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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Shwetha, A.N. Prabodh, C.P. Siddaganga Institute of Technology Department of Computer Science and Engineering Tumakuru India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349900

Many challenges are faced by agricultural sector due to various dependent factors like changes in climatic conditions, variation in soil properties and farming practices. What to plant in their agricultural land is the common question for all farmers. Many farmers are failing to identify the crop that suits their agricultural land to get good yields. This results in following the same methods and crops what their ancestors were following. The crop yield always depends on multiple parameters like soil properties, climate conditions, water level etc. So if the farmers get suggestions regarding which crop to grow in their agricultural land would be of great help. The government is also helping farmers in many ways, one such scheme is soil testing. Soil testing is very important because through testing we can identify nutritional level, pH level and moisture level of soil which can optimize crop prediction. To address the above mentioned issues, this paper proposes a Crop Recommendation System(CRS) to help farmers based on machine learning algorithms to take wise decision to select a crop for their agricultural land. The CRS considers historical data, soil type, pH level, season, water level and amount of pesticides to provide crop recommendation for agricultural plots. The CRS system uses Naive Bayes algorithm to select the crop suitability. The CRS is a user friendly system that can be accessed through web or mobile. The user can give necessary input regarding their agricultural land and get crop recommendation which suits their farming land. The proposed system aims to improve crop yield, minimize resources and environmental impact. © 2024 IEEE.

关键词： Smart agriculture

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Tracking of Ship and Plane in Satellite Videos Using a Convolutional Regression Network with Deep Features 3rd

Tracking of Ship and Plane in Satellite Videos Using a Convo...

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3rd International Conference on Machine Vision and Augmented Intelligence, MAI 2023

作者： Sharma, Devendra Srivastava, Rajeev Computer Science and Engineering Department Indian Institute of Technology BHU Varanasi India

ISBN: (纸本)9789819743582

Object tracking is one of the latest research topic in various applications of computer vison. Satellite videos are provided by the commercial satellite makes it possible to extend into earth observation. Moving objects are occupy the small region of pixel and they are confused with neighboring regions also with objects. Due to the resolution constraints, the nearest similar objects can rarely be distinguished by appearance features. Conventional tracking techniques in satellite videos with hand-crafted features do not give better results. Deep learning-based techniques have proven to be superior in visual tracking benchmarks, but this is not fully explored for the object tracking in the satellite videos. Regression network is used to integrate the regression model with convolutional layers. Both motion feature and appearance feature are taken from pre-trained deep neural networks VGG16, VGG19, and ResNet50. When tracker faces obscure appearance information, motion features of object are used for discrimination and complementary information to improve the performances of model. Experimental analysis on ship and plane represents the ResNet50 feature extractor gives better tracking result. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep neural networks

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Study Exploring The Use Of Iot And ML In Pregnant Women Healthcare Monitoring System 2

Study Exploring The Use Of Iot And ML In Pregnant Women Heal...

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2nd IEEE International Conference on Advances in Information technology, ICAIT 2024

作者： Hemanth, N.G. Aishwarya, S.S. Department of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383867

In developing countries, rural areas often lack integrated medical systems, leading to pregnant women not receiving regular checkups, resulting in higher death rates for newborns and parents. This presents significant medical issues for women. A healthcare kit for pregnant women is designed to help them monitor their health and well-being. The kit, equipped with sensors that track heart rate, temperature, and other vital metrics, provides a convenient way for pregnant women to track ztheir health and identify potential problems early. By providing real-time data, the kit enables women to seek medical attention if needed, reducing the need for costly and time-consuming medicalinterventions. © 2024 IEEE.

关键词： Medical problems

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A Multi-Functional Web Tool for Comprehensive Threat Detection Through IP Address Analysis

A Multi-Functional Web Tool for Comprehensive Threat Detecti...

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2024 IEEE Conference on engineering Informatics, ICEI 2024

作者： Tanan, Cebajel Kulkarni, Sameer G. Das, Tamal Hanawal, Manjesh K. Indian Institute of Technology Computer Science and Engineering Department Dharwad India

ISBN: (纸本)9798331505776

In recent years, the advances in digitalisation have also adversely contributed to the significant rise in cybercrimes. Hence, building the threat intelligence to shield against rising cybercrimes has become a fundamental requisite. Internet Protocol (IP) addresses play a crucial role in the threat intelligence and prevention of cyber crimes. However, we have noticed the lack of one-stop, free, and open-source tools that can analyse IP addresses. Hence, this work introduces a comprehensive web tool for advanced IP address characterisation. Our tool offers a wide range of features, including geolocation, blocklist check, VPN detection, proxy detection, bot detection, Tor detection, port scan, and accurate domain statistics that include the details about the name servers and registrar information. In addition, our tool calculates a confidence score based on a weighted sum of publicly accessible online results from different reliable sources to give users a dependable measure of accuracy. Further, to improve performance, our tool also incorporates a local database for caching the results, to enable fast content retrieval with minimal external Web API calls. Our tool supports domain names and IPv4 addresses, making it a multi-functional and powerful IP analyser tool for threat intelligence. © 2024 IEEE.

