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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Mathada, Mallikarjuna Amgoth, Tarachand Indian Institute of Technology Indian School of Mines Department of Computer Science & Engineering Dhanbad India

ISBN: (纸本)9798350372120

Wireless charging is one of the most practical procedures to replenish the energy of the resource-constrained nodes in a Wireless Sensor Network (WSN) that contributes mainly towards enhanced network lifetime. After reviewing the existing methodologies of wireless charging schemes, it is noted that there is still a wide scope for improvement owing to various open sets of problems. Therefore, considering the mobility-oriented charging policy, this study presents a unique computational model that may restore energy for the energy-depleted sensor nodes. The proposed study introduces a scheme where both static and mobile chargers are used for energy replenishment using a Reinforcement Learning scheme. Further, the proposed scheme also introduces a dual topology scheme that can perform mobile charging on variable dynamic environmental conditions in WSN. The simulated outcome of the study shows the proposed model to acquire a 50% increase in residual energy, 28% enhanced charging efficiency, 92% reduced delay, and 43% effective packet delivery ratio under increasing number of sensor nodes. © 2024 IEEE.

关键词： Sensor nodes

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PALCT: vehicle-to-vehicle communication based on pseudonym assignment and encryption scheme using delay minimisation cover tree algorithm

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International Journal of Vehicle Information and Communication systems 2025年第2期10卷 132-147页

作者： Tandon, Righa Gupta, P.K. Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Jaypee University of Information Technology HP Solan173 234 India Department of Data Science School of Computing Mohan Babu University AP Tirupati517 102 India

Vehicle-to-vehicle communication is one of the new paradigms of networking, which should be secure, fast, and efficient. In this paper, we propose a framework that implements the pseudonym-based authentication scheme in which communication among vehicles is encrypted by using a matrix array symmetric key (MASK), digital signature algorithm (DSA), and intelligent water drop (IWD). The proposed security scheme also ensures the handling of many security attacks like key-guessing, non-repudiation, replay, and modification. In the proposed scheme, to preserve the identity of the vehicle, we have provided the different pseudonyms for each vehicle in the network that ensure secure communication among vehicles. Furthermore, the proposed delay minimisation cover tree algorithm ensures the issue of timed delay during vehicle-to-vehicle communication. In this algorithm, we have used Dijkstra’s algorithm for finding the optimal shortest path during vehicular communication. The results obtained show that the proposed scheme is effective and efficient, as it reduces the time delay by 4% for 140 vehicle nodes and 28.4% for 1000 vehicle nodes. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Encryption algorithms

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Bone Fracture Detection using Region-Based Convolutional Neural Network

Bone Fracture Detection using Region-Based Convolutional Neu...

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2024 IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2024

作者： Pattabiraman, V. Hari Hara Sudhan, K. Logeshwari, V. Vellore Institute of Technology School of Computer Science and Engineering Tamil Nadu Chennai600127 India

ISBN: (纸本)9798350376876

Bone fracture detection is an essential process in medical field to ensure accurate diagnosis and treatment. This study focusses on novel approach to fracture diagnosis in X Ray images that uses complex machine learning algorithms. As early diagnosis results in an effective treatment, finding bone fractures in medical imaging became very crucial. This study utilizes a substantial dataset of annotated X-ray images featuring various types of fractures. Existing methods for fracture detection are reviewed and compared to the proposed approach. We provide a neural network-based method for identifying bone fractures in X-ray images using Region-Based Convolutional Neural Network (R-CNN) and You Only Look Once (YOLOV8) which are well-known object detection methods that we employ to find fractures. For testing and training we use a large collection of annotated X-Ray images of patients with different kinds of fractures. We evaluate the performance of our method on this dataset and compare it with state-of-the-art methods. Our results demonstrate that the approach we proposed yields superior precision and recall than alternative approaches, with high accuracy of 9 6. 5 % and 9 0 % respectively. The recommended method can be reliable tool for radiologists, who can use it to detect bone fractures more rapidly and accurately, leading to better patient outcomes. © 2024 IEEE.

关键词： Convolutional neural networks

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Forecasting of Photovoltaic Power with ARO based AI approach 2

Forecasting of Photovoltaic Power with ARO based AI approach

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2nd International Conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Nallur, Mounika Nalini, B.M. Khan, Zabiha Nayana, S. Achyutha, Prasad N. Manjula, G. Murray State University Computer Information Systems United States East West Institute of Technology Department of Computer Science and Engineering Bengaluru India Nitte Meenakshi Institute of Technology Department of Artificial Intelligence and Machine Learning Bengaluru India Bgs College of Engineering and Technology Department of Cse Bangalore India

ISBN: (纸本)9798350382952

Renewable power from sunlight can power the future smart grid with massive amounts of electricity. systems struggle with solar energy's unpredictability and intermittent nature. Unpredictability of solar electricity hinders smart grid optimization and planning. Photovoltaic (PV) power generation must be accurately estimated to reduce power interruptions. PV power must be accurately predicted to avoid grid disturbances from PV facilities. Thus, we describe a transfer learning and AlexNet-based CNN architecture for short-term power forecasting. Past power, solar radiation, wind speed, and temperature readings determine the input. AlexNet's hyper-parameters are optimized using the artificial rabbit method. By adding selective opposition to ARO, local solution tracking efficiency is improved. CNN input features are created from all input parameters as 2D feature maps. After analyzing real PV data from Limberg, Belgium, the math shows that PV systems work. © 2024 IEEE.

