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3rd EAI International Conference on Cognitive Computing and Cyber Physical systems, IC4S 2022

作者： Pareek, Prakash Maurya, Naveen Kumar Singh, Lokendra Gupta, Nishu Reis, Manuel J. Cabral S. Department of Electronics and Communication Engineering Vishnu Institute of Technology Andhra Pradesh Bhimavaram534202 India Department of ECE Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522002 India Department of Electronic System Faculty of Information Technology and Electrical Engineering Norwegian University of Science and Technology Gjøvik2815 Norway Engineering Department UTAD/IEETA Vila Real5001-801 Portugal

ISBN: (纸本)9783031289743

This work is focused on the exploration of the potential of Group IV alloy based nanostructured photodetector which can perform well in smart city environment. The theoretical model for multiple quantum well detector based on Germanium Tin (GeSn) alloy working in short wave infrared range (SWIR) is proposed after considering various design aspects. In this work, detectivity is estimated for SiGeSn/GeSn based interband multiple quantum well infrared photodetector. Detectivity is calculated by using responsivity and dark current, assuming appropriate conditions. After calculation, it is studied under variation of some important parameters. The result reveals the enhancement of detectivity with number of wells. It also indicates that the low biasing range is quite sufficient to make the proposed device work efficiently. Moreover, peak detectivity in the tune of 109 cm Hz1/2 W−1 is attained. © 2023, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Tin alloys

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Organising unstructured data using Double Net Self-Organising Map (DNSOM) model

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Soft Computing 2025年第7期29卷 3533-3554页

作者： You, Cheng Chun Lim, Seng Poh Lee, Chen Kang Tan, Joi San Lim, Seng Chee Department of Computer Science Faculty of Information and Communication Technology Universiti Tunku Abdul Rahman Jalan University Bandar Barat Perak Kampar31900 Malaysia Department of Computing Lee Kong Chian Faculty of Engineering and Science Universiti Tunku Abdul Rahman Jalan Sungai Long Bandar Sungai Long Cheras Selangor Kajang43000 Malaysia School of Computing and Informatics ViTrox College 746 Persiaran Cassia Selatan 3 Batu Kawan Industrial Park Penang Bandar Cassia14110 Malaysia

Surface reconstruction is the process of representing surfaces using the data obtained from scanning devices. When the data obtained is unstructured during data collection, it can cause problems in presenting the surface due to the lack of connectivity information for the data. It shows that data collection is a crucial task in surface reconstruction. Previous works have proved that Self-Organising Map (SOM) models can be used to organise unstructured data. However, the 2-D SOM model will generate a surface with holes for the closed surfaces. Besides, the 3-D SOM model will generate an incorrect surface due to the connectivity of the internal neurons. In addition, the Cube Kohonen SOM (CKSOM) model is limited to the same grid size. Hence, a SOM model known as Double Net SOM (DNSOM) is proposed via the merging of two 2-D SOMs to overcome the issues. Three data sets (cube, sphere, and talus bone) with different grid sizes are applied to test the models. When the grid size of all the models increases, the surface becomes smoother. The DNSOM contains a lower quantisation error than the 2-D SOM and the lowest topographic error among the other SOM models. It performs faster than the 3-D SOM and CKSOM. It presents the correct closed surface with a smaller number of neurons and different grid sizes. Microsoft Visual Studio 2022 with the C++ programming language is used to develop the models, while GNUPlot is used to visualise the results. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Visualization

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach

Initial Study of Oil Palm Crop Condition Detection Through D...

