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5th International Conference on Contemporary Computing and Informatics, IC3I 2022

作者： Shashidhar, R. Tippannavar, Sanjay S Bhat, Krishnakumar Balachandra Sharma, Neha Rashid, Mamoon Rana, Arti JSS Science and Technology University Department of Electronics and Communication Engineering Mysuru India Chitkara University Institute of Engineering and Technology Department of Computer Science & Engineering Chitkara University Punjab Rajpura India Vishwakarma University Faculty of Science & Technology Department of Computer Engineering Pune India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun India

ISBN: (纸本)9798350398267

The human brain is made up of millions of neurons, each of which plays a crucial part in directing the behaviour of the human body in response to internal/external motor-impulses. These neurons will act as data conduits between the brain and the body. Studying brain signals or images may help us better understand the cognitive behaviour of the brain. Motor and sensory states including eye movement, lip movement, memory, concentration, hand clutching, and others may be used to depict human behaviour. These states are associated with certain signal frequencies, which aids in understanding the functional behaviour of complex brain structures. In this study, the characterization of EEG signals in connection to various bodily states is the main topic of study. It also talks about the experimental design used for the EEG analysis. On a dataset of physiological signals, in this case the AMIGOS dataset, including electrocardiogram and galvanic skin reaction, this study uses a deep convolutional neural network. In order to characterize a person's emotional state, these physiological signals are combined with the dataset's arousal and valence data. In addition, a tool for detecting emotions based on traditional machine learning techniques is provided in order to extract the properties of physiological data in the time, frequency, and nonlinearity domains. For autonomous feature extraction of physiological inputs in this application, a convolutional neural network is used. Fully connected network layers are then used to predict emotions. Compared to the original authors of this dataset, the experimental results on the AMIGOS dataset show that the approach suggested in this study provides more accurate emotional state categorization. © 2022 IEEE.

关键词： Convolution

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Image Denoising for Smart Laser Osteotomy Using Deep Learning-based Fast Optical Coherence Tomography (OCT) 2

Image Denoising for Smart Laser Osteotomy Using Deep Learnin...

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2nd International Conference on Electronics and Renewable systems, ICEARS 2023

作者： Ghate, Shubhangi N. Kalpanadevi, D. Amudha, K. Velayutham, Priya Balu, S. Shah, Mohd Asif D. Y. Patil Deemed to Be University Ramrao Adik Institute of Technology Department of Electronics and Telecommunication Engineering Maharashtra Navi Mumbai Nerul India Kalasalingam Academy of Research and Education Department of Computer Applications Krishnan Koil Tamil Nadu Srivilliputhur India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Thottiyam Tamilnadu Trichy India Paavai Engineering College Department of Computer Science and Engineering Tamilnadu Namakkal India Vivekanandha College of Engineering for Women Department of Computer Science and Engineering Tiruchengode Tamilnadu Namakkal India Chandigarh University Adjunct Faculty University School of Business Gharuan Punjab Mohali India

ISBN: (纸本)9798350346640

With laser osteotomy, patients may expect clean incisions and minimal damage to bone structure. With the smart laser osteotomy method, this study has suggested using Optical Coherence Tomography (OCT) to track the ablation as it occurred. The OCT image is useful for distinguishing different types of tissue and giving the ablation laser guidance on where to focus its energy to avoid damaging vital structures like bone marrow and nerves. Furthermore, the application relies on the quality of the OCT picture to determine the tissue classifier's exactness. Because of this, establishing a reliable feedback system depends heavily on picture denoising. The frame averaging approach is widely utilized as a means of picture denoising in Optical Coherence Tomography (OCT). The more photographs that are used, the better the final image will seem. However, this method requires more time to acquire data and makes the system more vulnerable to motion abnormalities. To get around these restrictions, a deep learning denoising algorithm is used to simulate the frame-averaging approach. The generated image was of higher quality than traditional digital filtering techniques while maintaining the same level of detail as when using a frame-averaging approach. This study also analyzes how the proposed strategy impacts the precision of the tissue classifier model to inform the ablation laser's adjustments. After applying image denoising, a significant improvement has been observed in the tissue classifier's performance. It is also evident that the classifier trained with deep learning denoised images was as accurate as the classifier trained with frame-averaged images. © 2023 IEEE.

