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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Shukla, Aasheesh Pundir, Sumit Rao, A Kakoli Shalini, N. Akhil Kumar, Rohit Rana, Ajay GLA University Computer Engineering and Applications Mathura India Graphic Era University Department of Computer Science & Engineering Dehradun India Lloyd Institute of Engineering and Technology Greater Noida India Institute of Aeronautical Engineering Department of Computer Science and Engineering Hyderabad India Lloyd Law College Greater Noida India Lloyd Institute of Management and Technology Greater Noida India Amity University Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350304480

A variety of difficulties while attempting to transmit a file including multimedia data through the many peers of a wireless IoT network. The system's centralization, which increases the risk of a security breach and makes it harder for users to access their information, is one of the biggest issues. It would be possible to address the issues with security and user accessibility, but doing so would forfeit scalability, low latency, and lengthy response times. The project in issue uses the Interplanetary File System and an original block chain approach as a direct result of this. Additionally, we discussed the system's workings and the ideas that led to its development. This research looks at a broad range of uses for economic and pricing models, which are often known as methods for intelligent, logical decision-making. Building adaptive protocols and algorithms that are designed expressly for use with WSNs is the ultimate objective of this study. Our objective is to create an atmosphere that inspires smartphone users to engage. The findings show that SATS has the potential to lower communication and computation costs, enabling it to provide affordable data transfer. The SATS system's calculation costs are as follows: AN: 14%, 23%, and 59%;Fog-Node: 6.5%,21.5%, and 51%;and Sensor Node and AN: 6%, 3%, and 4%, respectively. This is a reasonable price when compared to the costs of PPDAS, IDAP, and ASAS, which are 14%, 23%, and 59%, respectively. According to the findings, the suggested SATS has a greater storage capacity, lower computation costs, lower communication costs, and more effective energy utilization when compared to other systems that belong to the same category. When compared to other existing systems. © 2023 IEEE.

关键词： Communication Technologies Crowdsensing Network Internet of Things Smart Devices Wireless Sensor Networks

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Malware Classification using LSTM-CF Framework for Disk Forensic Analysis 2

Malware Classification using LSTM-CF Framework for Disk Fore...

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Thirupathi, Chaithanyaka Yeddeli Sundaram, Jawahar Yeo, Kheng Cher Khan, Shujahat Ali Pritika Sellappan, Devaraju Energy and Resources Institute Charles Darwin University Faculty of Science and Technology Darwin Australia Christ Academy Institute for Advanced Studies Department of Computer Science and Applications Bengluru India Charles Darwin University Faculty of Science and Technology Darwin Australia School of Computing Science and Engineering VIT Bhopal University Bhopal India

ISBN: (纸本)9798350383652

In the current era, digital forensic investigators need specialized tools to extract digital footprints from hard disks. The number of crimes is rising dramatically. This has resulted in an increased number of unresolved cybercrime cases including malicious software, hacking and cyberfraud. Disk forensics or disk investigation is a massive task. It takes weeks to collect the traces of the 1TB hard drive and analyze them. It is a challenging task to detect reliable evidence because of the worldwide use and advancement of digital communication technologies. By using automated tools, only predictable areas of the disk are often investigated, and as a result, latent evidence in the hidden area might be missed. In this paper, we present Chaithu's Framework (CF) that gives us a clear picture of the steps to follow and implement a machine learning algorithm which is able to predict whether the request is related to malware or not by analyzing the large world Microsoft Malware Prediction dataset collected from Kaggle. It consists of a total of 7.8 million data samples with 84 features collected in real time from Windows 7,8,9 and 10 systems. Using predictive machine learning algorithms, frauds can be detected automatically and autonomous actions can be taken to prevent them. © 2024 IEEE.

关键词： Hard disk storage

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computer Vision Algorithms for Surgical Assistance in Cloud-Based Telemedicine 1

Computer Vision Algorithms for Surgical Assistance in Cloud-...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Sharma, Himanshu Bordoloi, Dibyhash Deepak, A. Mayuri, K. Dwivedi, Shashi Prakash Kumar, Navneet Gla University Department of Computer Engineering and Applications Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Institute of Aeronautical Engineering Department of Computer Science and Engineering Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

In order to improve surgical support, this study investigates the incorporation of algorithmic techniques for computer vision into a stored in-cloud telemedicine platform. We concentrate on the technical facets of this cutting-edge healthcare paradigm using an interpretive ideology and deductive methodology. Utilizing secondary gathering of information from credible sources, an exploratory approach is used. The study assesses the privacy and safety precautions necessary to protect patient data while it is being sent and stored. Access controls, strong encryption techniques, and regular safety inspections are cited as essential elements. These steps promote system confidence by ensuring adherence to laws governing the safeguarding of healthcare data. The research also evaluates the use of algorithms for machine vision for surgical navigation. The algorithms are adept at tracking instruments, identifying anatomical structures, and enhancing visibility, supporting surgical teams throughout real-time. Depth awareness is a key component of precision surgical procedures, and stereoscopic visual abilities improve the sense of depth. Customer acceptance and satisfaction are crucial to the achievement of telemedicine-assisted operation. Both healthcare professionals and patients appreciate the system's use, trustworthiness, and clarity of communication. For a flawless user experience, appropriate technical assistance and training are considered as being essential. In its conclusions, the paper makes suggestions for future research, highlighting the necessity of empirical investigations, sophisticated encryption methods, and the incorporation of cutting-edge technology like AR. © 2023 IEEE.