关键词： Internet protocols

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A Review on YOLO Algorithms for Social Distancing 5th

A Review on YOLO Algorithms for Social Distancing

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5th International Conference on Deep Learning, Artificial Intelligence and Robotics, ICDLAIR 2023

作者： Kumar, Vijay Murmu, Mahendra Kumar Computer Engineering Department National Institute of Technology Kurukshetra136119 India

ISBN: (纸本)9783031609343

Object detection is one of the appealing branch of the study in the fields of computer science. Object detection defines some popular object detection models in deep learning such as YOLO (You Only Look Once), Single Sort Detector, R-CNN etc. It can be applied to solve many important challenges in computer vision, human-computer interaction such as health-care monitoring, autonomous driving, anomaly detection etc. Social distancing terminology has been coined repeatedly in-light of COVID19. Social distancing is an inherent part of health-care systems. In general, nowadays, social distancing applications have become more important due to the transmission of various diseases like smallpox, Scabies, COVID19, etc. It happens due to not maintaining of specified distance between infected peoples. The paper proposes a review of real-time applications of YOLO based object detection in social distancing. The present work is useful to understand the overview of YOLO models that can be applied for social distancing. Additionally, comparisons between the various YOLO versions with respect to different performance metrics concerning speed, accuracy, and computational complexity. These results are vital for the selection of an optimal YOLO version for real-time social distancing application. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Deep learning

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PCLive: Pipelined Restoration of Application Containers for Reduced Service Downtime 15

PCLive: Pipelined Restoration of Application Containers for ...

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15th Annual ACM Symposium on Cloud Computing, SoCC 2024

作者： Tripathi, Shiv Bhushan Mishra, Debadatta Department of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798400712869

Application containers are widely used in contemporary cloud computing environments. Migration of containers across hosts provides cost-effective cloud management by enabling improved server consolidation, load balancing and enhanced fault tolerance. One of the primary objectives of container migration is to reduce the service downtime of applications hosted in containers. The service downtime depends on performing the migration activities efficiently, specifically from the time the container is stopped on the source host till it is restored and fully functional at the destination host. In this paper, we show that, the state-of-the-art pre-copy migration strategy for containers using checkpoint and restore techniques (e.g., CRIU) inflates the downtime due to its inherent limitations in the restoration procedures, particularly for containers with large memory working set size. We propose PCLive to address this bottleneck using a pipelined restore mechanism. Compared to the baseline CRIU pre-copy migration, PCLive results in up to ∼38.8x reduction in restoration time which leads to a reduction of service downtime by up to ∼2.7x for migration of a container hosting the Redis key-value store over an one Gbps network. We also present comprehensive comparative analysis of the resource cost for the proposed solution along with additional optimizations to demonstrate that PCLive can reduce the application downtime in a resource efficient manner leveraging its flexible and efficient design choices. © 2024 Owner/Author.

关键词： Restoration

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On building automation system security

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High-Confidence Computing 2024年第3期4卷 103-122页

作者： Christopher Morales-Gonzalez Matthew Harper Michael Cash Lan Luo Zhen Ling Qun Z.Sun Xinwen Fu Department of Computer Science University of Massachusetts LowellLowell 01854USA Department of Electrical and Computer Engineering University of Central FloridaOrlando 32816USA School of Computer Science and Technology Southeast UniversityMa’anshan 243032China School of Computer Science and Engineering Anhui University of TechnologyNanjing 211189China

Building Automation Systems(BASs)are seeing increased usage in modern society due to the plethora of benefits they provide such as automation for climate control,HVAC systems,entry systems,and lighting *** BASs in use are outdated and suffer from numerous vulnerabilities that stem from the design of the underlying BAS *** this paper,we provide a comprehensive,up-to-date survey on BASs and attacks against seven BAS protocols including BACnet,EnOcean,KNX,LonWorks,Modbus,ZigBee,and *** studies of secure BAS protocols are also presented,covering BACnet Secure Connect,KNX Data Secure,KNX/IP Secure,ModBus/TCP Security,EnOcean High Security and Z-Wave *** and ZigBee do not have security *** point out how these security protocols improve the security of the BAS and what issues remain.A case study is provided which describes a real-world BAS and showcases its vulnerabilities as well as recommendations for improving the security of *** seek to raise awareness to those in academia and industry as well as highlight open problems within BAS security.

关键词： Building automation system BAS protocols Security Attack

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A New Approach for Prevention of Malicious Attack in Post Disaster Using Delay Tolerant Network

A New Approach for Prevention of Malicious Attack in Post Di...

