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Landslide Monitoring Using IoT System with Cloud Platform 10

Landslide Monitoring Using IoT System with Cloud Platform

10th IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and computer engineering, UPCON 2023

作者： Rawat, Pradeep Singh Soni, Prateek Kumar Goel, Aryan Kaur, Gurmeet Dit University Computer Science & Engg. Department Dehradun India Gla University Computer Engineering & Application Mathura India

ISBN: (纸本)9798350382471

The natural disasters poses the losses in the disaster prone area. Landslides pose great socio-economic threats to regions which geography of the location favors it. The hill states of the India are more prone to the landslides due to the heavy rain fall other environmental challenges. The researchers found that ninety-nine percent of landslides induced due to heavy rain falls. The severity of disasters especially landslides is gradually increasing day by day in scale and frequency, resulting in widespread loss of human lives and infrastructure of the nation. The detection, monitoring and mitigation of the landslides in hill states is the major concern. The mitigation and monitoring of the effects of landslides can be performed using technology integration. This article presented an innovative landslide monitoring system. It is based on the IoT protocols based monitoring system consisting of cloud platform which provides instant alerts of upcoming landslides on the basis of real time data set collected inside the IoT cloud platform. As part of this monitoring system, landslide data is sent directly to the civil protection department and notified to the local population for the awareness and future precautionary safety measures. The results and discussions section shows the real time data visualization of the landslide inducting parameters, including metrological, geophysical and hydrological parameters. © 2023 IEEE.

关键词： Accelerometer sensor Arduino IDE Arduino uno Cloud Internet of Things Soil moisture Sensor

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Time Series Analysis and Forecasting of Water Quality Parameters along Yamuna River in Delhi 2

Time Series Analysis and Forecasting of Water Quality Parame...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Gupta, Neetu Yadav, Surendra Chaudhary, Neha School of Engineering & Technology Career Point University Kota324005 India Department of Computer Science and Application Vivekananda Global University Jaipur303012 India Department of Computer Science and Engineering Manipal University Jaipur 303007 India

The extent of pollution in areas near industrial activities and Yamuna River has increased tremendously and to determine the overall water quality status in these locations is utmost important. This paper presents a comprehensive study on assessing the water quality and gain insights into the overall water quality status in the vicinity of industries and along the Yamuna River in Delhi. To achieve this, water samples were collected from Central Pollution Control Board (CPCB) for the last eight years (2013-2021) and was converted into a machine-readable format to facilitate further analysis. These samples were analysed for several water quality parameters, including pH, dissolved oxygen (DO), biochemical oxygen demand (BOD), chemical oxygen demand (COD), nutrient levels, heavy metals, and other relevant pollutants. By understanding these factors, a deeper understanding of the pollution sources and their impact on water quality is gained. Time series methods are applied to forecast future trends and values of the water quality parameters. This allows for predicting potential changes in water quality over time. A remarkable accuracy of 93.6% is attained in predicting water quality values up to the present time. The analysis reveals that water quality in the NCR region falls below acceptable standards. However, there has been a marginal enhancement in the water quality at Khajori Paltoon (Location L3) post-COVID. To achieve a more significant improvement in water quality, it is imperative to implement new policies. © 2024 Elsevier B.V.. All rights reserved.

关键词： Water quality

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A Study Of Cyclone Tracking Mechanism Using Deep Learning Techniques

A Study Of Cyclone Tracking Mechanism Using Deep Learning Te...

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

作者： Paul, Tushar Dhanalakshmi, R. Masters of Computer Application School of Computer Science and Engineering Vellore Institute of Technology Chennai India School of Computer Science and Engineering Vellore Institute of Technology Centre for Cyber Physical Systems Chennai India

ISBN: (纸本)9798350315127

With over 90 storms emerging worldwide each year, tropical cyclones (TCs) are the most damaging weather systems to emerge over the tropical oceans. For the purpose of providing advanced warning to the impacted areas, fast TC detection and tracking is essential. Remote sensing is essential for the detection of these storms since they originate over open oceans that are far from the continents. Here, we describe an innovative deep learning approach-based automated technique for TC detection from satellite photos. Our research presents a three-phase deep learning architecture for TC detection that consists of three components: 1) a classifier - convolutional neural network (CNN);2) a wind speed filter;and 3) a detector - Mask convolutional neural network (CNN). Using Bayesian optimization, the hyperparameters of the pipeline as a whole are tuned to display optimal performance. Results show that the suggested method produces test images with specificity (97.59%), high precision (97.10%) and accuracy (86.55%). © 2023 IEEE.

