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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Sangeetha, M. Kannan, S. Rajes Boopathi, Sampath Ramya, J. Ishrat, Mohammad Sabarinathan, G. Srm Institute of Science and Technology Department of Data Science and Business Systems Tamil Nadu Kattankulathur603203 India Chennai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Chennai600069 India Muthayammal Engineering College Department of Mechanical Engineering Tamil Nadu Rasipuram637408 India St. Joseph's College of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai600119 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram Andhra Pradesh Guntur522502 India Department of Mathematics Tamil Nadu Dindigul624622 India

ISBN: (纸本)9798350300888

In this research, a prototype has been created to automatically collect the photos of fruits using a smartphone camera and a thermal camera, generating two distinct datasets: thermal and RGB. Fruit classification and bruise detection have both been accomplished using image processing, machine learning, and deep learning. To assess quality and shelf-life, a second set of RGB and thermal picture datasets was created. The suggested study enhances fruit categorization and flaw detection systems by utilizing multiple Grey level co-occurrence matrix (GLCM)-based texture characteristics. Using color-texture characteristics, pictures are classified using RF and KNN algorithms. RGB features were classified with 97.24 percent accuracy by K-Nearest Neighbor (KNN) and Random Forest (RF) classifiers, while GLCM-based features were classified with 96.40 percent accuracy by the RF classifier. Using thermal pictures, the KNN and RF algorithms' prediction accuracy was 81.70 percent and 84.12 percent, respectively. KNN and RF algorithms have kappa (percentage) values of 79.97 and 81.86, respectively. The random forest (RF) technique is hence the most appropriate for classifying fruit using a thermal picture dataset. © 2023 IEEE.

关键词： Fruits

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Traffic Light Control Using Reinforcement Learning 2

Traffic Light Control Using Reinforcement Learning

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2nd IEEE International Conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： Masfequier Rahman Swapno, S.M. Nuruzzaman Nobel, S.M. Ramachandra, A.C. Islam, Md Babul Haque, Rezaul Rahman, Mohammad Mominur Bangladesh University of Business and Technology Dept of CSE Dhaka Bangladesh Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India UNICAL Dept. of Computer Modeling Electronic and System Engineering Rende Italy East West University Dept of CSE Dhaka1212 Bangladesh Hamad Bin Khalifa University College of Science and Engineering Qatar

ISBN: (纸本)9798350317558

Nowadays, one of the biggest issues in urban areas is traffic. This problem wastes important time and contributes to air and sound pollution. This affects people's general quality of life in addition to posing health dangers. Our study attempts to mitigate these problems, effectively cutting down on wait times and delays. Our Reinforcement Learning method creates intelligent agents that can adjust traffic lights at crossings instantly. Our objective is to minimize delays, reduce congestion, reduce travel times, improve safety, and improve traffic flow. We implemented the Deep Q Learning algorithm which activities yield the greatest benefits under various traffic scenarios. Our model can the sequence time since the Green signal (GS) lasts 10 seconds and the Red signal (RS) lasts 5 seconds. The waiting period is shortened by 50% as a result. This study suggests reinforcement learning may improve traffic signal controller synchronization and urban traffic congestion. This novel method may improve transport efficiency and sustainability. © 2024 IEEE.

关键词： Action Deep Q Learning GS Model Q Value Rewards RS State Traffic signal vehicles Waiting Time

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Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

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Computers, Materials & Continua 2024年第6期79卷 4963-4977页

作者： Iftikhar Naseer Tehreem Masood Sheeraz Akram Zulfiqar Ali Awais Ahmad Shafiq Ur Rehman Arfan Jaffar Faculty of Computer Sciences&Information Technology The Superior UniversityLahore54000Pakistan Intelligent Data Visual Computing Research(IDVCR) Lahore54000Pakistan Information System Department College of Computer and Information SciencesImam Mohammad Ibn Saud Islamic University(IMSIU)Riyadh11432Saudi Arabia School of Computer Science and Electronic Engineering(CSEE) University of EssexWivenhoe ParkColchesterCO43SQUK