关键词： Convolutional neural networks

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A Real-Time crisis informatics system by fusing Geo-Spatial Twitter streams and User-Posted images

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Materials Today: Proceedings 2022年 62卷 4738-4744页

作者： Rajarathinam, Ragul Bhuvaneswari, A. Jothi, R. School of Computer Science Engineering Vellore Institute of Technology Tamil Nadu India

Social media such as Twitter is these days one of the fundamental news hotspots for a great many individuals all over the planet because of their minimal expense, simple access, and quick spread. In this paper a real-time disaster mining from twitter disaster event mining using apache spark for natural disasters is proposed by fusing text, images and geo-spatial data. The way of transmitting messages or news reports is not good for developing societies where even the emergency information even can't be transmitted to locals who may get affected due to the disaster. Locating disasters from different locations of the world and mapping location collected from the Twitter sources from different areas at risk of natural disaster and gather them to Geo-parsed real-time tweet data streams. In addition, the Geo-statistical analysis to generate real-time disaster mapping is plotted in a base map. Then report is generated for two case studies tweet disaster mapping to find post-event impact assessment is carried out and detection accuracy is computed. The dataset applied Streaming K-means algorithm and CNN is validated to be a very effective technique to extract event clusters of the identical spatial entities in diversified contexts such as text, photos, geo-tags are observed with an accuracy of 91% which is reasonably acceptable. © 2022

关键词： Mapping

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Predicting Suicidal Ideation on Reddit: A Precise Machine Learning Classifier for Mental Health Support

Predicting Suicidal Ideation on Reddit: A Precise Machine Le...

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International Conference on Advanced Communications and Machine Intelligence, MICA 2023

作者： Goel, Roma Digalwar, Mayuri Department of Computer Science Engineering Indian Institute of Information Technology Maharashtra Nagpur441108 India

ISBN: (纸本)9789819762217

The main aim of this research work is to detect suicide-related thoughts in social media, particularly on Reddit, for preventing potential suicidal incidents and offering appropriate mental health support. Limited access to mental health care facilities, lack of awareness, and inadequate social support hinder individuals facing mental health issues, stress, and insecurity from receiving proper therapy. Social networking platforms like Instagram, Reddit, Twitter, and Facebook have become crucial channels for expressing emotions. Supervised machine learning algorithms, such as classification and sentiment analysis (SA), are commonly used in text analysis. The proposed model employs machine learning (ML) techniques and smart classifiers to proactively identify individuals expressing suicidal thoughts on social media, achieving 95% accuracy, 100% precision, 98% recall, and a 95% F-score. A comparison of proposed model with base paper is performed. This research opens the door for leveraging technology to save lives and enhance well-being in this digital age. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Tweets

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DCHR: Dynamic Cluster Head Replacement Algorithm for Energy Optimization in Wireless Sensor Network 13

DCHR: Dynamic Cluster Head Replacement Algorithm for Energy ...

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13th IEEE International Conference on Communication systems and Network Technologies, CSNT 2024

作者： Kapoor, Abhishek Priyanka Bhadoria, Robin Singh National Institute of Technology Hamirpur Department of Computer Science and Engineering Himachal Pradesh 177005 India

ISBN: (纸本)9798350305463

One of the major aspects for sensing solutions across diverse applications, is the demand associated with better lifetime of sensor nodes. This can be resolved using energy optimization of these nodes in Wireless Sensor Network (WSN). The energy consumption level for certain nodes may not be the same and therefore, it may die earlier than its expectation. In WSN, the critical challenge lies in efficiently managing the finite energy resources of sensor nodes to ensure prolonged network operation and transmission success. In this paper, we present a dynamic cluster head replacement algorithm (DCHR) that calculates a threshold value in determining the number of rounds for a particular sensor as active cluster head (CH). This depends on the remaining energy left with the battery of that particular node. This algorithm helps in reduction of energy consumption during next replacement of CH and ultimately results in overall lifetime of WSN. © 2024 IEEE.

关键词： Sensor nodes

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Trajectory-Driven Efficiency: An Energy-Efficient Opportunistic Routing Protocol for Industrial IoT Wireless Sensor Networks 10

Trajectory-Driven Efficiency: An Energy-Efficient Opportunis...