关键词： Optical tomography

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ANFIS Grey Wolf Optimized And Secured Path Selection Algorithm For VANET Applications

ANFIS Grey Wolf Optimized And Secured Path Selection Algorit...

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2023 International Conference on Energy, Materials and communication engineering, ICEMCE 2023

作者： Sivaprakash, P. Nisha, K.S. Jagadish Vijay, R. Madhivadhani, D. Maheswari, E. Sasikumar, M. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Computer Science and Engineering Tamilnadu India Amrita College of Engineering and Technology Department of Electrical and Electronics Engineering Tamil Nadu Nagercoil India Kings Engineering College Faculty of Electronics and Communication Engineering Chennai India Sri Sai Ram Institute of Technology Department of Electrical and Electronics Engineering Chennai India C. Abdul Hakeem College of Engineering & Technology Department of Electrical and Electronics Engineering Tamil Nadu India

ISBN: (纸本)9798350393378

Vehicle ad hoc networks (VANETs) present a unique mix of challenges and opportunities for routing protocols because to the semi-organized organization of means of transportation that faces limits of road structure and laws, as well as obstructions that limit physical access in urban environments. The dependability and scalability of routing protocols in big metropolitan VANETs are actively being explored. Furthermore, ANFIS-GWO Optimization approaches are used to increase routing sustainability and availability in VANETs by clustering vehicles beside one other based on connection locations and relative velocity, resulting in the widespread building of hierarchical network topologies. The transmission range demonstrates that the ANFIS-GWO (Adaptive Neuro-Fuzzy Inference system- Grey Wolf Optimization) is significantly more efficient than the ANFIS and GWO. End to End delay, Energy Consumption, Packet Loss and Throughput evaluation is contrasted with conventional approaches like FUZZY, ANN and ANFIS GWO Respectively. Throughput evaluation is contrasted with conventional approach ANFIS GWO is 650kbps. © 2023 IEEE.

关键词： Routing protocols

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A Squeeze-Excitation ResNet Approach for Effective Classification of Parasitic Eggs

A Squeeze-Excitation ResNet Approach for Effective Classific...

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2024 International Visualization, Informatics and Technology Conference, IVIT 2024

作者： Muthulakshm, M. Venkatesan, K. Rahayu, Syarifah Bahiyah Elangovan, Karthickeien Amrita School of Engineering Amrita Vishwa Vidyapeetham Dept. of Electronics and Communication Engineering Chennai India Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India National Defence University Malaysia Faculty of Defense Science and Technology Kuala Lumpur Malaysia National Defence University Malaysia Cyber Security and Digital Industrial Revolution Centre Kuala Lumpur Malaysia

ISBN: (纸本)9798350354447

The identification and classification of different kinds of parasite eggs in microscopic samples represent a critical challenge in the field of Soil-transmitted helminth infection diagnosis. Traditional methods are often labor-intensive and time-consuming. The emergence of deep learning models has shown promising results in automating this process by extracting intricate features from complex images. This study aims to develop an automated system for accurately classifying parasite egg types in microscopic images by leveraging the ability of squeeze excitation layers to learn the global information from the input. The proposed system employs features extracted by ResNet50 and ResNet101 with Squeeze Excitation (SE) layers for analysis. The extracted features are then input into a Support Vector Classifier. The study systematically evaluates the features extracted from ResNet50+SE and ResNet101+SE. Results from the evaluation demonstrate the efficacy of the ResNet50+SE in accurately classifying parasite egg types in microscopic images with an accuracy of 0.94. The study provides valuable insights into the choice of squeeze-excitation block added Resnet in the context of parasitology, contributing to the advancement of automated medical image analysis. The findings hold great potential for improving diagnostic processes and supporting epidemiological studies through efficient and accurate parasite detection. © 2024 IEEE.

