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2nd International Conference on Connected Objects and Artificial Intelligence, COCIA 2024

作者： Olanloye, Odunayo Dauda Adeniyi, Abidemi Emmanuel Aworinde, Halleluyah Oluwatobi Awotunde, Joseph Bamidele Imoize, Agbotiname Lucky Mejdoub, Youssef Department of Computer Science Bowen University Osun Iwo Nigeria Department of Computer Science Faculty of Information and Communication Sciences University of Ilorin Ilorin240003 Nigeria Department of Electrical and Electronics Engineering University of Lagos Akoka Lagos Nigeria RITM Laboratory Higher School of Technology ESTC Hassan II University Casablanca Morocco

ISBN: (纸本)9783031704109

A prominent application in healthcare is the classification and detection of breast cancer using convolutional neural networks (CNNs). It entails creating a system that can precisely pinpoint malignant spots or gauge the tendency of breast cancer through the examination of diagnostic pictures like mammograms. This study focuses on the use of Convolutional Neural Networks for the detection and classification of breast cancer. The proposed model analyzes mammogram images to distinguish cancerous and non-cancerous instances automatically. Preparing the dataset developing the CNNs model’s architecture, training the model, assessing its performance, and producing predictions based on hypothetical data are all the steps in the process. The CNN model learns to extract relevant features and patterns from a large dataset, increasing classification accuracy. Tests show that the CNN model is effective at correctly identifying breast cancer. This method will help medical professionals diagnose patients quickly and correctly, improving patient outcomes in the fight against breast cancer. The prediction can be improved using post-processing techniques and the trained model can be used in practical applications. The model accuracy was 0.98 and the model loss was 0.14. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolutional neural networks

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Dual-Band Reflectarray Antennas Using Integrated Resonant and Non-Resonant Natures of Metallic Waveguide Elements at Millimeter Wave Frequencies

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Radio Science 2023年第1期58卷 1-13页

作者： Chou, H.-T. Wang, N.-N. Fang, M. Wang, L.-Q. Akkaraekthalin, P. Torrungrueng, D. Graduate Institute of Communication Engineering National Taiwan University Taipei Taiwan School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand Research Center of Innovation Digital and Electromagnetic Technology Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut's University of Technology North Bangkok Bangkok Thailand

Pure metallic reflectarray antennas have good power efficiency and low fabrication cost, limited to single-band operation in the past. A dual-band reflectarray antenna is developed for two directional beams by two feeds. The reflecting elements are metal waveguides and have novel properties of both resonant and non-resonant modes at the two frequency bands. The low and high-frequency bands can be easily separated by the cutoff frequency of waveguide modes. The phase-changing mechanisms via ray optics and fundamental waveguide modes, respectively, provide simple formulations for the elemental structure design of directional beams. Radiation characteristics of the linear and circular polarizations are cross-examined with a good performance by full-wave simulations using HFSS, FEKO, and CST at 28 and 60 GHz bands for 5G front-haul network applications. In particular, we have used HFSS to simulate the reflecting elements' basic characteristics of phase variation, FEKO to simulate the linear polarization antenna case, and CST to simulate the circular polarization antenna case, respectively. © 2023. American Geophysical Union. All Rights Reserved.

关键词： Cutoff frequency

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Enhancing Perceptual Quality of Images using Deep Residual U-Net and PatchGAN Discriminator

Enhancing Perceptual Quality of Images using Deep Residual U...

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International Conference on New Trends in Computing Sciences (ICTCS)

作者： Satvik Raghav Sathwik Narkedimilli Prasanth Ayitapu Raja Karthikeya Lalitha Sreeram Department of Electronics and Communication Engineering Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science Engineering Indian Institute of Information Technology (IIIT) Dharwad India

ISBN: (数字)9798331523411

ISBN: (纸本)9798331523428

This paper presents a novel approach for perceptually aware image enhancement utilizing a deep residual U-Net generator combined with a Patch-based Generative Adversarial Network (PatchGAN) discriminator. Our method incorporates a perceptual loss function based on a pre-trained VGG16 network, ensuring that enhanced images exhibit improved color sharpness, clarity, and retention of critical high-level features. Experiments show improved structural similarity, with SSIM of 0.9270 and FSIM of 0.9998. These results indicate both perceptual and structural enhancement, while the model efficiently generates high-quality images with minimal training. Our method consistently outperforms conventional approaches in visual appeal and computational efficiency, making it relevant for real-world applications such as radiology, self-driving vehicles, and low-light photography. Our implementation is open-sourced and available here: https://***/bp8zsl.