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International Conference on Informatics, Multimedia, Cyber and Information system (ICIMCIS)

作者： Winata Nugraha Ermatita Deris Stiawan Faculty of Computer Science Doctoral Program Faculty of Engineering Universitas Sriwijaya Palembang Indonesia Department of Information System Universitas Sriwijaya Palembang Indonesia Faculty of Computer Science Universitas Sriwijaya Palembang Indonesia

ISBN: (数字)9798331533243

ISBN: (纸本)9798331533250

Oil palm plantations in Indonesia still have many challenges, especially in terms of monitoring and mapping technology. One of the important aspects in the monitoring stage of oil palm plantations is monitoring the productivity of oil palm plants. To identify the oil palm plants, many previous studies have utilized image analysis to solve the problem. The problem is that poor image conditions can affect the architecture model to be built.[1] So in this research we do tune mapping on the image to produce a quality image. Each image data that has been improved will be feature extracted using ResNet and VGG to produce the best model. Furthermore, the classification model development process is carried out using CNN architecture as the model architecture. We also provide innovation by using object detection technology. Which uses artificial neural networks. Here we train the model using aerial video datasets of oil palm plants obtained from drones. Furthermore, to evaluate the performance of the above technique, it is proposed to use the mapping model, precision and accuracy evaluation obtained in the test phase[2]

关键词： YOLO Productivity Technological innovation Oils Multimedia systems Plantations Feature extraction Informatics Monitoring Drones

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Towards Real Time Traffic Analysis with Reduced Image Quality

Towards Real Time Traffic Analysis with Reduced Image Qualit...

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IEEE India Conference (INDICON)

作者： Rohit Mohanraj Erunika Oshani Dayaratna Department of Electronics and Communication Engineering National Institute of Technology Warangal Warangal India Department of Statistics and Computer Science Faculty or science University of peradeniya SriLanka

ISBN: (数字)9798350391282

ISBN: (纸本)9798350391299

This paper presents a detailed study of two computationally efficient object recognition models-YOLOv5 and Fast R-CNN with a MobileNet backbone-focusing on their performance under different degrees of image / video qualities. Blurring is a common problem in autonomous driving due to motion, weather conditions, and other factors, which can greatly reduce picture quality and, consequently, the accuracy of object recognition. Also, the low-quality images produced by on-car devices suffer when trying to utilize them for advanced detection applications. Utilizing the Zenseact Open Dataset (ZOD), images are generated by varying kernel sizes of Gaussian blur to examine how each model's recognition accuracy is affected by picture degradation. Our study shows key differences in the stability of these models, showing the trade-offs between processing speed and recognition confidence in poor visual settings. The results provide helpful guidance for choosing object recognition systems that keep performance in difficult situations, crucial for the safety and efficiency of driverless cars.

关键词： YOLO Performance evaluation Degradation Training Adaptation models Visualization Accuracy Real-time systems Object recognition Video recording

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Design and Implementation of Flutter Based Multi-Platform Docker Controller App

Design and Implementation of Flutter Based Multi-Platform Do...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Saxena, Adarsh Singh, Sudhakar Prakash, Shiv Yadav, Nand Lal Yang, Tiansheng Rathore, Raikumar Singh Singh, Shreya University of Allahabad Department of Electronics and Communication Prayagraj India University of South Wales Faculty of Business and Creative Industries Pontypridd United Kingdom Cardiff School of Technologies Cardiff Metropolitan University Cardiff United Kingdom Institute of Engineering and Technology Department of Computer Science & Engineering Dr. A.P.J. Abdul Kalam Technical University Lucknow India

ISBN: (纸本)9798350369106

This paper focuses on developing a Flutter application for controlling Docker resources remotely. The application provides a user-friendly interface for executing various Docker-related commands on the server where the Docker engine is installed. The application uses the SSH protocol to establish a secure connection with the server and execute the commands. Further, an alternative approach is also explored, which involves connecting the application with the Docker engine using HTTP. This proposed Docker controller application provides a significant advantage for managing Docker resources remotely, which is highly beneficial in DevOps fields. It provides a user-friendly interface to manage containers, making it easy to create, start, stop, restart, and remove containers. It abstracts away the complexities of working with Docker commands, allowing users to interact with containers more intuitively. It can be used to manage a number of docker engines from one place making it easy to control and monitor all the docker resources. Its performance, security, and scalability are evaluated using various testing techniques, and the results are found satisfactory. Further improvements may include enhancing the application's features, optimizing the performance, and exploring other possible approaches for establishing the connection between the application and the Docker engine. © 2024 IEEE.