关键词： internet machine learning models medical teams technology videoconferencing

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AI-enabled Crop Health Monitoring and Nutrient Management in Smart Agriculture 6

AI-enabled Crop Health Monitoring and Nutrient Management in...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Swarnkar, Suman Kumar Dewangan, Leelkanth Dewangan, Omprakash Prajapati, Tamanna Manishkumar Rabbi, Fazle Shri Shankaracharya Institute of Professional Management and Technology Department of Computer Science and Engineering Raipur India Swangi Wardha India Kalinga University Department of CS & IT Chhattisgarh Raipur India Department of Computer Science Shri D. N. Institute of Computer Applications Gujarat India Australian Computer Society Australia

ISBN: (纸本)9798350304480

The rapid advancement of artificial intelligence (AI) has paved the way for transformative applications in various fields, and agriculture is no exception. In this research paper, we explore the integration of AI technologies in smart agriculture to enhance crop health monitoring and nutrient management. By leveraging AI algorithms and techniques, we aim to improve the efficiency and sustainability of farming practices. The paper delves into the utilization of AI for early detection of crop diseases and pests, as well as the development of predictive models for precise nutrient management. Remote sensing and imaging technologies play a crucial role in real-time monitoring, allowing for informed decision-making. Through a series of case studies and evaluations, we demonstrate the effectiveness and potential benefits of AI-enabled crop health monitoring and nutrient management in modern agriculture. Despite the promising results, challenges and limitations are discussed, and future directions for research and application are suggested. © 2023 IEEE.

关键词： AI-enabled agriculture artificial intelligence crop diseases crop health monitoring machine learning nutrient management pest detection precision farming real-time monitoring remote sensing smart agriculture

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Fog Restoration in Hazy Images using Deep Transfer Learning

Fog Restoration in Hazy Images using Deep Transfer Learning

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2024 International Conference on Smart Devices, ICSD 2024

作者： Agrawal, Navin Kumar Sharma, Viboo Singh, Pranita Sachi, Savya Jain, Arpit Alam, Md Mohtab GLA University Department of Computer Engineering and Applications U.P. Mathura India Hindustan College of Science and Technology Department of IT U.P. Mathura India Roorkee Institute of Technology Department of Computer Science and Engineering U.K. Roorkee India L N Mishra College of Business Management Department of Information Technology Bihar Muzaffarpur India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering AP Vaddeswaram India Eklavya University Department of Computer Science & Engineering M.P. Damoh India

ISBN: (纸本)9798350394412

Issues like low vision and the distortion of spectral and spatial information in photographs can be brought on by unfavorable atmospheric circumstances including smog, rain, overcast sky, fog, and smoke. This piece looks at images taken in a hazy environment. While de-smogging requires veil and transmission map data, optical imaging systems are limited to capturing smoggy images. To restore blurry photographs, effective optical information prediction is necessary. Research has shown that optical information anticipated by the dark channel prior technique frequently produces subpar images in photographs with areas of higher brightness, noticeable pollution gradients, or textured information. In order to estimate optical information, this study makes use of Deep Transfer Learning (DTL) and Optimized Gradient Prior Prediction (OGPP). We gathered benchmark images of both smoggy and smog-free environments in order to train the DTL model. The OGPP technique uses the smog gradient, which the DTL model is taught to anticipate, to recover pictures free of pollution. A comparison analysis shows that when it comes to recovering hazy photos, the OGPP restoration model performs better than other approaches. © 2024 IEEE.

关键词： Restoration

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Architecture Dataware Housing and Related Issues

Architecture Dataware Housing and Related Issues

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2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Jain, Upma Alabdeli, Haider Bansal, Saloni Deepak, A. Singla, Atul Veena, C.H. Kaushik, Rohit Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq Department of Computer Engineering and Applications GLA University Mathura India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Tamilnadu Chennai India Lovely Professional University Phagwara India Institute of Aeronautical Engineering Dundigal Hyderabad India Faculty of Management SRM Institute of Science and Technology NCR Campus India

ISBN: (纸本)9798350355338

In today's fast-paced business environment, making informed decisions is crucial for success. To achieve this, decision-makers are increasingly turning to data-oriented and business intelligence databases. As a result, data warehousing is becoming a popular tool for managing and analyzing information. In essence, data warehousing is an operational system that processes an organization's day-to-day transactions and stores present and historical data from various sources in a centralized computer system. This enables organizations to gain insights and make informed decisions based on their data. In this study, the architecture, classification, and challenges associated with data warehousing have been explored to provide a thorough understanding of this essential technology. © 2024 IEEE.

关键词： Data warehouses

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Efficacious Intrusion Detection on Cloud Using Improved BES and HYBRID SKINET-EKNN 7th

Efficacious Intrusion Detection on Cloud Using Improved BES ...