关键词： Training Visualization Technological innovation Smart cities Service robots Computational modeling Surveillance Image enhancement Biomedical imaging Sports

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Land Cover Segmentation for Agricultural Monitoring Using Synthetic Aperture Radar (SAR) Imagery with Convolutional Neural Networks and Attention-Based Transformer Models

Land Cover Segmentation for Agricultural Monitoring Using Sy...

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Multi-Agent Systems for Collaborative Intelligence (ICMSCI), International Conference on

作者： Prakash E. P K.G. Sathiya S. Manoranjitham G. Sekar S Munaf R. K. Santhia Department of Computer Science and Engineering Dr. N.G.P. Institute of Technology Coimbatore Department of Electronics and Communication Engineering Arunai Engineering College Tiruvannamalai Department of Information Technology Agni college of Technology Chennai Department of Electronics and Communication Engineering VSB College Of Engineering Technical Campus Coimbatore Department of Electronics and Communication Engineering Sri Ramakrishna Institute of Technology Coimbatore Department of Computer Science and Engineering Pondicherry University

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Automated classification of land cover types is one of the most crucial aspects within many remote sensing applications, including the monitoring of agricultural land and territories. This work proposes a novel approach for division of land covers using a hybrid of CNNs and Attention-based Transformer networks. CNNs are adopted to extract the spatial features from different levels of the SAR image, while Transformer Model Arch applies self-attention functions to explore the long-range dependency and contextual relation of the entire image. The model learning process is performed end-to-end from multi-temporal SAR data to differentiate different types of land covers, including crops, vegetation, water, and urban surfaces. In this case, we apply the proposed approach to different agricultural areas to show that it outperforms traditional methods, such as thresholding and other machine learning approaches. The results highlight the viability of this mixed form to solve the noise and changeable nature of SAR imagery, in terms of enhancing the use of the agricultural monitoring application.

关键词： Image segmentation Accuracy Computational modeling Noise Land surface Transformers Feature extraction Radar polarimetry Convolutional neural networks Monitoring

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Energy Efficient Routing Scheme for Performance Enhancement of MANET Through Dominating Set

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2023年第4期17卷 89-100页

作者： Sangeetha, M. Erna, G. Kannan, S. Rajes Salameh, Anas A. Kishore, Kakarla Hari Department of Data Science and Business Systems SRMIST KattankulathurChennai India Ongole India Computer Science and Engineering Chennai Institute of Technology Chennai India Department of Management Information Systems College of Business Administration Prince Sattam Bin Abdulaziz University Al-Kharj Saudi Arabia Department of Electronics and Communication Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh India

Mobile Ad-Hoc network is a distributed wireless network that is self-organized and self-maintained and it doesn’t require a fixed framework or central administration. Wireless nodes in a mobile ad hoc network are transient and have a short lifespan. They combine to form a self-configured, infrastructure-free network where routing is a significant difficulty. The wireless nodes in the network must synchronize in order to decide connectivity and routing. Ad hoc network synchronization includes activities including node organization, neighborhood detection, and routing. The predetermined framework resulted in an increase in network control overhead. If a node can link to every other node in the network, it is referred to as a dominating node, and a set of dominating nodes make up the dominating set. The main purpose of this article is to lessen routing overhead in the wireless network with the dominating set based routing (DSBR). In order to discover routes, shorten reroute establishment times, and increase packet delivery rates, this research presents an energy-efficient routing algorithm (EERA) that considerably increases the network performance. The effectiveness of this technique is demonstrated by the simulation analysis of the outcome. © 2023,International Journal of Interactive Mobile Technologies. All Rights Reserved.

关键词： Mobile ad hoc networks

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Road Accident Detection using MobileNetv2 with Gradient Boosting Algorithm 1

Road Accident Detection using MobileNetv2 with Gradient Boos...