Lung cancer is a leading cause of global mortality *** detection of pulmonary tumors can significantly enhance the survival rate of ***,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high ***,the existing method-ologies cannot obtain a high level of specificity and *** present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet *** LCSC model comprises two distinct *** first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung ***,an improved AlexNet architecture is employed to classify lung *** the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate *** suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or *** proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image database Consortium and Image database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

关键词： Lung cancer segmentation AlexNet U-Net classification

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A Novel Method to Identify and Recover the Fault Nodes over 5G Wireless Sensor Network Environment

A Novel Method to Identify and Recover the Fault Nodes over ...

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2024 Asia Pacific Conference on Innovation in technology, APCIT 2024

作者： Vijaya Vardan Reddy, S.P. Juliet, A. Hency Jayadurga, R. Sethu, S. Pavithra, K.N. Samuthira Pandi, V. R.M.K. Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Saveetha Institute of Medical and Technical Sciences Department of Data Analytics Saveetha College of Liberal Arts and Sciences Saveetha University Tamil Nadu Chennai India Prince Shri Venkateshwara Padmavathy Engineering College Department of Master of Business Administration Tamil Nadu Chennai India Department of Computer Science and Engineering Tamilnadu Chennai India R. M. D. Engineering College Department of Science and Humanities Tamil Nadu Chennai India Chennai Institute of Technology Centre for Advanced Wireless Integrated Technology Tamil Nadu Chennai India

ISBN: (纸本)9798350361537

The significance of communication networks is growing in tandem with the proliferation of communication technologies. Present methods for maintaining 5G Wireless Sensor Networks are still restricted to routine maintenance and post-maintenance tasks. They lack a comprehensive function for monitoring the network's status, are unable to assess the network's health, and are difficult to maintain before the 5G-based WSNs seriously degrade. 5G Wireless Sensor Network faults can only be resolved by highly trained technicians due to low maintenance efficiency. As a result, errors cannot be detected or located promptly or accurately, leading to forced repairs that incur the expense of new network cables. First, the article lays forth the basics of network fault analysis. Then, it uses deep learning to simulate communication network problem diagnosis. Lastly, the experimental section compares various methodologies and analyses the findings of fault location. The results of the simulation demonstrate that the suggested approach mitigates the created model's flaws to a certain degree while simultaneously enhancing the network fault detection model's accuracy, universality, and robustness. A novel approach to autonomous placement, the Fault Node Recovery Protocol is described in this study. It is implemented in 5G mobile communications to detect faulty data according to wireless sensor network standards, and it is made to fix the problems with conventional techniques, such poor positioning precision and lengthy running time. The automated localization model for 5G mobile communication fault data, constructed using the suggested FNRP approach, is presented in this work. By comparing it to the standard Adhoc On-Demand Distance Vector Routing protocol, we can see how well the suggested system performs. © 2024 IEEE.

关键词： 5G mobile communication systems

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The Impact of 5G technology on Agile Project Management: A Cross-Industry Analysis 5

The Impact of 5G Technology on Agile Project Management: A C...

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5th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Mishra, Mahesh Kumar Selvaraj, Krishnamoorthy Santosh, Kathari Aarif, Mohd Suma Christal Mary, S. Kiran Bala, B. Delta System and Software Inc 2320 Grover Cleveland Dr McKinneyTX75072 United States Encloud Service Llc Dept of Erp KellerTX United States Cmr Institute of Technology Bengaluru Department of Mba Bengaluru India Iosrp UP Noida India Panimalar Engineering College Department of Information Technology Poonamalle Chennai India K.Ramakrishnan College of Engineering Department of Artificial Intelligence and Data Science Trichy India