关键词： HTTP

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Colour Image Cryptography using Chaotic Pixel Shuffling and 3D Logistic Diffusion 4

Colour Image Cryptography using Chaotic Pixel Shuffling and ...

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4th International Conference on Information Management and Machine Intelligence, ICIMMI 2022

作者： Ravi, Renjith V Goyal, S.B. Singla, Sanjay Department of Electronics and Communication Engineering M.E.A Engineering College Kerala Malappuram India Faculty of Information Technology City University Petaling Jaya Malaysia Department of Computer Science and Engineering Uie Chandigarh University Punjab Mohali India

ISBN: (纸本)9781450399937

All types of multimedia data, including digital images, audio, text, and video, are now easily accessible on the internet due to the phenomenal growth of internet services and wireless telecommunication systems, including mobile and computer systems, in order to achieve a specific degree of security, integrity, and secrecy as well as to prevent malicious access to the information. In contrast, for data storage and transmission, cryptographic procedures are necessary. This study presents brand-new, very effective colour image encryption and decryption algorithms that use diffusion and pixel scrambling. At first Zig-Zag permutation was used to scramble the data. Then, by concealing the statistical makeup of the pixels, the two-dimensional Lozi map is used to distort further the connection between neighbouring pixels in the original image. Furthermore, the diffusion in three colour channels is accomplished using a 3D logistic map. During the decryption process, the image pixels are again moved back to their original positions. The chaotic mapping technique encrypts images to reduce the correlation between encrypted and unencrypted versions. As described in this study, the tested and examined encryption algorithm on certain colour photos showed substantial security and authenticity to resist statistical and differential cyber attacks. © 2022 ACM.

关键词： Pixels

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Diagnosis Expert system on Breast Cancer Using Fuzzy Logic and Clustering Technique 5th

Diagnosis Expert System on Breast Cancer Using Fuzzy Logic a...

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5th International Conference on Information systems and Management science, ISMS 2022

作者： Awotunde, Joseph Bamidele Sisodia, Dilip Singh Ayodele, Peace Ayomide Ogundokun, Roseline Oluwaseun Chouhan, Virendra Singh Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria National Institute of Technology Raipur Raipur India Department of Multimedia Engineering Kaunas University of Technology Kaunas44249 Lithuania Manipal University Jaipur India

ISBN: (纸本)9783031311529

The disease of breast cancer is now widespread throughout the world. Expert systems are useful tools that have been effective in the diagnosis of various diseases. Therefore, this paper develops an expert system for diagnosis and detection of breast cancer using fuzzy logic enabled with clustering classifier. The fuzzy logic provides a bridge in improving the classification and detection accuracy of breast cancer disease. To balance out the dataset and avoid the costly overhead of collecting and labeling a large set of training tuples or patterns, clustering was utilized for data segmentation. The classifier can then make use of data of each group in terms of how similar they are. These tools have been developed to improve and alter the current expert systems, increasing their usefulness for the reasons for which they were designed. The fuzzy rule-based method includes expert knowledge to address the patient's ailment and render a reliable judgment in accordance with established rules. The proposed model can be applied as a clinical decision support tool to help healthcare professionals. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Diseases

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An Effective Cervical Cancer Detection Approaches - A Comparative Analysis

An Effective Cervical Cancer Detection Approaches - A Compar...