关键词： Deep learning

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Design and Implementation of a QoL Improvement system for Visually Impaired Patients 5

Design and Implementation of a QoL Improvement System for Vi...

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5th International Conference on Advances in Computational Tools for engineering Applications, ACTEA 2023

作者： Abi Zeid Daou, Roy Beaino, Jad Hawat, Johnny Ibrahim, Ramy Hayek, Ali Boercsoek, Josef Olmedo, Jose Javier Serrano Université la Sagesse Faculty of Engineering Mechatronics Department Beirut Lebanon MART Learning Education and Research Center Chananir Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Hamm59063 Germany University of Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Germany Centro de Tecnología Biomédica Universidad Politécnica de Madrid Lab of Bioinstrumentation and Nanomedicine Spain

ISBN: (纸本)9798350309959

Visually impaired people encounter several challenges in their mobility and navigation. Their daily activities are obstructed due to their inability to adapt or recognize accurately their surroundings, especially outside their house which they are more familiar with. Thus, it becomes the main reason of accidents, falling off, getting lost in unknown areas, etc. Furthermore, one of the sensory systems that helps the body to process data about the external environment is the visual system. Blind people also lose touch with the outside world, develops poor motor habits, which results in postural problems as a result. This project will assist visually impaired people in their daily life and simplify normal tasks through a system combining two previously designed projects, 'Smart Shoes for Blind and Visually Impaired People', and 'Human posture monitoring device'. The multifunctional system is developed with the goal of securing safe movements for visually impaired people as well as maintaining a good back posture by detecting leaning postures (LP). The purpose of the smart shoe is to identify obstacles and protect the user from unintended accidents. A compatible Android application has been developed to alert the user when there is an obstruction or when he is walking on a wet surface. Voice alarms will be used to acoustically alert the user. If the user collapses, a message with their position will be sent right away to a relative. On the other hand, the smart vest will identify the position of the user's back and alert him to maintain a straight posture through the same application as well. As the system is dealing with human health, some safety measurements would be taken into consideration to implement a safe electrical system in order to reduce error and to increase accuracy. © 2023 IEEE.

关键词： Accidents

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Leveraging Artificial Intelligence in Online Educational Platforms to Enhance Continuous Professional Development in the Teaching Profession 2

Leveraging Artificial Intelligence in Online Educational Pla...

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2nd International Conference Computational and Characterization Techniques in engineering and sciences, IC3TES 2024

作者： Amankulovich, Khujakulov Sherzod Tokhirovich, Mannabov Javlon Bakhridinovich, Kadirov Tulkin Kuvandikovich, Taylakov Umid Ananth, Christo Kumar, T. Ananth Samarkand Regional National Center for Training Teachers in New Methods Uzbekistan Uzbekistan Samarkand Agroinnovations and Research University Uzbekistan Samarkand State University Uzbekistan&Adjunct Faculty Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Avadi Chennai India Ifet College of Engineering Computer Science and Engineering Tamilnadu India

ISBN: (纸本)9798350364699

To keep up with the dynamic nature of the modern classroom, teachers must have access to online learning platforms for professional development purposes. This investigation looks at how these platforms can be used to improve teachers' skills, make information sharing easier, and open doors to CPD opportunities. This study examines the relationship between online resources and methods to demonstrate how digital tools have an impact on teacher training and professional development by leveraging AI. It also draws attention to the fact that these platforms are helping with information-methodological support for teachers as well as the ongoing reforms in our nation. Incorporating empirical observations and case studies, the analysis demonstrates how effective online platforms are in fostering a collaborative and innovative environment for professional learning. Digital transformation has the potential to radically alter how teachers obtain professional development, and this study emphasizes that fact. This will enable teachers to effectively address the demands of modern classrooms. © 2024 IEEE.