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2024 International Conference on Intelligent Systems for Cybersecurity, ISCS 2024

作者： Chakrabarti, Chandrima Bal, Saptarshi Jaiswal, Rishabh Ansari, Arbaz Akhtar Narula Institute of Technology Department of Computer Science and Engineering Kolkata India

ISBN: (纸本)9798350375237

Natural disasters still have a crippling effect on our infrastructure notwithstanding society's development. Communication systems, the backbone of our network, sustain the most damage during such crises. When it comes to setting up working communication systems, volunteers working on disaster relief missions face enormous obstacles because of the deteriorating communication infrastructure and extended power outages. Smartphones have become essential tools because they may create a peer-to-peer network, in opportunistic way also called as a Delay Tolerant Network (DTN), through Bluetooth or Wi-Fi connections in a latency mode, which makes communication easier in post-disaster situations. Nevertheless, nodes in this network can act strangely, failing to create routes or send data to other nodes, which would hinder the functioning of the network as a whole. Malicious nodes are those that behave in this way. They have the ability to either deny or destroy messages, which can cause serious disruptions. The performance of the network may be further complicated by some nodes displaying partial malicious activity, which causes interference on a selective basis. In response to these difficulties, this work suggests a novel technique for identifying and mitigating the effects of malicious nodes coordinating assaults inside DTNs. Through simulations with the Opportunistic Network Environment (ONE) simulator, the efficacy of the scheme was assessed, demonstrating its ability to reduce the disruptive impact of malicious nodes on network operations. This suggested method shows promise in strengthening DTNs against malevolent attacks and promoting more dependable communication infrastructures in places affected by disasters. © 2024 IEEE.

关键词： Delay tolerant networks

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Pedestrian Trajectory Prediction with Missing Data: Datasets, Imputation, and Benchmarking 38

Pedestrian Trajectory Prediction with Missing Data: Datasets...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chib, Pranav Singh Singh, Pravendra Department of Computer Science and Engineering Indian Institute of Technology Roorkee India

Pedestrian trajectory prediction is crucial for several applications such as robotics and self-driving vehicles. Significant progress has been made in the past decade thanks to the availability of pedestrian trajectory datasets, which enable trajectory prediction methods to learn from pedestrians' past movements and predict future trajectories. However, these datasets and methods typically assume that the observed trajectory sequence is complete, ignoring real-world issues such as sensor failure, occlusion, and limited fields of view that can result in missing values in observed trajectories. To address this challenge, we present TrajImpute, a pedestrian trajectory prediction dataset that simulates missing coordinates in the observed trajectory, enhancing real-world applicability. TrajImpute maintains a uniform distribution of missing data within the observed trajectories. In this work, we comprehensively examine several imputation methods to reconstruct the missing coordinates and benchmark them for imputing pedestrian trajectories. Furthermore, we provide a thorough analysis of recent trajectory prediction methods and evaluate the performance of these models on the imputed trajectories. Our experimental evaluation of the imputation and trajectory prediction methods offers several valuable insights. Our dataset provides a foundational resource for future research on imputation-aware pedestrian trajectory prediction, potentially accelerating the deployment of these methods in real-world applications. Publicly accessible links to the datasets and code files are available at https://***/Pranav-chib/TrajImpute. © 2024 Neural information processing systems foundation. All rights reserved.

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Image Segmentation Techniques for Bone Cancer Identification in X-ray and MRI Imagery 2

Image Segmentation Techniques for Bone Cancer Identification...

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2nd IEEE International Conference on Networks, Multimedia and Information technology, NMITCON 2024

作者： Likitha, M. Shidaganti, Ganeshayya I Ramaiah Institute of Technology Department of Computer Science and Engineering Bangalore India

ISBN: (纸本)9798350372892

Integrating computer science with biology is crucial for advancing bone cancer diagnostics, particularly in distinguishing between benign and malignant tumors in X-ray and MRI images. The rapid evolution of medical imaging technology necessitates updated diagnostic methods to meet modern healthcare demands. Advanced image processing techniques, such as Watershed segmentation, are pivotal in precisely defining tumor boundaries. This research focuses on enhancing bone cancer detection by evaluating various machine learning methods and optimizing image clarity. Techniques like KAZE feature extraction, combined with XGBoost algorithms, demonstrate superior accuracy in identifying malignant regions compared to traditional classifiers such as CNN, ADABoost, and Random Forest. By integrating these advanced techniques, the ability to differentiate between benign and malignant tumors is significantly improved, leading to enhanced diagnostic accuracy and early detection rates. This approach holds promise for revolutionizing clinical practices and improving patient outcomes through more effective and automated diagnostics. © 2024 IEEE.

关键词： Magnetic resonance imaging

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