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2nd International Conference on Sustainable computing and Data Communication Systems, ICSCDS 2023

作者： Gowtham, R. Jebakumar, R. Srm Institute of Science and Technology School of Computing Faculty of Engineering and Technology Department of Computing Technologies Chengalpattu Tamil Nadu Kattankulathur India

ISBN: (纸本)9781665491990

Human population growth strains food supplies, resulting in more than one billion of the world's 6.5 billion people facing hunger, which is the leading cause of death. In consequence, increasing crop production through traditional farming methods will not address the global food scarcity problem. As a precision agriculture approach, aeroponics, a soilless agriculture technique, can assist in cultivating in-demand food crops, which will help serve the people. Because aeroponics is an indoor farming technology, factors such as pH, EC, light, temperature, PPM, and turbidity will affect crop growth. Hence, monitoring an aeroponic crop growth system efficiently is crucial for achieving a high crop yield and reducing the need for food crops. Even though, many crop monitoring systems use IoT and machine learning algorithms, their prediction accuracy is not up to the level and if they are deployed in the real world, they may not meet customer expectations. In order to efficiently monitor crops, more advanced models are needed. However, understanding the large volume of data can be very tedious. Data visualization techniques can help make the distribution of the dataset easier to understand. Machine learning methods and packages can be used for environmental monitoring and visualization of the data where, the collection of crop growth dataset is carried out using various IoT sensors deployed in the growth environment. This study provides a comprehensive explanation of the data visualization techniques used to study the lettuce crop dataset and predict lettuce yield using the logistic regression machine learning method which is been implemented using Python coding language. © 2023 IEEE.

关键词： Lettuce

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Inductive Lottery Ticket Learning for Graph Neural Networks

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Journal of Computer Science & technology 2024年第6期39卷 1223-1237页

作者： Yong-Duo Sui Xiang Wang Tianlong Chen Meng Wang Xiang-Nan He Tat-Seng Chua School of Data Science University of Science and Technology of ChinaHefei 230027China Department of Electrical and Computer Engineering The University of Texas at AustinAustin 78712U.S.A School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Computing National University of SingaporeSingapore

Graph neural networks (GNNs) have gained increasing popularity, while usually suffering from unaffordable computations for real-world large-scale applications. Hence, pruning GNNs is of great need but largely unexplored. The recent work Unified GNN Sparsification (UGS) studies lottery ticket learning for GNNs, aiming to find a subset of model parameters and graph structures that can best maintain the GNN performance. However, it is tailed for the transductive setting, failing to generalize to unseen graphs, which are common in inductive tasks like graph classification. In this work, we propose a simple and effective learning paradigm, Inductive Co-Pruning of GNNs (ICPG), to endow graph lottery tickets with inductive pruning capacity. To prune the input graphs, we design a predictive model to generate importance scores for each edge based on the input. To prune the model parameters, it views the weight’s magnitude as their importance scores. Then we design an iterative co-pruning strategy to trim the graph edges and GNN weights based on their importance scores. Although it might be strikingly simple, ICPG surpasses the existing pruning method and can be universally applicable in both inductive and transductive learning settings. On 10 graph-classification and two node-classification benchmarks, ICPG achieves the same performance level with 14.26%–43.12% sparsity for graphs and 48.80%–91.41% sparsity for the GNN model.

关键词： lottery ticket hypothesis graph neural networks neural network pruning

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Machine Learning for Brain Tumour Detection: A Systematic Review of Segmentation and Classification Methods

Machine Learning for Brain Tumour Detection: A Systematic Re...

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2023 Intelligent computing and Control for engineering and Business Systems, ICCEBS 2023

作者： Uvaneshwari, M. Baskar, M. School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computer Science and Engineering Chengalpattu Tamilnadu Kattankulathur603 203 India School of Computing College of Engineering and Technology Srm Institute of Science and Technology Department of Computing Technologies chengalpattu Tamilnadu Kattankulathur603 203 India

ISBN: (纸本)9798350394580

A brain tumour is an abnormal development of brain tissue that disrupts normal brain function. The proper diagnosis of a brain tumour relies heavily on its correct identification. This article discusses the pros and cons of recently developed technologies for diagnosing brain tumours. We propose a generic flow map that can be used in many different situations, including the detection of brain tumours. The many accessible datasets are given for use in evaluating brain tumour algorithms. There is a need for research that identifies brain tumours with decent efficiency and fair effort, therefore this article compares the performance of recent literature additions in terms of Accuracy, Sensitivity, Specificity, and time consumption. © 2023 IEEE.