关键词： Contrastive Learning

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A deep learning fusion model for accurate classification of brain tumours in Magnetic Resonance images

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CAAI Transactions on Intelligence Technology 2024年第4期9卷 790-804页

作者： Nechirvan Asaad Zebari Chira Nadheef Mohammed Dilovan Asaad Zebari Mazin Abed Mohammed Diyar Qader Zeebaree Haydar Abdulameer Marhoon Karrar Hameed Abdulkareem Seifedine Kadry Wattana Viriyasitavat Jan Nedoma Radek Martinek Department of Information Technology Lebanese French UniversityErbilIraq Department of Computer Science University of ZakhoZakhoKurdistan RegionIraq Department of Computer Science College of ScienceNawroz UniversityDuhokKurdistan RegionIraq Department of Artificial Intelligence College of Computer Science and Information TechnologyUniversity of AnbarRamadiIraq Department of Telecommunications VSB‐Technical University of OstravaOstravaCzech Republic Department of Cybernetics and Biomedical Engineering VSB‐Technical University of OstravaOstravaCzech Republic Department of Computer Network and Information Security Technical College of Informatics-AkreDuhok Polytechnic UniversityDuhokIraq Information and Communication Technology Research Group Scientific Research CenterAl‐Ayen UniversityThi‐QarIraq College of Computer Sciences and Information Technology University of KerbalaKarbalaIraq College of Agriculture Al‐Muthanna UniversitySamawahIraq Department of Applied Data Science Noroff University CollegeKristiansandNorway Faculty of Commerce and Accountancy Chulalongkorn Business SchoolChulalongkorn UniversityBangkokThailand

Detecting brain tumours is complex due to the natural variation in their location, shape, and intensity in images. While having accurate detection and segmentation of brain tumours would be beneficial, current methods still need to solve this problem despite the numerous available approaches. Precise analysis of Magnetic Resonance Imaging (MRI) is crucial for detecting, segmenting, and classifying brain tumours in medical diagnostics. Magnetic Resonance Imaging is a vital component in medical diagnosis, and it requires precise, efficient, careful, efficient, and reliable image analysis techniques. The authors developed a Deep Learning (DL) fusion model to classify brain tumours reliably. Deep Learning models require large amounts of training data to achieve good results, so the researchers utilised data augmentation techniques to increase the dataset size for training models. VGG16, ResNet50, and convolutional deep belief networks networks extracted deep features from MRI images. Softmax was used as the classifier, and the training set was supplemented with intentionally created MRI images of brain tumours in addition to the genuine ones. The features of two DL models were combined in the proposed model to generate a fusion model, which significantly increased classification accuracy. An openly accessible dataset from the internet was used to test the model's performance, and the experimental results showed that the proposed fusion model achieved a classification accuracy of 98.98%. Finally, the results were compared with existing methods, and the proposed model outperformed them significantly.

关键词： brain tumour deep learning feature fusion model MRI images multi‐classification

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Improving Accuracy and Speed of Deep Learning Algorithms for Real-Time Data Analysis in Machine Learning

Improving Accuracy and Speed of Deep Learning Algorithms for...

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2023 IEEE International Conference on Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario, ICPSITIAGS 2023

作者： Inamdar, Fatima M. Singar, Mahendra Kumar Naval, Preeti Pattanaik, Ashutosh Santhoshkumar, M.P. Maheswaran, S. Vishwakarma Institute of Information Technology Department of Information Technology Pune India Vivekananda Global University Department of Civil Engineering Jaipur India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Faculty of Engineering and Technology Department of Mechanical Engineering Karnataka 562112 India Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering Chennai127 India Kongu Engineering College Department of Electronics and Communication Engineering Tamil Nadu Erode India

ISBN: (纸本)9798350329773

This have a look at examines using deep learning algorithms for stepped forward accuracy and velocity in real-time facts evaluation in gadget studying. The goal is to offer a comprehensive assessment of recently developed optimization strategies for deep gaining knowledge of algorithms, as well as techniques to enhance their accuracy and overall performance. Additionally, the paper evaluates the potential of deep learning algorithms for actual-time information analysis, in phrases of their accuracy, pace, reliability, and scalability. In the end, the paper offers a survey of to be had datasets for deep learning, and gives hints for in addition studies. The results of this examine suggest that deep mastering algorithms can be used to enhance accuracy and speed of system getting to know for actual-time data evaluation, and that further studies must attention on exploring the ability of optimization techniques for accomplishing higher accuracy and pace. © 2023 IEEE.