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1st International Conference on Software, Systems and information Technology, SSITCON 2024

作者： Priyanka, K. Obaid, Saif Anguraj, K. Thangam, V. Thirumani Sahana, R. Nitte Meenakshi Institute of Technology Department of Information Science and Engineering Bengaluru India The Islamic University College of Technical Engineering Department of Computers Techniques Engineering Najaf Iraq Sona College of Technology Department of Electronics and Communication Engineering Salem India New Prince Shri Bhavani College of Engineering and Technology Department of Computer Science and Engineering Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr.Sakunthala Engineering College Avadi Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798350352931

An increased number of vehicles on the roads increases the risk of accidents and eventually resulted huge loss of lives and property. Accident detection utilizing computer vision in much consideration as of late. The existing methods for accident detection gave inaccurate detection due to the errors in sensors and cameras may not capture all angles or all areas. Therefore, this paper proposes MobileNetV2-Gradient Boosting (MNGB) for detection of road accidents which effectively gave accurate detections. Firstly, all videos are split up into smaller shots according to scene changes and then key frames are extracted from each shot based on consecutive frames. This images are augmented by using Kalman filter which reduced the noise and artifacts in images, accurately tracks the objects across frames. Then, features are extracted by using Multi-Scale Network (MSNet) where it extracts the features at various scales by capturing both local and global information. After that, these features were classified by using proposed MNGB which enables the real time processing and reduces the computational resources and gave accurate detection results. The proposed MNGB for road accident detection achieved better results in terms of accuracy 95 %, precision 9 8 %and F1-Score of 9 6 %when compared with existing YOLOv3. © 2024 IEEE.

关键词： Kalman filters

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Deep Learning for Sentiment Analysis: Exploring the Power of Deep Learning Techniques in Opinion Mining

Deep Learning for Sentiment Analysis: Exploring the Power of...

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2023 International Conference on Evolutionary Algorithms and Soft Computing Techniques, EASCT 2023

作者： Sindhu, L. Rameshkumar, M. Kumar, Gotte Ranjith Manjunath, B.N. Sapna Kumari, C. Sona College of Technology Department of Information Technology Salem India Paavai Engineering College Department of Computer Science and Engineering India Sr University School of Computer Science & Artificial Intelligence Warangal India R.L. Jalappa Institute of Technology Department of Computer Science & Amp Engineering Bangalore India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350313413

Sentiment analysis (SA) is a popular method for collecting relevant and arbitrary information from text-based data. To locate, examine, extract reactions, and emotions from the data or states, it applies computational linguistics, biometrics, text analysis, and Natural Language Processing (NLP). A SA model can be developed and improved with the use of the features analysis method. However, it can be difficult to find the best classification methods for this type of data. When compared to current feature-based methods, deep learning models for the analysis of sentiment, are capable of providing precise representation and improved performance. In this paper, the deep learning method was proposed for improving sentiment classification performance with a sophisticated embedding word method and develop a network of Convolutional Neural Network and Long short-term memory (CNN-LSTM). For the purpose of predicting the sentiment of tweets for classification, a large amount of Amazon data is analysed. This study, concentrate on improving sentiment analysis performance by building a CNN-LSTM network and particularized model of deep learning with novel word embedding techniques. The CNN-LSTM model has achieved 98.20% accuracy, 99.86% precision, 99.50% recall, and 98.78% recall in sentiment analysis. © 2023 IEEE.

关键词： Convolutional Neural Network Deep Learning Long-short-term memory (LSTM) Sentiment analysis Word embedding

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On Connectivity of Flying Ad Hoc Networks in the Presence of Ground Terminal

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China communications 2021年第9期18卷 1-10页

作者： Chao Liu Zhongshan Zhang School of Computer and Communication Engineering University of Science and Technology(USTB)BeijingBeijing 100083China School of Information and Electronics Beijing Institute of Technology(BIT)Beijing 100081China

The Unmanned Aerial Vehicle(UAV)technologies are envisioned to play an important role in the era of Air-Space-Ground integrated *** this paper,we investigate the connectivity of a Flying Ad hoc Network(FANET)in the presence of a groundbased *** particular,the connected probability of the UAV-to-UAV (U2U) link as well as that of the UAV-to-Ground (U2G) link in a three dimensional (3D) space are ***,to mitigate the aggregate interference from UAV individuals,a priority based power control scheme is implemented for enhancing the connectivity of both U2U and U2G *** results illustrate the effectiveness of the proposed analysis.