关键词： 3D MRI Images Brain Tumour Classification Machine Learning Segmentation

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Vision based intelligent traffic light management system using Faster R‐CNN

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CAAI Transactions on Intelligence technology 2024年第4期9卷 932-947页

作者： Syed Konain Abbas Muhammad Usman Ghani Khan Jia Zhu Raheem Sarwar Naif R.Aljohani Ibrahim A.Hameed Muhammad Umair Hassan Department of Computer Science and Engineering University of Engineering and TechnologyLahorePakistan Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal UniversityJinhuaChina OTHEM Manchester Metropolitan UniversityManchesterUK Faculty of Computing and Information Technology King Abdulaziz UniversityJeddahSaudi Arabia Department of ICT and Natural Sciences Norwegian University of Science and Technology(NTNU)ÅlesundNorway

Transportation systems primarily depend on vehicular flow on roads. Developed coun-tries have shifted towards automated signal control, which manages and updates signal synchronisation automatically. In contrast, traffic in underdeveloped countries is mainly governed by manual traffic light systems. These existing manual systems lead to numerous issues, wasting substantial resources such as time, energy, and fuel, as they cannot make real‐time decisions. In this work, we propose an algorithm to determine traffic signal durations based on real‐time vehicle density, obtained from live closed circuit television camera feeds adjacent to traffic signals. The algorithm automates the traffic light system, making decisions based on vehicle density and employing Faster R‐CNN for vehicle detection. Additionally, we have created a local dataset from live streams of Punjab Safe City cameras in collaboration with the local police authority. The proposed algorithm achieves a class accuracy of 96.6% and a vehicle detection accuracy of 95.7%. Across both day and night modes, our proposed method maintains an average precision, recall, F1 score, and vehicle detection accuracy of 0.94, 0.98, 0.96 and 0.95, respectively. Our proposed work surpasses all evaluation metrics compared to state‐of‐the‐art methodologies.

关键词： access control artificial intelligence computer vision intelligent control

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LoRa WAN Architecture with Collusion Management and Collusion Avoidances Schemes in MAC Layer

LoRa WAN Architecture with Collusion Management and Collusio...

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International Conference on computing and Machine Learning, CML 2024

作者： Saravanan, N. Kayalvizhi, R. Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chengalpattu Tamil Nadu India

ISBN: (纸本)9789819778386

Long Range (LoRa) is the most widely used technology for significant attention in recent years due to their ability to enable long-range, low-power connectivity, making it an ideal communication system for Internet of Things applications. LoRa radio is considered as the physical layer, MAC layer which was specifically developed for low power IoT devices. LoRa is one of the technologies in LPWAN, which demonstrated remarkable potential in various domains used for IoT applications. This research paper presents a comprehensive study of LoRa technology, covering its LoRa principles (Physical Layer, MAC Layer, Network layer) Communication Mechanism, Application usage. Through an in-depth analysis of the key features and advantages of LoRa, this paper aims to provide a holistic understanding of the technology and its potential implications for the future of IoT in Collusion management and collision avoidance. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Information management

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A Hybrid Relational Approach Toward Stock Price Prediction and Profitability

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第11期5卷 5844-5854页

作者： Patel, Manali Jariwala, Krupa Chattopadhyay, Chiranjoy Sardar Vallabhbhai National Institute of Technology Department of Computer Science and Engineering Surat395007 India FLAME University School of Computing and Data Sciences Pune412115 India

An accurate estimation of future stock prices can help investors maximize their profits. The current advancements in the area of artificial intelligence (AI) have proven prevalent in the financial sector. Besides, stock market prediction is difficult owing to the considerable volatility and unpredictability induced by numerous factors. Recent approaches have considered fundamental, technical, or macroeconomic variables to find hidden complex patterns in financial data. At the macro level, there exists a spillover effect between stock pairs that can explain the variance present in the data and boost the prediction performance. To address this interconnectedness defined by intrasector stocks, we propose a hybrid relational approach to predict the future price of stocks in the American, Indian, and Korean economies. We collected market data of large-, mid-, and small-capitalization peer companies in the same industry as the target firm, considering them as relational features. To ensure efficient feature selection, we have utilized a data-driven approach, i.e., random forest feature permutation (RF2P), to remove noise and instability. A hybrid prediction module consisting of temporal convolution and linear model (TCLM) is proposed that considers irregularities and linear trend components of the financial data. We found that RF2P-TCLM gave the superior performance. To demonstrate the real-world applicability of our approach in terms of profitability, we created a trading method based on the predicted results. This technique generates a higher profit than the existing approaches. © 2024 IEEE.

关键词： Profitability

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Space robotics hybrid conceptual model for tracking and estimation using IoRT and AI-based control system

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Materials Today: Proceedings 2023年 72卷 958-965页

作者： Sinha, Anurag Garg, Namit Singh, N.K. Kumar, Biresh Banerjee, Pallab Verma, Achyut Kumar Department of Computer Science IGNOU Delhi India Department of Mathematics and Computing Indian Institute of Technology Kanpur India Department of Computer Science and Engineering Birla Institute of Technology Mesra Ranchi India Department of Computer Science and Engineering Amity University Jharkhand Ranchi Jharkhand India Department of Chemical Engineering Indian Institute of Technology Kanpur India

