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11th International Conference on System Modeling and Advancement in research Trends, SMART 2022

作者： Chouhan, Robin Singh Rajawat, Anand Singh Goyal, S.B. Bedi, Pradeep Constantin, Neagu Bogdan Raboaca, Maria Simona Verma, Chaman Shri Vaishnav Institute of Information Technology Shri Vaishnav Vidyapeeth Vishwavidyalaya Indore India School of Computer Science and Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Galgotais University Greater Noida India Gheorghe Asachi Technical University of Iasi Department of Power Engineering Iasi Romania National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania University of Eötvös Loránd Faculty of Informatics Budapest1053 Hungary

ISBN: (纸本)9781665487320

With the introduction of industry 4.0, the role of robots especially autonomous bots has increased exponentially. The next thing in order to improve the navigation for these bots is the need for a proper indoor navigation system. The paper proposes experimental analysis for estimating the position using trilateration. The experiment includes three wifi nodes operating at 2.4GHz working as wifi beacons. The paper provides an efficient and cost-effective solution to estimate the approximate position of the node in terms of coordinates. The paper also includes a working mobile application to showcase the node's position in real-time in the form of a 2d map. © 2022 IEEE.

关键词： Indoor positioning systems

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Vehicle Re-Identication Model Based on Optimized DenseNet121 with Joint Loss

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computers, Materials & Continua 2021年第6期67卷 3933-3948页

作者： Xiaorui Zhang Xuan Chen Wei Sun Xiaozheng He Jiangsu Engineering Center of Network Monitoring Engineering Research Center of Digital ForensicsMinistry of EducationSchool of Computer and SoftwareNanjing University of Information Science&TechnologyNanjing210044China Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science&TechnologyNanjing210044China Department of Civil and Environmental Engineering Rensselaer Polytechnic InstituteTroyNY12180USA

With the increasing application of surveillance cameras,vehicle re-identication(Re-ID)has attracted more attention in the eld of public *** Re-ID meets challenge attributable to the large intra-class differences caused by different views of vehicles in the traveling process and obvious inter-class similarities caused by similar *** existing methods focus on local attributes by marking local ***,these methods require additional annotations,resulting in complex algorithms and insufferable computation *** cope with these challenges,this paper proposes a vehicle Re-ID model based on optimized DenseNet121 with joint *** model applies the SE block to automatically obtain the importance of each channel feature and assign the corresponding weight to it,then features are transferred to the deep layer by adjusting the corresponding weights,which reduces the transmission of redundant information in the process of feature reuse in *** the same time,the proposed model leverages the complementary expression advantages of middle features of the CNN to enhance the feature expression ***,a joint loss with focal loss and triplet loss is proposed in vehicle Re-ID to enhance the model’s ability to discriminate difcult-to-separate samples by enlarging the weight of the difcult-to-separate samples during the training *** results on the VeRi-776 dataset show that mAP and Rank-1 reach 75.5%and 94.8%,***,Rank-1 on small,medium and large sub-datasets of Vehicle ID dataset reach 81.3%,78.9%,and 76.5%,respectively,which surpasses most existing vehicle Re-ID methods.

关键词： Vehicle re-identication densenet joint loss focal loss SE block

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Regression models for predicting the effect of trash rack on flow properties at power intakes

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engineering Applications of Computational Fluid Mechanics 2024年第1期18卷

作者： Li, Shuguang Qasem, Sultan Noman Karami, Hojat Salwana, Ely Rezaei, Alireza Shahmirzadi, Danyal Band, Shahab S. School of Computer Science and Technology Shandong Technology and Business University Yantai China Computer Science Department Imam Muhammad Ibn Saud Islamic University Riyadh Saudi Arabia Computer Science Department Faculty of Applied Science Taiz University Taiz Yemen Civil Engineering Department Semnan University Semnan Iran Institute of Visual Informatics Universiti Kebangsaan Malaysia Selangor Malaysia Graduate School of Engineering Science and Technology National Yunlin University of Science and Technology Douliou Taiwan International Graduate School of AI National Yunlin University of Science and Technology Yunlin Taiwan