关键词： Tropical cyclone

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Re-Cognition I: An Inspection of Learner's Cognitive Well-Being, Pursuant to Contemporary Period, Facilitates the Academic Life Cycle 6

Re-Cognition I: An Inspection of Learner's Cognitive Well-Be...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Ganguly, Souvik Pati Mandal, Kailash Guha, Debasis Dr. B.C. Roy Engineering College Dept. of Computer Application Durgapur India National Institute of Technology Dept. of Computer Science and Engineering Durgapur India

ISBN: (纸本)9798350306613

Recommender systems enhance the user experience by integrating factual and behavioral practices, enhancing learning strategies, and granting academic opportunities based on learners subjective interests. Cognitive intelligence, a learner's capability to interpret and synthesize cognitive abilities, is crucial to assemble effective recommendations. However, a student encounters diverse adversities in his or her academic life, whether it is his or her academic pacers or outward circumstances. As a result, a learner consequently experienced severe academic setbacks. Thus, this paper proposes a novel approach to identify cognitive deficits of learners in a school or college before recommending any scholastic content or pertaining in any academic activity. We have employed a modified Bi-directional Gated Recurrent Unit (Bi-GRU) method in conjunction with Shuffled Frog Leaping Algorithm (SFLA). Our approach overcomes some of the shortcomings of conventional Bi-GRU method in choosing the right scaling parameters during training. Bi-GRU method is opted for detecting the cognitive deficits of a learner and SFLA is applied over Bi-GRU in order to hyper-Tune the parameters. The proposed method is compared with a few cutting-edge methods, and the experimental result outperforms the other methods in training, validation, and testing accuracies, achieving 99.34%, 98.98%, and 99.19% respectively. © 2024 IEEE.

关键词： Bi-directional GRU Cognitive Deficits Cognitive Equilibrium Learner's Mental State Meta-heuristic Optimization SFLA

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RG-ACA: efficient and adaptive routing method for internet of things based on metaheuristic approach

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International Journal of Information Technology (Singapore) 2025年 1-8页

作者： Kiani, Farzad Rad, Hamid Data Science Application and Research Center (VEBIM) Fatih Sultan Mehmet Vakif University Istanbul Türkiye Computer Engineering Dept Azad University Zanjan Iran

In Internet of Things (IoT) systems that use various and limited devices, efficient use of resources is critical. However, since this problem is inherently complex, an enhanced meta-heuristic approach is proposed. In this paper, a new method called Reverse Gauss Ant Colony Algorithm (RG-ACA) is suggested for the design of efficient routing protocol for these systems. It is analyzed on the network lifetime, throughput and packet delivery parameters and the results are compared with HEEL, I-HEEL, EES-LEACH and iABC algorithms. The RG-ACA algorithm ranked first in all categories compared to other related current studies with 94%, 96 Kbps and 91% performance in these three parameters, respectively. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Energy efficiency Internet of things Reverse gauss ant colony algorithm Routing

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Enhanced Route Optimization for Wireless Networks Using Meta-Heuristic engineering

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computer Systems science & engineering 2022年第10期43卷 17-26页

作者： S.Navaneetha Krishnan P.Sundara Vadivel D.Yuvaraj T.Satyanarayana Murthy Sree Jagadeesh Malla S.Nachiyappan S.Shanmuga Priya Department of Electronics and Communication Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science and Engineering SACS MAVMM Engineering CollegeMaduraiIndia Department of Computer Science Cihan University-DuhokKurdistan RegionIraq Department of Computer Science and Engineering Bapatla Engineering College BapatlaAndhra PradeshIndia Department of Computer Application National Institute of TechnologyThiruchirapalliIndia School of Computer Science and Engineering Vellore Institute of TechnologyChennaiIndia Department of Computer Science and Engineering M.I.E.T Engineering CollegeTrichyIndia

