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Advances in Electrical, Computing, communication and Sustainable Technologies (ICAECT), International Conference on

作者： Mayuri Kundu Binduswetha Pasuluri Argha Sarkar School of Computer Science and Engineering REVA University Bengaluru India Department of Electronics and Communication Engineering G Pulla Reddy Engineering College Kurnool India

Vehicular Ad-hoc Network is subclass of Mobile Ad-hoc networks and plays a vital role in Intelligent Transportation System implementation for smart cities. In the transportation scenario finding out an optimized path and locating the malicious vehicle is a big challenge. Routing is challenging in this technology because of reasons like high mobility of the node, rapid change in topology, interruption in connectivity, etc. The mobility of nodes affects the link stability and congestion is the effect of limited node resources. It also degrades the Quality-of-Service performance. Unmanned Ariel Vehicle helps to mitigate such conditions with the combination of learning capability. In this paper, the contribution of the Unmanned Ariel Vehicle to a smart transportation system has been studied and the working of different protocols is also analyzed. This paper gives a complete overview of the advancement of the system and implementation concept of different smart traffic management systems in urban areas.

关键词： Wireless communication Smart cities Network topology Stability criteria Quality of service Routing Ad hoc networks

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Image Caption Generation using Deep Residual Learning

Image Caption Generation using Deep Residual Learning

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2022 IEEE International Conference on Current Development in engineering and Technology, CCET 2022

作者： Jain, Ritika Jhapate, Arun Saxena, Minal Computer Science & Engineering Sagar Institute of Research & Technology Madhya Pradesh Bhopal India Electronics & Communication Sagar Institute of Research & Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9781665454155

The process of creating a written description of an image that describes the action depicted in it is known as image captioning. It is one of the most challenging study areas, and the only method that can help is the machine learning one. Image captioning is a tool where intellectual approach is required for better performance. The suggested work uses CNN, RNN, and Deep Residual Network to propose an image captioning system. RNN serves as the decoder while CNN serves as the encoder. For the MSCOCO benchmark, the system can accurately infer the state of affairs. Deep Residual Network (Deep ResNet), which efficiently utilises the layers and cuts down on computation time, was used to train the model. Deep ResNet, also known as skip connection, bypasses the convolutional layers that provided a solution to the gradient explosion problem. Comparing the Bilingual Evaluation Understudy (BLEU) score to the previously used model, the system perceived a higher score. By lowering the image's size and improving its visibility return good results. © 2022 IEEE.

关键词： Deep learning

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A Lightweight Blockchain Scheme for Secure Data communication in Internet of Things-enabled Wireless Sensor Network

A Lightweight Blockchain Scheme for Secure Data Communicatio...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Lavina Balraj A. Prasanth K.K. Devi Sowndarya T. Kuntavai Department of Electronics and Communication Engineering Sri Venkateswara College of Engineering Sriperumbudur Tamil Nadu India Department of Computer Science and Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Tamil Nadu India Department of Electronics and Communication Engineering DMI College of Engineering Chennai Tamil Nadu India Department of Electrical and Electronics Engineering Adhipara Sakthi Engineering College Chengalpattu Tamil Nadu India

ISBN: (数字)9798350364040

ISBN: (纸本)9798350364057

In today’s world, there will probably be an enormous rise in the volume of data sensed by sensors on the Internet of Things-enabled Wireless Sensor Network (IWSN). The sensed data from sensor nodes is gathered and transmitted to the sink. These sensor nodes suffer from several consequences including security, transparency, trust, and immutability. Furthermore, other technical impairments like computation, storage of sensed data, privacy, and energy conservation enhance the issues of the IWSN. To overcome this, a novel Blockchain Interplanetary File Access Control System (BIFACS) has been proposed to manage intelligent autonomous access control for IWSN. Efficient data security and data transmission can be achieved in the proposed scheme by using the BIFACS algorithms. Further, the proposed scenario is implemented in the Solidity programming language and deployed on the local Ethereum. The efficacy of the proposed system has been analyzed based on computing, storage, and network performance.

关键词： Access control Performance evaluation Wireless sensor networks Memory Sensor systems and applications Internet Blockchains

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Blockchain Technology and Artificial Intelligence's Future Applications in Cyber Security 3

Blockchain Technology and Artificial Intelligence's Future A...

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3rd International Conference on Advancement in electronics and communication engineering, AECE 2023

作者： Salama, Ramiz Al-Turjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Gupta, Richa Yadav, Satya Prakash Vats, Satvik Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350330724

Artificial intelligence and blockchain are quickly integrating in daily life and business applications. When numerous information systems must access and analyze data in real-time in centralized systems and applications, such as healthcare, a bottleneck develops. This issue would be resolved by blockchain's decentralized database architecture, safe data storage, data exchange, and authentication. Additionally, AI can be present at the very top of the blockchain and produce insights from the shared data that is created and applied to forecasts. Blockchain is a cutting-edge Cybersecurity technology that creates chains by mutual consent between nodes that chronologically link new blocks to those previously stored in nodes. A number of industries, including banking, insurance, cybersecurity, forecasting, healthcare, and cryptocurrencies, among others, are growing more quickly as a result of technology convergence. The likelihood of these systems being hacked increases as more digital technologies are used and these businesses provide services. Blockchain technology and artificial intelligence can work together to create a strong defense against these dangers and security problems. We'll examine how blockchain and AI are merged in cybersecurity in this chapter. We'll go into more detail about how they help secure cyber-physical systems. © 2023 IEEE.