Vortex flow characteristics in a reservoir and horizontal water intake have been predicted by using regression models in this numerical research. In this paper, three standalone machine learning models–Random Forest (RF), K-nearest neighbours (KNN), Gradient Boosting (GB)–and a proposed hybrid model based on Lévy Jaya Algorithm (LJA) and GB (LJA-GB) are employed to estimate the effect of trash racks on flow properties at power intakes. The experimental data which are prepared for the proposed study in this paper were obtained through a rectangular laboratory tank 8.3 m³ with various submergence depths and Froude numbers on nine trash racks with 63.7%–84.1% opening, made out of 2, 2.5, 4, and 6 mm thick copper wire. The outcomes revealed that the proposed LJA-GB model shows the best overall performance among the four models used for estimation. Thus, the LJA-GB model has the lowest mean absolute error (MAE) (0.3344), mean squared error (MSE) (0.1784), and root mean squared error (RMSE) (0.4223) values and highest R-squared ((Formula presented.)) (0.9899) and Willmott’s index (WI) values (0.9508) in the testing stage metrics for (Formula presented.) estimation and MAE (0.0061), MSE (0.0001), RMSE (0.0073), (Formula presented.) (0.9971), WI (0.9727) for (Formula presented.) estimation. Whereas the RF and KNN models exhibited poor performance in both stages of estimation. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

关键词： experimental modelling Hydro power plant regression models vortex strength

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Improving State of Charge Estimation for Lithium-Ion Batteries through Optimized CNN Models

Improving State of Charge Estimation for Lithium-Ion Batteri...

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Mobile Computing and Sustainable informatics (ICMCSI), International Conference on

作者： Indradeep Kumar Madhavi Dasari Chaitanya Danamaraju Bindu K V V. Mohanavel Joshuva Arockia Dhanraj Department of Aeronautical Engineering Institute of Aeronautical Engineering Dundigal Hyderabad India Department of Computer Science Sapthagiri College of Engineering Bengaluru Karnataka India Department of Electrical and Electronics Engineering RNS Institute of Technology Bengaluru Karnataka India Department of Science And Humanities( General Engineering) R.M.K. College of Engineering and Technology Thiruvallur Tamil Nadu India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Chennai Tamil Nadu India University Centre for Research & Development (UCRD) Chandigarh University Mohali Punjab India

Lithium-ion (Li-ion) batteries are the preferred choice for electric vehicles (EVs) because of their extended lifespans, low self-discharge rates, high voltage, and high energy density. A well-functioning Battery Management System (BMS) is critical to the efficient operation of an EV. The State of Charge (SoC) is an important statistic that reflects the remaining charge in the battery, and its exact assessment is essential for BMS and improving EV efficiency, which extends the battery's life and decreases the probability of catastrophic failure. However, SOC estimation is complicated and affected by numerous unknowns, such as battery age and external temperature. In this study, we estimated SOC using a Convolutional Neural Network (CNN) model. To improve the CNN architecture, this study has applied three different optimization algorithms: Particle Swarm Optimization (PSO), Elephant Search Algorithm (ESA), and Equilibrium Optimization (EO). Sensor data from lithium-ion batteries were carefully processed. The processed dataset was then supplied to the CNN and three optimized CNN models. These models were tested using error, R2, and time metrics to identify the optimal technique. CNN-ESA outperformed the other CNN models in SOC estimation, with the lowest error rates and the highest R2 value of 0.9987. This simulation result demonstrates the effectiveness of applying ESA to improve CNN architectures for better Li-ion battery SOC estimates. It enhances the efficiency and lifespan of EVs.

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Multimodal Feature Extraction for Memes Sentiment Classification 2

Multimodal Feature Extraction for Memes Sentiment Classifica...

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2nd IEEE Conference on Information technology and Data Science, CITDS 2022

作者： Ouaari, Sofiane Tashu, Tsegaye Misikir Horvath, Tomas Elte - Eötvös Loránd University Faculty of Informatics Department of Data Science and Engineering Pázmány Péter sétány 1/C Budapest1117 Hungary Institute of Computer Science Faculty of Science Pavol Jozef Šafárik University Jesenná 5 Košice040 01 Slovakia College of Informatics Kombolcha Institute of Technology Wollo University Kombolcha208 Ethiopia

ISBN: (数字)9781665496537

ISBN: (纸本)9781665496537

In this study, we propose feature extraction for multimodal meme classification using Deep Learning approaches. A meme is usually a photo or video with text shared by the young generation on social media platforms that expresses a culturally relevant idea. Since they are an efficient way to express emotions and feelings, a good classifier that can classify the sentiment behind the meme is important. To make the learning process more efficient, reduce the likelihood of overfitting, and improve the generalizability of the model, one needs a good approach for joint feature extraction from all modalities. In this work, we proposed to use different multimodal neural network approaches for multimodal feature extraction and use the extracted features to train a classifier to identify the sentiment in a meme. © 2022 IEEE.

关键词： computer vision

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Experimental analysis of gravity and buoyancy powered energy generation storage systems 1st

Experimental analysis of gravity and buoyancy powered energy...