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10th International Conference on Communication and Signal Processing, ICCSP 2024

作者： Rajulu, G. Govinda Murugesan, M. Mahaboog Basha, S.K. Kalaiselvan, S.A. Kumar, M. Lenin Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai Avadi India Vel Tech Multi Tech Dr.Rangarajan Dr.Sakunthala Engineering College Department of Computer Science and Engineering Chennai Avadi India Tkr College of Engineering and Technology Department of Computer Science and Engineering Telangana India Rajalakshmi Engineering College Department of Computer Science and Engineering Chennai India Vignan Institute of Technology and Science Department of Electronics and Communications Engineering Telangana India

ISBN: (纸本)9798350353068

The rapid expansion of Wireless Sensor Networks (WSNs) in the realm of Industrial Internet of Things (IIoT) demands sophisticated routing protocols to ensure energy efficiency and reliable communication. This paper introduces a novel Energy-Efficient Opportunistic Routing Protocol Based on Trajectory Prediction (EORB-TP) to address these challenges in industrial environments. The research identifies the need for dynamic adaptation to industrial dynamics and changing environmental conditions, prompting the development of trajectory prediction models using Hidden Markov Models (HMMs). EORB-TP intelligently leverages these models for dynamic routing decisions, strategically optimizing data transmission paths to minimize energy consumption. The proposed system's efficiency is demonstrated through a comprehensive experimental study, showcasing superior performance in terms of network lifetime, energy consumption, end-to-end delay, and routing cost compared to existing protocols. This innovative protocol offers a promising solution for enhancing the sustainability and efficiency of Wireless Sensor Networks in Industrial IoT applications, paving the way for resilient and resource-conscious industrial monitoring systems. © 2024 IEEE.

关键词： Routing protocols

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Garbage Detection System Using Robot and Advanced YOLO Algorithm 5th

Garbage Detection System Using Robot and Advanced YOLO Algo...

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5th Doctoral Symposium on Computational Intelligence, DoSCI 2024

作者： Maurya, Saransh Prachi Tiwari, Ayush Singh, Aryan Dwivedi, Shashank Computer Science and Engineering United Institute of Technology Uttar Pradesh Naini Prayagraj 211010 India

ISBN: (纸本)9789819763177

Escalating urbanization has exacerbated challenges in waste management, demanding innovative solutions. This study introduces a smart waste management system integrating the YOLOv5-OCDS (Object Counting Detection System) with TensorFlow and IoT technologies. The YOLOv5-OCDS algorithm, trained on a diverse dataset, efficiently categorizes paper, cardboard, glass, metal, and plastic waste, ensuring rapid and precise results with minimal background noise. The system utilizes a camera module and servomotor on a Raspberry Pi 4 for efficient waste sorting. An ultrasonic sensor monitors bin fill levels, and GPS technology provides real-time location data. The LoRa(Long Range) module transmits bin status at 915 MHz, enhancing remote monitoring. Electronic work is secured by Radio Frequency Identification(RFID)-dependent locker accessible only with authorized badges for maintenance. The system, framed around robotics, neural networks, IoT, and deep learning, demonstrates superior speed and accuracy in garbage detection compared to existing methods. YOLOv5-OCDS’s real-time prediction capability streamlines detection, offering precise results with minimal background noise and exceptional learning capabilities. This research contributes to efficient waste management in urban areas, addressing the escalating challenges of increasing garbage output. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Ultrasonic sensors

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Research on predicting legal judgments by incorporating key information 7

Research on predicting legal judgments by incorporating key ...

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7th International Conference on computer Information science and Application technology, CISAT 2024

作者： Xu, Fan Liu, Wei Guo, Kao Wang, Zihao Wuhan Institute of Technology School of Computer Science & Engineering School of Artificial Intelligence Wuhan China

ISBN: (纸本)9798350375107

The prediction of legal judgments is based on the description of case facts to predict the final charges. Through judgment prediction technology, the judicial system can handle a large number of cases more efficiently and provide more accurate judgment results. However, in reality, the types of charges are numerous and complex. To address the issues of severe data imbalance and easily confusable charges, this paper proposes a legal judgment model that incorporates key information. Firstly, it combines the ALBERT (A Lite BERT) pre-trained model and the bidirectional Gated Recurrent Unit (GRU) model to encode cases, efficiently capturing the contextual semantic information in case descriptions. Then, the concept of key information is introduced, using attention mechanisms and statistical methods to extract two distinguishable parts of key information from the cases, which are then concatenated with the case encoding for the final prediction. Additionally, category classification weights are employed to enhance the model's ability to predict low-frequency samples. The proposed method has achieved good results on real legal judgment datasets. © 2024 IEEE.

关键词： Prediction models

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