including Weighted Mask R-CNN, GLCM feature extraction, Multi-thresholding image pre-processing, and K means image segmentation classification. The weighted Mask R-CNN outperforms the standard RNN, the Mask R-CNN, and the CNN in terms of accuracy and recall in analytical trials by a significant margin. © 2023 IEEE.

关键词： Recurrent neural networks

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The Effect of Data Augmentation on the Accuracy of Fish Species Classification Using Deep Learning

The Effect of Data Augmentation on the Accuracy of Fish Spec...

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International Conference on Informatics and Computing (ICIC)

作者： Ellya Helmud Catur Edi Widodo Oky Dwi Nurhayati Doctor Information System Postgraduate Diponegoro University Semarang Indonesia Faculty of Information Technology Information Systems Study Program Atma Luhur Institute of Science and Business Pangkalpinang Indonesia Physics Department Faculty of Science and Mathematics Diponegoro University Semarang Indonesia Department of Computer Engineering Faculty of Engineering Diponegoro University Semarang Indonesia

ISBN: (数字)9798331517601

ISBN: (纸本)9798331517618

Fish image classification presents an intriguing challenge in the field of computer vision. This research aims to develop an accurate classification model to differentiate between four different fish species using a convolutional neural network. The dataset used consists of $\mathbf{3 0 1 0}$ fish images, divided into training, validation, and testing sets. The convolutional neural network model was trained both with and without data augmentation. Evaluation results show that the model trained with data augmentation achieved an accuracy of $95 \%$ with a loss value of 0.0983, slightly better than the model without augmentation which achieved an accuracy of $94.56 \%$ with a loss value of $\mathbf{0. 1 7 9 4}$. This indicates that data augmentation techniques are effective in improving model performance, likely because augmentation helps the model generalize better to variations in fish image data. The results of this research demonstrate the significant potential of convolutional neural network for fish image classification tasks. The developed model can serve as a foundation for the development of computer vision-based applications such as automatic fish species identification in fisheries or educational applications. Further research can be conducted by exploring different convolutional neural network architectures, more advanced data augmentation techniques, and larger datasets to improve model performance.

关键词： Deep learning Training Accuracy Computational modeling computer architecture Fish Data augmentation Data models Convolutional neural networks Image classification

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R3−Rescale, Rotate and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyperchaotic systems

TechRxiv

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TechRxiv 2023年

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Por, Lip Yee Ku, Chin Soon Alexan, Wassim Cairo11835 Egypt Department of Computer Science and Information Engineering National Taipei University of Technology Taipei106344 Taiwan Department of Computer System and Technology Faculty of Computer Science and Information Technology Universiti Malaya Kuala Lumpur50603 Malaysia Department of Computer Science Universiti Tunku Abdul Rahman Kampar31900 Malaysia Cairo11835 Egypt New Administrative Capital Cairo13507 Egypt

This work proposes a novel image encryption algorithm that combines unique image transformation techniques with the principles of chaotic and hyperchaotic systems. The proposed algorithm performs rescaling, rotation, and randomization on the target image by exploiting the unpredictable behavior of the Chua system and the hyperchaotic nature of the Chen system. These chaotic systems’ unpredictability and sensitivity to initial conditions are utilized to create an encryption algorithm that provides an extensive key space of 25208, contributing to its robustness against brute-force attacks and amplifying its overall security. The efficiency of the algorithm is demonstrated in its computational speed and minimal resource consumption, making it practical for real-time applications. A series of rigorous tests and analyses were conducted to evaluate the algorithm’s performance, substantiating its capability to withstand a wide array of attacks, including differential attacks, statistical attacks, and brute-force attacks. With its vast key space, high efficiency, and robust resistance to attacks, the proposed algorithm shows potential as a vital tool for securing digital images in various applications, such as secure communication, data storage, and multimedia transmission. This research contributes to the ongoing development and evolution of high-security image encryption methodologies, meeting the escalating demand for advanced security in the digital information era. © 2023, CC BY.