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1st International Conference on Frontiers in Smart Systems Technologies, ICFSST 2019

作者： Kashem, Saad Bin Abul Ngambi, David Tionge Ahmed, Jubaer Qidwai, Uvais Suresh, P. Computer Skills Faculty Qatar Armed Forces—Academic Bridge ProgramQatar Foundation Doha Qatar Department of Mechanical Engineering Swinburne University of Technology Sarawak Malaysia School of Engineering Computing and Sciences Swinburne University of Technology Sarawak Sarawak Malaysia Department of Computer Science and Engineering Qatar University Doha Qatar Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789811550287

The concept of harnessing energy from buoyancy as well as the ability to have underwater energy storage is an area of research that, compared to other renewable energy generation techniques, is relatively unexplored. This study presents an experimental analysis of a buoyancy generation and storage system. Tests were performed under standard laboratory conditions with the primary fluid being chlorinated municipal tap water. The buoyancy systems were designed, fabricated, and tested, with a focus on scalability and the need for results extrapolation. The system charge cycle was analyzed based on the system discharge voltage. Energy losses in the system due to drag or mechanical loss were estimated. The power generation and storage system utilized air transfer between containers to account for buoyancy increase and decrease in the main air vessel. Both systems were designed to function as standalone systems. Key variables of the experiment were the system float and the charge depth. Conclusions are based on experimental observation and compared with expected results from theoretical analysis, with a focus on performance improvement and real word practicality. © Springer Nature Singapore Pte Ltd 2021.

关键词： Wireless sensor networks

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An improved deep convolutionary neural network for bone marrow cancer detection using image processing

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informatics in Medicine Unlocked 2023年 38卷

作者： Ramasamy, Manjula Devi Dhanaraj, Rajesh Kumar Pani, Subhendu Kumar Das, Rashmi Prava Movassagh, Ali Akbar Gheisari, Mehdi Liu, Yang Porkar, P. Banu, Sabitha Department of Computer Science and Engineering KPR Institute of Engineering and Technology India School of Computing Science and Engineering Galgotias University India Krupajal Engineering College Biju Patnaik University of Technology Odisha India Department of Computer Science and Engineering CV Raman Global University Bhubaneswar India Department of Biomedical Engineering School of Medicine Tehran University of medical sciences Tehran Iran Department of Computer Engineering Science and Research Branch Islamic Azad University Tehran Iran School of Computer Science and Technology Harbin Institute of Technology Shenzhen China Guangdong Provincial Key Laboratory of Novel Security Intelligence Technologies China Department of Cognitive Computing Institute of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Young researchers and elite club Islamic Azad University Parand Iran

Bone Marrow Cancer is a type of cancer that develops in the stem cells of the bone marrow that are responsible for blood formation. AML(Acute Myeloid Leukaemia) and MM(Multiple Myeloma) are both types of malignancy that can affect bone marrow. Conventionally, the process was carried out manually by a skilled professional in a considerable amount of time. Hence, in order to provide accurate treatment, a better diagnosis technique is required. In this regard, the suggested study has improved a unique technique, specifically classification and segmentation, allowing for the identification of more intricate disorders. The study's findings on the accuracy of cancer cell identification, as well as the decrease in the possibility of false alarm rates, continue to fall on the negative side of the outcome spectrum, according to the researchers. The hybrid approach has been proposed in this article that includes deep convolutional neural networks, hyperparameter sets utilising adaptive multi-objective CAT algorithms and other image processing techniques. To train the suggested model, it is critical to use previously processed cell images. The suggested model is then used to train an Optimized Convolutional Neural Network (OCNN), which is then used to determine the type of tumour identified in the bone area. The SN-AM datasets were thoroughly examined, and a number of presentation measures, such as accuracy (recall), F1-score, and specificity (specificity), were generated and analysed. When it came to predicting different types of cancer, researchers observed that they had an overall accuracy of 99.45 percent. In terms of recognizing the sorts of cancer cells, the proposed model surpassed all existing learning models. © 2023

关键词： Bone marrow CAT Algorithm Leukaemia Multiple myeloma Optimized convolutional neural network

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Leveraging Open Source Large Language Models to generate datasets from existing field-specific texts

Leveraging Open Source Large Language Models to generate dat...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Claudiu Trăistaru Florin Pop Costin Bădică Daniel Ciochiu Mircea Bădoi Gabriel-Cătălin Nedianu University Politehnica of Bucharest University of Craiova Craiova Romania National Institute of Research and Development in Informatics (ICI) Academy of Romanian Scientists University Politehnica of Bucharest Bucharest Romania Computer and Information Technology Department University of Craiova Craiova Romania Software Development Department NetDania SRL Craiova Romania