关键词： Blockchain

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IOT Enabled Heart Disease Detection System Using KNN 13

IOT Enabled Heart Disease Detection System Using KNN

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13th International Conference on Computing communication and Networking Technologies, ICCCNT 2022

作者： Utsav, Ankur Mohanty, Shreeya Bishwajeet, Kumar Kumar, Tanay Birla Institute of Technology Mesra Department of Electronics and Communication Patna Campus India Birla Institute of Technology Mesra Department of Computer Science and Engineering Patna Campus India

ISBN: (数字)9781665452625

ISBN: (纸本)9781665452625

Many people around the world suffer from heart disease, which is a critical health issue. Early identification of disease is important in order to save many lives. Internet of things (IoT) technology is being developed in conjunction with the healthcare industry. IoT sensor data collected through IoT devices may contain more noise and missing values than data collected from traditional *** order to remotely anticipate and monitor patients with heart disease, technological assistance or clinic professionals is needed. This model is developed with the help of classification algorithms, as they play an important role in *** classification techniques, including Random Forest, Support Vector Machine (SVM), K-nearest Neighbors (KNN), and Decision Tree, are used to create the model. The classifiers are trained and tested using the Cleveland data repository. As part of our research, we have also sought to find correlations between the different attributes of the dataset using standard machine learning methods and then used them effectively to predict chances of heart disease. In this research work, we have used a benchmark dataset of Cleveland Dataset, which consists of 14 different parameters. KNN has the best accuracy of all classifiers assessed using performance metrics. The KNN model may be utilized in healthcare, which is essential in the field of cardiology to accurately and practically diagnose cardiac *** goal of this study is to create a model for anticipating cardiac illness using machine learning methods from IoT sensor devices, which has so far yielded a prediction accuracy that is satisfactory. © 2022 IEEE.

关键词： Internet of things

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Modified CNN for Multi-class Brain Tumor Classification in MR Images with Blurred Edges 2

Modified CNN for Multi-class Brain Tumor Classification in M...

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2nd IEEE Mysore Sub Section International Conference, MysuruCon 2022

作者： Pathak, Vishvesh Maheswari, B. Uma Iyer, Sridhar Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Bengaluru India Kle Technological University-MSSCET Department of Electronics and Communication Belagavi India

ISBN: (数字)9781665497909

ISBN: (纸本)9781665497909

Brain tumors are caused by the hyperproduction of specific cells. Only sixty percent of patients with a malignant brain tumor survive for more than a year. As there is no treatment for tumor, the strategy implemented to provide relief to the patient is preventing tumor cells from spreading through chemotherapy or surgery. Only early stages of the cancer can be successfully treated with these approaches, making early cancer detection crucial. However, the doctors with exceptional domain knowledge need not be occupied with this task as it is linear enough to program computers to achieve adequate success in predictions, and hence, the doctors can be involved only when there is an uncertainty in the computer predictions. In order to determine the type of tumor on Magnetic Resonance Images (MRI), this article suggests a novel strategy that combines deep learning and image transformation techniques using Fourier transformation, and classifies brain tumors into four categories, including gliomas, meningiomas, pituitary tumors and no-tumor. The results show that, in comparison to the existing deep learning models, proposed strategy yields a 5% gain in accuracy simultaneously classifying and providing results more quickly. © 2022 IEEE.

关键词： Chemotherapy

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Car Damage Identification System Using Convolutional Neural Network

Car Damage Identification System Using Convolutional Neural ...

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Industrial electronics: Developments & Applications (ICIDeA), IEEE International Conference on-

作者： Raji Krishna Tulasi Prasad Sariki Siri Mahalakshmi Sigamsetty G. Bharadwaja Kumar D. Jayanthi A. Asha School of Electrical Engineering Vellore Institute of Technology Chennai Campus Chennai India School of Computer Science Engineering Vellore Institute of Technology Chennai Campus Chennai India Electronics and Communication Engineering Rajalakshmi Engineering College Chennai India

ISBN: (数字)9798331533205

ISBN: (纸本)9798331533212

The number of individuals driving has increased daily, leading to a rise in the number of auto insurance claims. The lengthy process of registering, processing, and determining the outcome of each claim includes the service engineer's manual assessment, which serves as the basis for the damage report, and the physical examination carried out by an insurance company surveyor. It would be advantageous for both the organization and the client if this entire procedure were automated. This system utilizes images of the car in a damaged state as input. It then generates pertinent data, including the locations of the damaged sections (front, rear, and sides) and an assessment of the extent of the damage (none, minor, or substantial). This system uses the Convolutional Neural Network (CNN) Model to analyze images automatically for auto insurance, providing the user with information on the location and extent of the vehicle damage. ImageNet weights and the VGG16 architecture are also used here. Transfer learning is used for a variety of tasks, such as finding and classifying different kinds of auto parts and issues. The logistic regression classifier is trained using feature and label extraction methods.