关键词： Cryptography

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Image Splicing Detection Based on Texture Features with Fractal Entropy

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computers, Materials & Continua 2021年第12期69卷 3903-3915页

作者： Razi J.Al-Azawi Nadia M.G.Al-Saidi Hamid A.Jalab Rabha W.Ibrahim Dumitru Baleanu Department of Laser and Optoelectronics Engineering University of TechnologyBaghdad10066Iraq Department of Applied Sciences University of TechnologyBaghdad10066Iraq Department of Computer System and Technology Faculty of Computer Science and Information TechnologyUniversiti MalayaKuala Lumpur50603Malaysia IEEE:94086547 Kuala Lumpur59200Malaysia Department of Mathematics Cankaya UniversityBalgat06530AnkaraTurkey Institute of Space Sciences R76900 Magurele-BucharestRomania Department of Medical Research China Medical UniversityTaichung40402Taiwan

Over the past years,image manipulation tools have become widely accessible and easier to use,which made the issue of image tampering far more *** a direct result to the development of sophisticated image-editing applications,it has become near impossible to recognize tampered images with naked ***,to overcome this issue,computer techniques and algorithms have been developed to help with the identification of tampered *** on detection of tampered images still carries great *** the present study,we particularly focus on image splicing forgery,a type of manipulation where a region of an image is transposed onto another *** proposed study consists of four features extraction stages used to extract the important features from suspicious images,namely,Fractal Entropy(FrEp),local binary patterns(LBP),Skewness,and *** main advantage of FrEp is the ability to extract the texture information contained in the input ***,the“support vector machine”(SVM)classification is used to classify images into either spliced or *** analysis shows that the proposed algorithm performs better than recent state-of-the-art of splicing detection ***,the proposed algorithm achieves an ideal balance between performance,accuracy,and efficacy,which makes it suitable for real-world applications.

关键词： Fractal entropy image splicing texture features LBP SVM

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Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Document Ranking and Retrieval

Sparsity Aware of TF-IDF Matrix to Accelerate Oblivious Docu...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zeshi Zhang Guangping Xu Hongzhang Yang Yulei Wu School of Computer Science and Engineering Tianjin University of Technology Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China Ministry of Education Key Laboratory of Computer Vision and System China Faculty of Engineering University of Bristol Bristol U.K.

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

Due to cloud security concerns, there is an increasing interest in information retrieval systems that can support private queries over public documents. It is desirable for oblivious document ranking and retrieval in public cloud at lower cost and faster speed without revealing query-related information. Currently, the term frequency-inverse document frequency (TF-IDF) and private information retrieval (PIR) techniques are used to solve this problem, but the encryption operation time is over dominant. Motivated by the observation of the sparsity of the TF-IDF matrix, we propose an efficient approach for oblivious document ranking and retrieval, called E-Coeus. It takes advantage of the high sparsity of the TF-IDF matrix to rearrange the matrix. Our method accelerates the speed of PIR inadvertently retrieving documents and reduces the user retrieval delay time. In a stand-alone experiment for a TF-IDF matrix of 1.2M rows and 64K columns with the sparsity of 10%, E-Coeus improves the document ranking and retrieval performance by 23% over the state-of-the-art approach, Coeus. With cluster of 64 machines, E-Coeus improves the performance by 34% over Coeus when the TF-IDF matrix sparsity is 30%.

关键词： Time-frequency analysis Privacy Costs Cloud computing security Information retrieval Encryption Delays

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LiFi in Short-Range Wireless Communication of IoT 3

LiFi in Short-Range Wireless Communication of IoT

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3rd International Conference on Computing and Information technology, ICCIT 2023

作者： Asmak Ismail, Nurul Halimatul Awwad, Samer A. B. Hassan, Rosilah Sha'ban Kanaker, Hasan Mahmoud Bani-Awwad, Alaa Adnan Ali Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information System Riyadh Saudi Arabia Deanship of Information and Communication Technology Imam Abdulrahman Bin Faisal University Quality Assurance Department Dammam Saudi Arabia Fakulti Teknologi Maklumat & Sistem Maklumat Universiti Kebangsaan Malaysia Selangor Malaysia Isra University Faculty of Information Technology Cyber Security Department Jordan Ministry of Digital Economy and Entrepreneuship Jordan

ISBN: (纸本)9798350321487

In the new era, various communication systems rapidly evolve to pertain the user's needs. One of these emerging communication systems, which is crucial to providing better life aspects, is the Internet of Things (IoT). Communications between the devices in the IoT is the core part of the system. There are two types of communication between the devices in the IoT;short-range wireless communication and long-range wired or wireless communication. This paper is dedicated to discuss the short-range wireless communication in the IoT. These are not to limit but short-range wireless communication includes Bluetooth, Bluetooth Low Energy (BLE), Wireless-Fidelity (WiFi), Light-Fidelity (LiFi), Radio-frequency Identification (RFID), or ZigBee. This paper addresses the LiFi short-range wireless communication system from the operations, modulation techniques, and IEEE 802.15.7 layer architecture. Finally, a comparison of these short-range wireless communications has been made to comprehend their importance in the life of the IoT. © 2023 IEEE.

关键词： Internet of things

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