ISBN: (数字)9798350368130

ISBN: (纸本)9798350368147

Network engineers are essential to the management of computer systems, as they configure services and devices on the network to guarantee effective data routing and improve computer networks which make easier to perform everyday tasks like sending and receiving emails as well as more sophisticated ones like cloud computing and online gaming. In addition, modern networks have to serve a rising number of IoT devices without compromising performance and handle more sophisticated cybersecurity threats. Network engineers must always be learning and adapting to new protocols and technologies if they are to successfully tackle these difficulties. Moreover, the introduction of Large Language Models (LLMs) that are available as open-source software has revolutionized technical innovation by enabling the automation of network setups and augmenting the capabilities of network administration. These developments represent an important step forward in the field of network engineering, with the goal of maximizing efficiency and guaranteeing strong network security and functioning.

关键词： Performance evaluation Technological innovation Protocols Large language models Network security Routing Internet of Things

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Deep Learning Approaches for Solving Challenges in computer Vision Applications and Exploring Evolving Patterns

Deep Learning Approaches for Solving Challenges in Computer ...

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International Conference on Contemporary Computing and informatics (IC3I)

作者： Sujay Gejji B. R. Supreeth Pathan Firoze Khan S. Venkat K Pradeepa S. Kaliappan Department of Electronics & Communication Engineering S. G. Balekundri Institute of Technology Belagavi Karnataka India Department of Computer Science Oxford Degree and PG College Sir. M. Visveshwaraiah Layout Bangalore Department of Computer Science & Technology Madanapalle institute of Technology & Science Madanapalle Andhra Pradesh India Department of Electronics and communication Engineering S. A. Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science KPR College of Arts Science and Research Coimbatore Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

Deep learning has brought substantial advancements to computer vision in recent years by providing answers to challenging visual interpretation problems. It investigates several DL methodologies, such as self-supervised learning strategies, hybrid CNN-RNN models, enhanced CNNs, generative adversarial networks (GANs), and vision transformers (ViTs). Novel CNN architectures are designed, GANs are used for image synthesis, hybrid models are used to integrate spatial and temporal information, self-attention is used in ViTs, and self-supervised learning is used for feature extraction. The results of the study show significant improvements: the accuracy of Enhanced CNNs was $81.6 \%$, the accuracy of GANs was $14.8 \%$, the accuracy of Hybrid CNN-RNN models was $86.2 \%$, the accuracy of ViTs was $79.2 \%$, and the accuracy of transfer for self-supervised learning techniques was $76.8 \%$. These findings demonstrate the improved effectiveness and performance of the suggested techniques, highlighting their potential to resolve challenging problems in feature extraction, video analysis, object recognition, picture enhancement, and image classification. It concludes that advances in computer vision will be facilitated by the ongoing development of DL methods. These advancements will lead to more precise and adaptable applications in a range of fields, including autonomous driving, medical imaging, surveillance, and augmented reality.

关键词： computer vision Visualization Accuracy Computational modeling Surveillance Self-supervised learning Transformers Generative adversarial networks Object recognition Image classification

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Image Captioning with Reinforcement Learning

Image Captioning with Reinforcement Learning

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Anand Verma Saurabh Agarwal K.V. Arya Ivan Petrlik Roberto Esparza Ciro Rodriguez Department of Information Technology ABV-IIITM Gwalior Madhya Pradesh India Department of Computer Science and Engineering Multimedia and Information Security Research Group ABV-Indian Institute of Information Technology and Management Gwalior India Faculty of Industrial and Systems Engineering National University Federico Villarreal Lima Peru Faculty of Systems Engineering and Informatics National University Mayor de San Marcos

Image captioning involves generating a natural language description that accurately represents the content and context of an image. To achieve this, image captioning utilises various machine learning techniques and fields, such as computer vision and natural language processing. In the field of image captioning, a lot of advances have been made with encoder-decoder models and reinforcement learning algorithms. However, there are still problems of imbalance between testing and training, as reinforcement learning only handles single comparator metrics such as CIDEr, SPICE, and BLEU and could not perform better in multiple metrics at once. Which is why a lack of diversity can be seen in generated captions. This idea proposes a general technique for collaborative updating that can bridge the gap between evaluation measures and test metrics to produce captions that are more human-like. To increase the precision of image captions, the approach involves using a compiled reward system that considers multiple evaluation metrics to compare the generated sentence with the provided sentences. We will evaluate the model's performance and the reward updating process on standard datasets like MS COCO.

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