关键词： flying ad hoc network UAV swarm connectivity power control connected probability

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Healthcare Bots for Elderly Assistance Using Cloud-Driven Gradient Boosting Machines 4

Healthcare Bots for Elderly Assistance Using Cloud-Driven Gr...

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4th International Conference on Innovative Practices in Technology and Management, ICIPTM 2024

作者： Sudha Juliet, P. Sivakumar, S. Thinakaran, K. Srinivas, Porandla Thamizhamuthu, R. Velmurugan, S. Meenakshi College of Engineering Department of Electronics and Instrumentation Engineering Tamil Nadu Chennai India Department of Computer Science Tamil Nadu Perambalur India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Malla Reddy Institute of Engineering and Technology Department of Information Technology Telangana Hyderabad India Srm Institute of Science and Technology Department of Computing Technologies Tamil Nadu Chennai India T.J.S. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350307757

As the world's population ages, there will be a greater need for creative methods to help the elderly maintain their independence and quality of life at home. This study investigates the feasibility of using healthcare bots powered by cloud-based Gradient Boosting Machines (GBM) to provide elderly people with individualized and timely support. The proposed system learns from each user's interactions and improves over time to better meet their specific requirements. Also, the cloud-based design ensures real-time updates and smooth interaction with different devices. Results show that the system is effective in improving the quality of geriatric support, promoting aging in place, and contributing to the creation of patient-centric healthcare solutions via a thorough review that includes user input and performance indicators. The results point to a potential direction for integrating Artificial Intelligence (AI), cloud computing, and healthcare to meet the issues of an aging population. © 2024 IEEE.

关键词： Machine learning

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Enabling Energy Efficient communication between Entities Using 5G Assisted Wireless Sensor Networks

Enabling Energy Efficient Communication between Entities Usi...

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2023 International Conference on System, Computation, Automation and Networking, ICSCAN 2023

作者： Vijayalakshmi, S. Charaan, R M Dilip Bala Sundara Ganapathy, N. Nivedha, C.S. Senthilkumar, C. Kumar, G V S Manoj R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Panimalar Engineering College Department of Information Technology Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Mba Tamil Nadu Chennai India Saveetha School of Engineering Simats University Department of Electronics and Communication Engineering Tamil Nadu Chennai India Department of Electronics and Communication Engineering MLR Institute of Technology Telangana Hyderabad India

ISBN: (纸本)9798350315127

Most of the sensor nodes in WSNs rely on power sources, which have a finite lifespan and can adversely influence the network's functionality, excellence and longevity because of the amount of energy they place on each node. Thus, several clustering approaches are proposed to enhance WSNs' energy efficiency. Mobile communication technology has completely altered the way individuals interact with one another. Providers of internet services now face difficulties due to the exponential growth in the amount of users, the speed of data transfer, the need for more bandwidth, and the volume of data being sent. The expectation is that the contemporary communication system will fulfill all these needs. Next-generation communication technologies will prioritize the creation of user terminals that mix several flows from different technologies and enable access to those technologies. The researchers of this work have offered a comprehensive summary of the current state of wireless communication technologies and their potential for future improvement. This transmission is energy intensive. There has been a rise in the number of users necessitating constant connection due to the transition from 4G, which is transformed to 4G with LTE in wireless communication. Because of this, battery life on mobile devices is shortened. Therefore, a strategy should be created to reduce consumption of energy, which will lengthen the life of portable packs of batteries. A fifth-Generation (5G) technological advance, which is discussed in this article, is discussed, and it is both intelligent and interconnected with the rest of the world. In addition, this paper addresses the limitations of current wireless technology, the problems with current cellular networks, and the necessity for the next-generation communication technologies. In addition, 5G wireless communication has been the subject of extensive research and discussion. The major concern of this paper is to prove the efficiency of the propos

关键词： Energy efficiency

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