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7th International Conference on Emerging Research in Computing, Information, Communication and applications, ERCICA 2022

作者： Kumar, C. U. Om Bhama, Ponsy R. K. Sathia Prasad School of Computer Science & Engineering Vellore Institute of Technology - Chennai Campus Chennai600127 India Department of Computer Technology MIT Campus-Anna University Chennai600044 India Department of Computer Applications NITTE Meenakshi Institute of Technology Bangalore560064 India

ISBN: (纸本)9789811954818

The in-built insecurity of a network of systems has made it vulnerable to attacks from cyber criminals. In the current scenario, the most complex vulnerability threat is posed by Botnet that forms a large volume of illegitimate network traffic. It causes security issues such as organizing snap bending traps, abusing extraordinary computational assets, accessing client data, sending spam messages and DDoS attacks, all of which are caused by botnets. With an intention of addressing the issues in DDoS attack, the present study adopted a novel approach to effectively predict the malware attack on the virtual machine (VM). One aspect of novelty in this approach is the maiden venture of adopting the concept of streamlining network efficiency by using an enhanced packet information algorithm (EPIA), the other novelty included adopting an improved bald eagle search (IBES) optimization strategy. The actual implementation is performed on JAVA platform through three datasets NSL-KDD 99, UNSW-NB15 and N-BaIoT datasets. An overview of the overall performance of the three datasets clearly shows that IBES_HSKiNET_EKNN has recorded a peak performance in every matrix through an average accuracy of 99.6% and detection rate of 99.3%. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Botnet

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Identification and Segmentation of Polyp Using Deep Learning Approaches 4

Identification and Segmentation of Polyp Using Deep Learning...

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4th IEEE Asian Conference on Innovation in Technology, ASIANCON 2024

作者： Yaligar, Suman Kallur, Shanta S. Patil, Vijeeta Thotad, Puneeth N. K. L. E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India K. L. E. Institute of Technology Dept. of Master of Computer Applications Hubballi India

ISBN: (纸本)9798350354218

Colorectal cancer (CRC) is a major health problem, and accurate detection of polyps plays a major role in its prevention and treatment. In the past, people often relied on manually extracted lower-level features such as color, texture, and shape, which often had issues capturing global context and lacked robustness to complex scenarios. With the rise in deep learning, more and more rigorous segmentation of medical images dependent on deep learning networks have emerged, making significant progress in this field. This paper aims to develop an efficient model for polyp detection and segmentation using machine learning techniques. The model will assist clinicians in accurately locating and segmenting polyp regions, which will help in the prevention and treatment of CRC. The work will first review traditional methods that are dependent manually on extracted features and deep segmentation algorithms, then detail benchmark datasets relevant for the topic. The two datasets Kvasir-SEG and CVC-ClinicDB are used for implementation. Specifically, we carried out a comprehensive evaluation of recent deep learning CNN methods. The model gave an accuracy with Pranet method of about 99% in polyp segmentation and identification respectively. © 2024 IEEE.

关键词： Convolutional neural networks

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Correction to: Sustainable edge computing with mobile crowd computing: a proof-of-concept with a smart HVAC use case

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The Journal of Supercomputing 2024年第18期80卷 26685-26686页

作者： Pramanik, Pijush Kanti Dutta Pal, Saurabh Mukhopadhyay, Moutan Choudhury, Prasenjit School of Computer Applications and Technology Galgotias University Greater Noida India Department of Computer Science and Engineering Brainware University Kolkata India Department of Computer Science and Engineering Bengal Institute of Technology Kolkata India Department of Computer Science and Engineering National Institute of Technology Durgapur India

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Integration of Clinical and Imaging Data for Enhancing Oral Cancer Detection Using Deep Learning

Integration of Clinical and Imaging Data for Enhancing Oral ...

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2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024

作者： Shakila, M. Dhasaratham, M. Saranya, N. Naga Thotapalli, Aruna Telrandhe, S.V. Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India Tkr College of Engineering and Technology Department of Information Technology Hyderabad India Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Cvr College of Engineering Department of Information Technology Telangana Hyderabad India Guru Nanak Institute of Engineering & Technology Department of Etc Nagpur India

ISBN: (纸本)9798350360660

This study aims at unlocking the integration of clinical and imaging data using the deep learning model for enhancing the diagnosis of oral cancer cases. Therefore, for image analysis, in this paper proposed to major on CNNs while for clinical data, our Research used RNNs and hence came up with a unique architecture. The proposed model was trained on the patients' records and Histopathological Images Database from 5000 patients. As for the overall measure of accuracy of all of the constructed classifiers applying our integrated model the result was 94 %. Specificity achieved for the system was 7% while sensitivity was 93% for the oral cancer. 2%, sensitivity of 69.1%. This is an improvement of the previous methods that required either imaging or clinical data to obtain accuracy approximated at 85-90 percent. As a unique effectiveness factor of increasing the accuracy of oral cancer diagnosis, it is proposed to use an integration of clinical and imaging data through deep learning, which can also result in inclusion of the diagnosis at the early stages of the disease, thereby enhancing the prognosis of the illness. © 2024 IEEE.

关键词： Lung cancer

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