Wireless Sensor Networks(WSN)are commonly used to observe and monitor precise *** consist of a large number of inexpensive sensor nodes that have been separated and distributed in different *** base station received the amount of data collected by the numerous *** current developments designate that the attentFgion in applications of WSNs has been increased and extended to a very large *** Trust-Based Adaptive Acknowledgement(TRAACK)Intrusion-Detection System for Wireless Sensor Networks(WSN)is described based on the number of active positive deliveries and The Kalman filter used in Modified Particle Swarm Optimization(MPSO)has been proposed to predict knot *** were run for non-malicious networks(0%malicious)and different percentages of malicious nodes were *** findings suggest that the proposed method TRAACK Modified Particle Swarm Optimization(MPSO)packet delivery rate outperforms TRAACKPSO by 3.3%with 0%malicious ***,the packet delivery rate of TRAACKMPSO is 30%malicious,3.5%better than TRAACKPSO in WSN.

关键词： Trust-based adaptive acknowledgment(TRAACK) modified particle swarm optimization(MPSO) intrusion detection system(IDS) kalman filter

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Digital Secure Storage for Healthcare Data Information Using Blockchain 3

Digital Secure Storage for Healthcare Data Information Using...

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3rd International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2023

作者： Naveenkumar, H. Ranjana, P. Hindustan Institute of Technology & Science Department of Computer Application Chennai603103 India Hindustan Institute of Technology & Science Department of Computer Science Engineering Chennai603103 India

ISBN: (纸本)9798350399264

Healthcare Blockchains grant access to a modern technique to accumulate healthcare information, achieve healthcare, and make a belief for medical records distribution and records combination in a decentralized unlock healthcare network surroundings. Even though the healthcare blockchain technologies have paid attention to extroverted benefits in addition to a concentration in assiduousness, management as well as university circles, the safety and confidentiality concerns stay behind the center of attention of deliberate while organizing Blockchains for information sharing across company operations and research collaboration in the healthcare sector. Medical data sharing via Blockchains are the subject of research that focuses on security and privacy needs. Together with technological solution techniques and strategies, the research provides a complete evaluation of the safety and confidentiality risks, requirements, and technology solutions. In the beginning, we discuss the security and privacy requirements for using healthcare Blockchains to share electronic medical records. Second, we group current initiatives into three reference blockchain procedure scenarios for the sharing of electronic medical records. We consider the technologies that can be used to implement this secure store for healthcare data properties in the two categories of healthcare Blockchains procedure scenarios, including anonymous signatures, and attribute-based encryption. Source to implement the access control for hospital, patient, doctor, admin. © 2023 IEEE.

关键词： Blockchain

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Robust Multi-Object Tracking Using Vision Sensor with Fine-Grained Cues in Occluded and Dynamic Scenes

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IEEE Sensors Journal 2025年第11期25卷 20547-20560页

作者： Hu, Yaoqi Sun, Jinqiu Jin, Hao Niu, Axi Yan, Qingsen Zhu, Yu Zhang, Yanning School of Computer Science China School of Astronautics China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Northwestern Polytechnical University Dongxiang Road Shaanxi Xi'an710129 China

Multi-object tracking (MOT) using vision sensors remains a challenging problem, particularly in dynamic backgrounds and severe occlusions. Existing methods, relying on holistic appearance or spatial cues, fail to capture detailed information within object regions and the background, resulting in inaccurate and inconsistent tracking. To address these issues, we propose a novel Detail-Driven Multi-Object Tracking (DD-MOT) method that revisits and leverages fine-grained cues to discover the dynamics of both object regions and the background, facilitating the recovery and association of trajectories. Specifically, the proposed method consists of three key modules: (i) Points Trajectories Generator, (ii) Camera Motion and Occlusion Compensation, and (iii) Fine- and Coarsegrained Association. The Points Trajectories Generator is responsible for generating fine-grained cues by sampling an initial set of points, generating point trajectories, and refining the initial set of points. The Camera Motion and Occlusion Compensation module utilizes the background and object point trajectories to correct background motion and recover occluded object bounding boxes. Finally, the Fine- and Coarse-grained Association module leverages point trajectory cues to assist in establishing a more effective association strategy, combining both fine-grained and coarse-grained spatial cues from the object bounding boxes. We evaluate our DD-MOT method on several benchmark datasets, including MOT17, MOT20, demonstrating that it consistently outperforms state-of-the-art (SOTA) methods in key metrics such as HOTA (64.2, 62.6), MOTA (80.8, 78.1), and IDF1 (78.8, 77.1). © 2025 IEEE.

