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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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Integrating Noise Classification and Speech Enhancement Model for Hearing Aids 21

Integrating Noise Classification and Speech Enhancement Mode...

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21st International System-on-Chip Design Conference, ISOCC 2024

作者： Hung, Ying-Hsiu Chang, Yen-Ching Lai, Shin-Chi Juang, Wen-Ho Sheu, Ming-Hwa Lee, Jeng-Dao National Yunlin University of Science and Technology Dept. Electronics Engineering Douliu Taiwan National Formosa University Doctoral Degree Program in Smart Industry Technology Research and Development Huwei Taiwan National Formosa University Dept. Automation Engineering Smart Machinery and Intelligent Manufacturing Research Center Huwei Taiwan National Formosa University Dept. Computer Science and Information Engineering Huwei Taiwan National Formosa University Dept. Automation Engineering Huwei Taiwan

ISBN: (纸本)9798350377088

As the global population ages, the number of elderly people needing hearing aids is increasing. This paper proposed an architecture designed to enhance speech based on noise conditions. After noise types are identified by a Convolutional Neural Network (CNN) noise classification model, these noise conditions are combined with the noisy speech and input into a conditional Generative Adversarial Network (cGAN) for speech enhancement. The CNN noise classification model achieves an average recognition accuracy of nearly 92%, with a Kappa value as high as 0.88. The cGAN speech enhancement achieves lower error values in the Mel-Cepstral Distortion (MCD) measure. Although some parts of the human voice may be filtered out, the results demonstrate the potential development of the proposed algorithm. © 2024 IEEE.

关键词： Generative adversarial networks

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Social media image classification and retrieval method based on deep hash algorithm

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International Journal of Web Based Communities 2022年第3-4期18卷 276-287页

作者： Li, Zilong Zhou, Yong Wang, Hongdong School of Information Engineering Xu’zhou University of Technology Xuzhou221018 China School of Computer Science and Technology China University of Mining and Technology Xuzhou221116 China Post Doctoral Research Center Onnes Power Technology Co. Ltd. Xuzhou221003 China

In order to solve the problems of large classification error and low retrieval accuracy of traditional retrieval methods, a social media image classification retrieval method based on deep hash algorithm is proposed. With the help of CBOW model to extract semantic features and colour histogram to extract colour features of social media images, social media image classification is completed. Hash algorithm is used to deal with the unidirectional irreversibility of social media image, and the pixel feature points in the image are regarded as density function for one-to-one correspondence. The loss function is used to control the convergence of the hash algorithm to achieve social media image retrieval. Experimental results show that the error of social media image classification is only 2%, the retrieval accuracy is always higher than 90%, and the retrieval time is only 3.4 s, which has the advantage of high retrieval efficiency. © 2022 Inderscience Enterprises Ltd.

关键词： Clustering algorithms

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Efficiency of Nanostructured Layers Deposited on Solar Cells - hardware system proposal 14

Efficiency of Nanostructured Layers Deposited on Solar Cells...

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14th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2022

作者： Calinescu, Valentin Oproescu, Mihai Iana, Gabriel-Vasile Novac, Ovidiu Constantin Novac, Mihaela Cornelia Interdisciplinary Doctoral School University of Pitesti Pitesti Romania University of Pitesti Faculty of Electronics Communication and Computer Science Pitesti Romania University of Oradea Faculty of Electrical Engineering and Information Technology Oradea Romania

ISBN: (数字)9781665495356

ISBN: (纸本)9781665495356

This article aims to highlight the role of layers deposited on solar cells. By depositing different materials, the energy efficiency of the solar cells can be increased or decreased. In order to quantify how much the efficiency of a solar cell increases or decreases depending on the deposited layers, a hardware system is proposed to acquire specific parameters depending on different operating scenarios: solar radiation level, temperature, value of the load connected to the solar cell output. The solar cells to be analyzed will be covered with nanostructured layers that have different sizes and concentrations of material. © 2022 IEEE.