ISBN: (纸本)9798350385649

Project management and organisation are completely transformed by the introduction of 5G technology, which has brought in a new era of connection. This research investigates how 5G networks will affect Enterprise Resource Planning (ERP), Agile Project Management (APM) and the larger context of the technology-Organization-Environment (TOE) paradigm through a cross-industry examination. Agile approaches may benefit from improved adaptive design, cooperation and connectivity as a result of 5G technology being included into project management procedures. A team of developers can work together more easily and make decisions more quickly with the real-time data transfer made possible by 5G networks' higher capacity and speed. This study looks at how Agile methods for managing projects are more effective and flexible when using 5G's low latency and high data throughput. This research explores how Enterprise Resource Planning (ERP) systems may be affected by 5G. With 5G networks' enhanced capabilities, current information synchronisation is made possible in new ways, enabling ERP systems to function with increased precision and speed. This study investigates how supply chain administration, resource allocation and general organisational agility are improved by 5G-enabled ERP. This study evaluates how various sectors' organisational structures, procedures, and objectives are affected by the deployment of 5G infrastructure. This research outlines critical elements that influence the successful implementation of 5G into project management and ERP systems, including technology preparedness, organisational culture, and external factors. This research provides important insights into the potential for transformation of 5G technology through a thorough cross-industry assessment. It also gives organisations an established basis on which to adapt their Agile Project Management and Enterprise Resource Planning practises in the dynamic context of today's digital world. © 2024 IEEE.

关键词： Decision making

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A Way of Making Smart Health Through Collaborating Machine Learning with the Bibliometrics 4

A Way of Making Smart Health Through Collaborating Machine L...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vignesh, T. Sruthi, P.S. Mohanraj, T. Kumar, Gaurav Varkale, Akshay Sheela, M.Sahaya Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Nehru Institute of Technology Department of Computer Science and Business System Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Computer Science Engineering India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India M.P Bhopal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-making processes that led to their creation. It is necessary to modify system creation and use procedures to handle the concrete effects of how knowledge for model building is envisioned and used in real-world applications as AI systems assume more and more important roles. Documents are created at every phase of the data generation lifecycle to improve communication, aid in decision-making, and highlight the need of careful data work. By putting out this paradigm, we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This framework provides an essential and important resource for guaranteeing ethical AI activities, and it is in line with current developments in auditing procedures. © 2024 IEEE.

关键词： Adversarial machine learning

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Modelling Air Pollution and Traffic Congestion Problem Through Mobile Application 1st

Modelling Air Pollution and Traffic Congestion Problem Throu...

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1st International Conference on Deep sciences for Computing and Communications, IconDeepCom 2022

作者： Amudha, S. Shobana, J. Satheesh Kumar, M. Chitra, P. Department of Computational Intelligence SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Data Science and Business Systems SRM Institute of Science and Technology Tamil Nadu Kattankulathur603 203 India Department of Information Technology K.L.N. College of Engineering Tamil Nadu Madurai India Department of Electrical and Electronics Sathyabama Institute of Science and Technology Tamil Nadu Chennai India

ISBN: (纸本)9783031276217

As we know, air pollution is one of the major problems in the metropolis. Due to this a lot of consequences have to be faced by all people. One of the main reasons for this are the vehicles. Most of the time people use their vehicles unnecessarily without any certain reasons. Due to the same reason another problem of traffic congestion arises. And this can be reduced if people become a bit more serious about it. They should prefer using public transport rather than personal vehicles for travelling large distances. By this the number of vehicles on the road will be turned down and it might solve the above two problems directly. So, the main approach is based on this theory itself to reduce the number of transports on the road. The solution is an application which runs on phone and keep track of the fuel used by the vehicle. A certain fuel limit shall be set based on vehicle type, its fuel consumption and other constraints. Once the limit got exceeded, fuel price shall differ and automated calculation would get you actual fuel amount. This aims at reducing the unnecessary usage of vehicles. There is a CO2 emission model in an application which can predict the amount of CO2 produced by the vehicle. The objective of the proposed work is to control the usage of vehicles based on the fuel used by a particular vehicle for a period of time. As the module has been tested with ML model for mileage prediction and CO2 emission with different algorithms and got the best accuracy of around 85%. There will be no hardware in this work, if any hardware implemented all over India then that prototype model will produce good impact in the vehicle fuel usage prediction and CO2 emissions. Hardware based projects using IOT devices, VTRS etc. is something which is very hard to implement in India as CCTV are still not installed and used properly. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Traffic congestion

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Enhancing the Clarity of Surveillance Footage with Deep Residual Neural Networks 2

Enhancing the Clarity of Surveillance Footage with Deep Resi...