关键词： Motion compensation

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Efficiency beyond Limits: LSTM and k-NN Synergy for Forecasting Energy Needs in Data Centers

Efficiency beyond Limits: LSTM and k-NN Synergy for Forecast...

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2024 Asia Pacific Conference on Innovation in Technology, APCIT 2024

作者： Mogal, Amit K. Sonaje, Vaibhav P. Kumar, C.H.N. Santhosh Devi, V. Chithra Gottumukkala, Prasanthi Satheesh Kumar, D. Sandip University School of Computer Science and Engineering Department of Computer Science & Application Maharashtra Nashik India Sandip University School of Computer Science and Engineering Department of Computer Science & Applications Maharashtra Nashik India Anurag Engineering College Department of CSE Telangana Kodada India PSNA College of Engineering and Technology Tamilnadu Dindigul India Gokaraju Rangaraju Institute of Engineering and Technology Department of Information Technology Hyderabad India Hindusthan College of Engineering and Technology Department of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350361537

The objective of this study is to examine the effectiveness of a hybrid methodology that combines Long Short-Term Memory (LSTM) and k-Nearest Neighbors (k-NN) models in the context of energy prediction within data centres. The objective of this study is to improve the accuracy of predictions and the efficiency of operations by utilising the combined capabilities of these two machine learning approaches. The proposed LSTM-kNN model has superior performance compared to individual LSTM and k-NN models, as evidenced by a thorough assessment on sample data. It achieves a Mean Absolute Error (MAE) of 9.45, Root Mean Square Error (RMSE) of 12.97, and a coefficient of determination (R2) value of 0.89. The importance of model synergy in enhancing the accuracy of energy forecasts is shown by comparative analysis. The results of this study provide a significant contribution to the advancement of energy management methods in data centres. These findings provide vital insights for stakeholders, enabling them to optimise resource allocation, capacity planning, and sustainability initiatives. Proactive energy optimisation measures and cost-effective operational decisions are examples of practical applications. Potential avenues for future study encompass the exploration of supplementary machine learning techniques and the examination of the influence exerted by external factors on patterns of energy usage. In summary, this work highlights the significance of incorporating machine learning methods to tackle intricate issues in energy prediction and control in data centre settings. © 2024 IEEE.

关键词： k-nearest neighbors

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Correction to: Evaluation of land use land cover dynamics and urban heat island effects over Mumbai metropolitan Region, India (International Journal of Environmental science and Technology, (2024), 10.1007/s13762-024-06266-4)

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International Journal of Environmental science and Technology 2025年 1-2页

作者： Kavathekar, V. Tripathy, A.K. Chettri, S.K. Bhanage, V. Computer Science and Engineering Computer Application Assam Don Bosco University Assam India Computer Engineering Don Bosco Institute of Technology Mumbai India PHIS Centre The IDEC Institute Hiroshima University IDEC Bldg. 1-5-1 Kagamiyama Higashi-Hiroshima739-8529 Japan

The original version of this Article contained an error in the representation of elevation values depicted in the Fig. 1. Incorrect version of Fig. 1 Geographical location of the Mumbai metropolitan region contributes to achieving the region’s sustainable development goals. (Figure presented.) Readers should consider following Figure as Fig. 1. Correct version of Fig. 1 Geographical location of the Mumbai metropolitan region (Figure presented.) © The Author(s) under exclusive licence to Iranian Society of Environmentalists (IRSEN) and science and Research Branch, Islamic Azad University 2025.

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