Robotics is an amalgamation of mechanical engineering and computer science. Mechanical engineering helps to design and develop mechanical parts and devices for control systems in robots. Space robots and robotics are known as devices that can enhance the manipulation, functionalities, and control exercised by astronauts, and thus can be called their artificial assistants for real-time exploration of space conditions. Control systems in robots like gestures and action become predominant for control, thus human-robot interaction comes into existence. AI and reinforcement learning have been used to control functionality in robots in various fields, but IoRT (Internet of robotic Things) is an emerging area of IoT that has the capabilities to monitor a variety of courses of action in robots. In this paper, we are proposing a conceptual framework based on an IoRT control system integrated with AI and reinforcement learning algorithms, which has been accessed based on multiple papers in the same domain, and it will help future researchers develop and simulate such a prototype. We are also using AKF (Adaptive Kalman Filtering) for robot tracking and noise deduction in sensors integrated with the A* algorithm. Overall, this is a concept-based framework to be designed and simulated. © 2023

关键词： Reinforcement learning

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A Lightweight Hybrid Framework for Real-Time Detection of Process Related Anomalies in Industrial Time Series Data Generated by Online Industrial IoT Sensors 15

A Lightweight Hybrid Framework for Real-Time Detection of Pr...

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15th International Conference on Computer and Automation engineering, ICCAE 2023

作者： Bagchi, Atish K Chandrasekaran, Sivachandran Swinburne University of Technology School of Science Computing and Engineering Technologies Department of Computing Technologies Hawthorn Campus Australia

ISBN: (纸本)9798350396225

Industrial Manufacturing plays an important role in the global economy, and estimates suggest that approximately 27 hours per month are lost in any major facility due to unplanned stoppages. The advent of Industrial IoT has seen manufacturing facilities deploy low-cost sensors with the hope of gaining improved visibility and thereby reducing unplanned machine stoppages and wastages. This paper introduces a lightweight, fast, easy-to-deploy framework that can be used for reliable and accurate identification of anomalies in real-time operational systems. The framework is holistic and includes data acquisition and data persistence modules to ensure that it can be deployed to a working production facility. The anomaly detection and contextualisation module is hybrid and uses statistical techniques and machine learning methods to provide fast responses while requiring minimal intervention. The framework was tested at a large metals manufacturing facility in New South Wales, Australia and the results show an accuracy of over 97% in anomaly detection. © 2023 IEEE.

关键词： Anomaly detection

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Explainable AI for Real-Time Object Detection in Autonomous Driving

Explainable AI for Real-Time Object Detection in Autonomous ...

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2024 International Conference on Artificial Intelligence, Metaverse and Cybersecurity, ICAMAC 2024

作者： Sajid, Tayyab Latif, Omar Abdul Electrical Engineering and Computing Sciences Department Rochester Institute of Technology - Dubai Dubai United Arab Emirates

ISBN: (纸本)9798350353488

The increasing complexity of Artificial Intelligence (AI) models, especially in applications like autonomous driving, healthcare, and other systems, requires not only high accuracy but also transparency in the decision-making processes. This paper presents an approach to integrating Explainable AI (XAI) methods with YOLOv8, a state-of-the-art object detection model, to provide interpretability of its predictions in real-time autonomous driving environments. We focus on implementing various Class Activation Mapping (CAM) methods, such as Grad-CAM and HiResCAM, as well as Layer-wise Relevance Propagation (LRP) to create an explanation framework. We also combine CAM and LRP to make use of the strengths of both methods, improving the overall explainability. This method achieves good performance, running object detection at 50 to 60 frames per second (FPS) on a standard dedicated GPU, and around 24 FPS when running alongside the explainability models. Furthermore, this approach maintains a high detection accuracy, due to its post hoc nature, making sure that the XAI implementations do not affect the model's detection performance. Our experimental results shows that the combined XAI approach not only preserves the high detection accuracy of YOLOv8 but also provides more meaningful visual explanations using the XAI methods, showing that this integrated XAI methods could improve the trustworthiness and safety of AI-driven systems in autonomous vehicles and other possible fields. © 2024 IEEE.

关键词： Cams

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A Comparative Study on Spam Identification using Naive Bayes and SVM 5

A Comparative Study on Spam Identification using Naive Bayes...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Verma, Shashwat Agrawal, Shikhar Gulati, Shreshth Gouthaman, P. Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Networking and Communications School of Computing Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

Email is one of the most frequently employed digital means of communication among individuals use on an on-going basis. Sending unidentified messages to someone is termed as spam. The internet remains the most accessible source of information, and social media is becoming more and more prevalent. This research offers a unique framework that uses multinomial naive bayes (multinomial NB) and support vector machine (SVM) advanced machine learning approaches to improve email spam detection. It increases the classification efficiency and accuracy when compared to more conventional techniques like Random Forest and basic Naive Bayes. The method also includes a significant amount of data preprocessing, including text normalization, stemming, and count vectorization feature extraction. This approach can be enhanced by being utilized by email clients or servers to recognize spam emails. Experiments have demonstrated that the Support Vector Machine technique returns good accuracy, and this research advances email spam detection techniques. © 2024 IEEE.

关键词： Adversarial machine learning

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