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

作者： Prajwal Hegde, N. Jamadar, Jitendrasinh Changala, Ravindra Mukherjee, Samrat Kumar Dhyani, Bijesh Sachuthananthan, B. Nitte Deemed to be University Nmam Institute of Technology Department of Artificial Intelligence and Data Science Karkala India G H Raisoni Institute of Engineering and Business Management Mohadi India Guru Nanak Institutions Technical Campus Department of Cse Hyderabad India Sikkim Manipal Institute of Technology Department of Management Studies Sikkim India Department of Mabagement Studies Dehradun India Sree Vidyanikethan Engineering College Department of Mechanical Engineering Tirupati India

ISBN: (纸本)9798350364699

Closed-circuit television (CCTV) camera pictures are often retained with low resolution as a result of technological and data storage constraints, which diminishes the usefulness of these images. This research suggests that by using intelligent design and training techniques, it is possible to boost the resolution of CCTV pictures using an enhanced deep residual network known as (EDSR). The EDSR approach has never before been used to improve the resolution of CCTV images before this study. In order to train and evaluate an EDSR that could generate high-resolution pictures from a low-resolution dataset, they used a dataset consisting of 986 CCTV images. Applying an EDSR method, they show that executing picture super-resolution with 60 layers is very effective, achieving a peak signal-to-noise ratio of 77.84 db. Additionally, this method has broader potential as a file compression approach that allows for the storing of low-resolution photographs with reduced file sizes and the capacity to recreate the same pictures in high resolution (HR) form. © 2024 IEEE.

关键词： Deep neural networks

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated IoT Prediction

Improving Solar Efficiency via CNN-LSTM and Cloud-Integrated...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： P Varun T. Sunitha Mythili Nagalingam P. Nithiya S Selvakumaran T A Mohanaprakash Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Department of Computer Science and Engineering St. Joseph's Institute of Technology OMR Chennai Department of Artificial Intelligence and Data Science Rajalakshmi Institue of Technology Chennai India Department of Computer Science and Engineering P.B college of engineering Chennai India Department of Artificial Intelligence and Data Science Velammal Engineering College Surapet Chennai Department of Computer Science and Engineering Panimalar Engineering College Chennai India

ISBN: (数字)9798350396157

ISBN: (纸本)9798350396164

Photovoltaic panel used in solar power generation is an environmentally beneficial and sustainable energy source that has been used to transform sunlight into electrical power. Arranged in large solar facilities, these panels are connected to a central inverter, which converts Direct Current (DC) to alternating current (AC) electricity with a small amount of energy loss. Clean surfaces, unhindered light exposure, and high solar irradiance are all necessary for optimal panel performance. It's critical to assess the inverter's efficiency by comparing its AC to DC power. Large-scale installations with sensor-equipped panels and inverters track performance to help with maintenance and forecasting of power generation. The Internet of Things (IoT) facilitates data accessibility and remote monitoring, which helps choose the best location for solar power generation. Smart system continuous monitoring expedites site inspections, which supports urban smart grid integration. In this research study, a hybrid machine learning model is presented by combining the attention processes, long short-term memory (LSTM) networks, and clustering approaches. This model is separated into different phases for forecasting, training, and cloud data clustering, finds pertinent historical data, builds a hybrid machine learning model, and chooses the best training model. In comparison to conventional approaches, this method significantly improves prediction accuracy, which is important for integrating photovoltaic systems into smart grids, particularly in smart cities.

关键词： Training Photovoltaic systems Cloud computing Predictive models Inverters data models Smart grids

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