关键词： Learning algorithms

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Analysis and Assessment of Wind Energy Potential of Al-Hodeidah in Yemen

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computers, Materials & Continua 2021年第11期69卷 1995-2011页

作者： Fahd N.Al-Wesabi Murad A.Almekhlafi Mohammed Abdullah Al-Hagery Mohammad Alamgeer Khalid Mahmood Majdy M.Eltahir Ali M.Al-Sharafi Amin M.El-Kustaban Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science College of ComputerQassim UniversitySaudi Arabia Department of Information Systems King Khalid UniversityMayahel AseerKSA Department of Computer Science College of Computers and Information TechnologyUniversity of BishaKSA Department of Electronic Faculty of EngineeringUSTSana’aYemen

Renewable energy is one of the essential elements of the social and economic development in any civilized *** use of fossil fuels and the non-renewable form of energy has many adverse effects on the most of *** the high potential of renewable energy sources in Yemen and the absence of similar studies in the region,this study aimed to examine the wind energy potential of Hodeidah-Yemen Republic by analyzing wind characteristics and assessment,determining the available power density,and calculate the wind energy extracted at different *** average wind speed of Hodeidah was obtained only for the data currently available for the five years 2005–2009(due to the current economic and the political situation in Yemen).The results show that the average wind speed in the five years is(25.2 W/m2 at 10 m,93.9 W/m2 at 30 m,and 173.5 W/m2 at 50 m).The average yearly wind power density(25.2 W/m2 at 10 m,93.9 W/m2 at 30 m and 173.5 W/m2 at 50m),and the average yearly energy density(220.8 KWh/m2/year at 10 m,822.6 KWh/m2/year at 30 m and 1519.9 KWh/m2/year at 50 m).This research is a preliminary assessment of the potential of wind energy in Hodeidah,which provides useful information for developing wind energy and an efficient wind *** to the International Wind Energy Rating criteria,the region of Hodeidah falls under‘Class 2’and is classified as‘Marginal’for most of the year.

关键词： Energy potential wind speed weibull distribution wind power density

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Design and Implementation of an EMG-based system for Monitoring and Help Request

Design and Implementation of an EMG-based System for Monitor...

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2022 International Conference on Smart systems and Power Management, IC2SPM 2022

作者： Zeine, Carla Daou, Roy Abi Zeid Telawi, Samer Boercsoek, Josef Hayek, Ali Haidar, Gaby Abou University of Kassel Faculty of Electrical Engineering /Computer Science Department of Computer Architecture and System Programming Kassel Germany Lebanese German University Faculty of Public Health Jounieh Department of Computer Architecture and System Programming Biomedical Technologies Department Jounieh Lebanon Université la Sagesse Faculty of Engineering Furn El Chebbak Mechatronics Department Beirut Lebanon University of Applied Science Hamm-Lippstadt Department Lippstadt 2 Hardware Engineering Marker Allee 76-78 Hamm59063 Germany American University of Science and Technology Faculty of Engineering Zahle Lebanon

ISBN: (数字)9781665452915

ISBN: (纸本)9781665452915

Integrating electrophysiological signals in larger systems is getting wider due to the fact that such modalities facilitate the daily tasks of users. However, the high cost of monitoring systems (e.g., oscilloscopes) limits the testing and analysis of the acquired signal from such noisy systems (i. e., electrophysiological devices). The proposed system is a part of a larger proj ect that deals with the identification of sign language movements through the use of Muscle-computer Interfaces. On one hand, the user is able to display the signal acquired by the electrodes on an LCD screen. On the other hand, the system helps a mute patient to issue a special message to his surrounding environment that does not know the sign language to ask for help. The second feature is not unique, as it can be replaced by any other application based on the user's needs. The system was tested on several persons, and the results are promising. © 2022 IEEE.

关键词： Liquid crystal displays

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