关键词： Solar cells

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Initial Study of Oil Palm Crop Condition Detection Through Drone Imagery: A YOLO Approach 6

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

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6th International Conference on Informatics, Multimedia, Cyber and information System, ICIMCIS 2024

作者： Nugraha, Winata Ermatita Stiawan, Deris Universitas Sriwijaya Faculty of Computer Science Doctoral Program Faculty of Engineering Palembang30139 Indonesia Universitas Sriwijaya Department of Information System Palembang30139 Indonesia Universitas Sriwijaya Faculty of Computer Science Palembang30139 Indonesia

ISBN: (纸本)9798331533243

关键词： Photomapping

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Hd Maps for Autonomous Vehicles: Challenges and Representing Frameworks

SSRN

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SSRN 2023年

作者： Al-Saidi, Muslem Aron, Ballagi Szechenyi Istvan University Doctoral School of Multidisciplinary Engineering Sciences Hungary Wasit University College of Computer Science and Information Technology Iraq

As a result of growing the technology in artificial intelligence and robotics, particularly the Highly Automated Driving (HAD) field, many companies, and researchers have focused on this area. thus, it is necessary to provide maps of high quality in terms of the information they contain, as they are very important for researchers in the various fields of research in HAD. A group of commercial companies provided high-quality maps which are used in many scientific types of research. These maps provide very clear and useful information that enables the self-driving vehicle to build approved and shortened paths and avoid various obstacles to facilitate the process of reaching the goal. Otherwise, there is worthless information because the large amount of data within the maps may need more time to process, especially if we know that the information required in HAD is only for a certain distance around the vehicle. On the other hand, there is information that is not available in the maps which may have high importance in HAD. Usually, this type of data is very important, especially in complex city scenarios. Therefore, the authors worked to customize these maps, almost by using the same technology, by self-created map format to meet their research needs. In this paper, we will investigate a group of these maps and touch on their advantages and disadvantages, and we will discuss some challenges of creating maps. © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Multi-channel Convolutional Neural Network for Hate Speech Detection in Social Media 9th

Multi-channel Convolutional Neural Network for Hate Speech D...

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9th EAI International Conference on Advancement of science and Technology, ICAST 2021

作者： Abebaw, Zeleke Rauber, Andreas Atnafu, Solomon IT Doctoral Program Addis Ababa University Addis Ababa Ethiopia Institute of Information Systems Engineering Technical University of Vienna Vienna Austria Department of Computer Science Addis Ababa University Addis Ababa Ethiopia

ISBN: (纸本)9783030937089

As online social media content continues to grow, so does the spread of hate speech. Hate speech has devastating consequences unless it is detected and monitored early. Recently, deep neural network-based hate speech detection models, particularly conventional single-channel Convolutional Neural Network (CNN), have achieved remarkable performance. However, the effectiveness of the models depends on the type of language they are trained on and the training data size. We argue that the effectiveness of the models could further be enhanced if we use multi-channel CNN models even for under-resourced languages that have limited training data size. This is because the single-channel CNN might fail to consider the potential effect of multiple channels to generate better features, which is not well investigated for hate speech detection. Therefore, in this work, we explore the use of multi-channel CNN to extract better features from different channels in an end-to-end manner on top of a word2vec embedding layer. Tested on a new small-scale Amharic hate speech dataset containing 2000 annotated social media comments, the experimental results show that the proposed multi-channel CNN model outperforms the single-channel CNN models but underperform from the baseline Support Vector Machine (SVM) with an average F-score of 81.3%, 78.2%, and 92.5% respectively. The finding of the study implies that the proposed MC-CNN model can be used as an alternative solution for hate speech detection using a deep learning approach when dataset scarcity is an issue. © 2022, ICST Institute for computer sciences, Social Informatics and Telecommunications engineering.

关键词： Embeddings

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Factors Influencing Romanian Teachers' Choice of IT Training to Avoid Issues Raised by Online Education: A Data Mining Approach

Factors Influencing Romanian Teachers' Choice of IT Training...