关键词： Industries Logistic regression Machine learning algorithms Transfer learning Insurance Manuals Companies Feature extraction Convolutional neural networks Automobiles

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Retraction Note: Sub-network modeling and integration for low-light enhancement of aerial images

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Optical and Quantum electronics 2024年第10期56卷 1-2页

作者： Uganya, G. Devi, C. H. Sarada Chaturvedi, Abhay Shankar, B. B. Ramesh, Janjhyam Venkata Naga Kiran, Ajmeera Department of ECE Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India Department of CSE Meenakshi College of Engineering Chennai India Department of Electronics and Communication Engineering GLA University Mathura India Department of Electronics and Communication Engineering NITTE NITTE (Deemed to Be University) NMAM Institute of Technology (NMAMIT) Karkala India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Vaddeswaram Guntur India Department of Computer Science and Engineering MLR Institute of Technology Dundigal Hyderabad India

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Optimization Model of Housing Construction Project Scheduling Based on Optimized Ant Colony Algorithm 2

Optimization Model of Housing Construction Project Schedulin...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Srinivasa, Gowda G.K. Krishna, G.R. Sanjay Rex, J. Kumar, K. Sambath Kumar, Madhan Mohan Manikandan, N. Department of Computer Science and Engineering ACS College of Engineering Tamilnadu Bangalore India Department: Mechanical Engineering Koneru Lakshmaiah Education foundation Green fields Andhra Pradesh Vaddeswaram India Department of Civil Engineering Malla Reddy Engineering College Tamilnadu Hyderabad India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai India Department of Robotics and Automation Easwari Engineering College Bharathi salai Ramapuram Tamilnadu Chennai India Dept of Mechanical Engineering School of Engineering Mohan Babu University Andhra Pradesh Tirupati India

ISBN: (纸本)9798350305418

Construction management is a standardized means of project management. The construction project takes a long time and has many links, and the construction workers involved are complicated. Poor quality buildings will not only affect the construction enterprises’ brand, but also threaten the safety of people's lives and property. In this paper, an optimization model of housing construction project scheduling based on ant colony algorithm (ACA) is put forward. By studying the relationship among various sub-projects in the construction system of building projects, the management of building projects can be better realized. The results show that compared with the other two algorithms, the optimized ACA has certain advantages in recall and accuracy. There are many contents involved in the building construction process, and there are many uncontrollable factors in the building process, which greatly impact the project's construction quality. The construction industry can use this technology to strengthen the building construction management, solve the problems existing in the construction management, and improve the construction quality. © 2023 IEEE.

关键词： ant colony algorithm Building engineering Construction supervision Optimization Model Project scheduling

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Implementing LoRa MIMO System for Internet of Things

arXiv

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arXiv 2025年

作者： Ghosh, Atonu Chandan, Sharath Misra, Sudip Department of Computer Science and Engineering Indian Institute of Technology Kharagpur Kharagpur721302 India Department of Electronics and Communication Engineering Sona College of Technology Tamil Nadu Salem636005 India

Bandwidth constraints limit LoRa implementations. Contemporary IoT applications require higher throughput than that provided by LoRa. This work introduces a LoRa Multiple Input Multiple Output (MIMO) system and a spatial multiplexing algorithm to address LoRa’s bandwidth limitation. The transceivers in the proposed approach modulate the signals on distinct frequencies of the same LoRa band. A Frequency Division Multiplexing (FDM) method is used at the transmitters to provide a wider MIMO channel. Unlike conventional Orthogonal Frequency Division Multiplexing (OFDM) techniques, this work exploits the orthogonality of the LoRa signals facilitated by its proprietary Chirp Spread Spectrum (CSS) modulation to perform an OFDM in the proposed LoRa MIMO system. By varying the Spreading Factor (SF) and bandwidth of LoRa signals, orthogonal signals can transmit on the same frequency irrespective of the FDM. Even though the channel correlation is minimal for different spreading factors and bandwidths, different Carrier Frequencies (CF) ensure the signals do not overlap and provide additional degrees of freedom. This work assesses the proposed model’s performance and conducts an extensive analysis to provide an overview of resources consumed by the proposed system. Finally, this work provides the detailed results of a thorough evaluation of the model on test hardware. © 2025, CC BY-NC-ND.

关键词： Orthogonal frequency division multiplexing

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