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International Conference on Recent Trends in Advance Computing (ICRTAC)

作者： S. Maheswari C. N. Marimuthu B. Deepa S. Geetha Faculty of Information and Communication Engineering Anna University Chennai India Department of Electronics and Communication Engineering Nandha Engineering College Erode India Department of Computer Science and Engineering Nandha Engineering College Erode India

The chance to preserve a life is provided by early identification of cervical cancer, which is the fourth most common malignancy among women globally. Early diagnosis can lower its frequency. However, due to a number of variables, manual screening techniques are ineffective in detecting cervical cancer. However, this leads to incorrect diagnoses and excessive therapy. Therefore, researchers suggested cervical identification using both conventional and deep learning (DL) methods. This study examines future approaches for automated cervical cancer diagnosis and reviews prior work, focusing on the DL area. This is thought to ensure accurate diagnosis and may possibly lower the incidence of cervical cancer. The comparison chart is provided among various techniques in which EL-SVM model shows higher accuracy.

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Design of IoT based physical protection system for nuclear facility 2

Design of IoT based physical protection system for nuclear f...

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2nd IEEE International Conference on Electronic engineering, ICEEM 2021

作者： Ateya, Muhammad Z. El-Din Hemdan, Ezz El-Samie, Fathi E. Abd Egypt Menoufia University Faculty of Electronic Engineering Department of Computer Science and Engineering Egypt Menoufia University Faculty of Electronic Engineering Department of Electronics and Communication Engineering Egypt

ISBN: (纸本)9781665418423

Radioactive materials are an attractive target for terrorist or criminal groups, because they can be used throughout the world in applications including agriculture, industry, medicine, scientific research, transport, and water desalination. Criminals can build dirty bombs by using these radioactive materials. The dirty bomb has harmful consequences on humanity. Keeping radioactive materials from any malevolent human attack is a must. Therefore, this work aims to strengthen the Physical Protection system (PPS) in nuclear facilities against criminal activities. This system works to provide the detection function of adversary actions through integrating various components. These components include Internet-of-Things (IoT) controller, magnetic sensors, vibration sensors, and Passive Infra-Red (PIR) motion detection sensors with cloud-based IoT platform and mobile application for monitoring and auditing purposes, remotely. The proposed work is intended for securing nuclear facility that contains radioactive materials through detection using various sensors and IoT controllers. A controller detects trigger signals from any sensor, an alarm and warning signs are sent to the dashboard and smartphone over the Internet. © 2021 IEEE.

关键词： Internet of things

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A graph-based CNN-LSTM stock price prediction algorithm with leading indicators

A graph-based CNN-LSTM stock price prediction algorithm with...

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作者： Wu, Jimmy Ming-Tai Li, Zhongcui Herencsar, Norbert Vo, Bay Lin, Jerry Chun-Wei Shandong University of Science and Technology Qingdao China Department of Telecommunications Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Faculty of Information Technology Ho Chi Minh City University of Technology Ho Chi Minh Viet Nam Department of Computer Science Electrical Engineering and Mathematical Sciences Western Norway University of Applied Sciences Bergen Norway

In today’s society, investment wealth management has become a mainstream of the contemporary era. Investment wealth management refers to the use of funds by investors to arrange funds reasonably, for example, savings, bank financial products, bonds, stocks, commodity spots, real estate, gold, art, and many others. Wealth management tools manage and assign families, individuals, enterprises, and institutions to achieve the purpose of increasing and maintaining value to accelerate asset growth. Among them, in investment and financial management, people’s favorite product of investment often stocks, because the stock market has great advantages and charm, especially compared with other investment methods. More and more scholars have developed methods of prediction from multiple angles for the stock market. According to the feature of financial time series and the task of price prediction, this article proposes a new framework structure to achieve a more accurate prediction of the stock price, which combines Convolution Neural Network (CNN) and Long–Short-Term Memory Neural Network (LSTM). This new method is aptly named stock sequence array convolutional LSTM (SACLSTM). It constructs a sequence array of historical data and its leading indicators (options and futures), and uses the array as the input image of the CNN framework, and extracts certain feature vectors through the convolutional layer and the layer of pooling, and as the input vector of LSTM, and takes ten stocks in U.S.A and Taiwan as the experimental data. Compared with previous methods, the prediction performance of the proposed algorithm in this article leads to better results when compared directly. © 2021, The Author(s).

关键词： Long short-term memory

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