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International Conference on Development and Application Systems

作者： Corina Simionescu Mirela Danubianu Adina-Luminița Bărîlă Bogdănel Constantin Grădinaru Doctoral School of Applied Sciences and Engineering Computers and Information Technology Ștefan Cel Mare University Suceava Romania Faculty of Electrical Engineering and Computer Science Ștefan Cel Mare University Suceava Romania

ISBN: (数字)9798350349290

ISBN: (纸本)9798350349306

Raising the students' academic performance and improving the overall quality of education is an important goal of our society. With the exploding development of information technologies, Educational Data Mining has become a growing activity that uses the essence of Data Mining concepts to help institutions learn useful information on various academic issues. Romanian schools are facing several problems and inequalities, many of which were accentuated during the Covid-19 pandemic, when, due to restrictions on physical proximity, classes were held entirely or partly online. This paper aims to presents an experimental study on modelling teachers' attitudes towards attending IT professional development courses to deliver quality online lessons. A real data set collected through a questionnaire answered by 956 teachers is used. The final goal was that, after the best-performing model was built, the features that are significant in this approach would be analyzed. These features are influential factors in teachers' choices. We initially considered four classification techniques, after which, we refined the optimization process for Random Forest, which proved, from the first experiments, the best performance.

关键词： Training Technological innovation Law Government Collaboration Turning Data mining

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Glucose Sensing Utilizing Complex-Valued Neural Networks 23

Glucose Sensing Utilizing Complex-Valued Neural Networks

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4th International Conference on Computing, Networks and Internet of Things, CNIOT 2023

作者： Lv, Yiqi Meng, Xiang Luo, Yong Pei, Yan The Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai University China and Jt. International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai University Shanghai China Doctoral Program Grad. Sch. of Comp. Science and Engineering Graduate Department of Computer and Information Systems University of Aizu Aizuwakamatsu Japan Computer Science Division University of Aizu Aizuwakamatsu Japan

ISBN: (纸本)9798400700705

This paper proposes a Complex-Valued Neural Network (CVNN) for glucose sensing in milli-meter wave (mmWave). Based on the propagation characteristics of millimeter wave in glucose medium, we obtain the S21 parameter of glucose with the concentration range of 0-300mg/dL in the 60-80 GHz frequency band by High Frequency Structure Simulator (HFSS) simulations. Then we combine the sensing model with a neural network to detect and predict the glucose concentration relying on the learning ability of the neural network. In the prediction of the concentration of unknown samples, the absolute error between the predicted value and the true value is within 5mg/dL, which confirms the ability of the proposed CVNN model. © 2023 ACM.

关键词： Millimeter waves

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Hybrid Competitive Swarm Optimization Algorithm Based Scheduling in the Cloud Computing Environment 5

Hybrid Competitive Swarm Optimization Algorithm Based Schedu...

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5th International Conference on Inventive Research in Computing Applications, ICIRCA 2023

作者： Shrichandran, G.V. Narayana Tinnaluri, V.S. Senthil Murugan, J. Meeradevi, T. Dwivedi, Vijay Kumar Suma Christal Mary, S. B.Sc -CS Programme Srm Institute of Science and Technology Department of Computer Science & Applications Ramapuram Chennai89 India Koneru Lakshmaiah Education Foundation Department of Computer Science Vaddeswaram522025 India Vel Tech High Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi Chennai India Kongu Engineering College Department of Ece Perundurai Erode India V.E.C. Ambikapur Dist Surguja497001 India Panimalar Engineering College Poonamalle Department of Information Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350321425

Cloud computing (CC) platform not only offers resource sharing for users and also provide many on-demand services. Business procedures can be managed to utilize the workflow technology on the cloud which signifies most of the problems in employing the resources in an effectual approach because of the dependencies between the tasks. Task scheduling (TS) in a CC platform applies to the procedure of effectively allocating resources computing to tasks or jobs submitted to the CC environment. In a cloud platform, tasks are separated as smaller subtasks that are applied in parallel on distinct machines. There are many features to consider if the TS is in CC platform, comprising the kind of tasks like resources cost, resources required, and resources availability. Therefore, this study presents a Hybrid Competitive Swarm Optimization Algorithm based Task Scheduling (HCSOA-TS) technique in the CC platform. The presented HCSOA-TS technique schedules the tasks proficiently in such a way that maximum resource utilization and performance gets accomplished. In the design of HCSOA, Cauchy mutation operator is included in the CSO algorithm and thereby improves the overall performance. In addition, the derivation of the fitness function by the HCSOA-TS technique undergoes optimal scheduling process and decreases energy usage. The experimental result analysis of the HCSOA-TS technique is tested using a series of measures. The comprehensive comparison study highlighted the improvements of the HCSOA-TS technique over recent approaches. © 2023 IEEE.

关键词： Cloud computing

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