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1st International Conference on Innovative engineering sciences and Technological Research, ICIESTR 2024

作者： Wilfred Blessing, N.R. Pyingkodi, M. Wasim Haidar, S.K. Palarimath, Suresh Paravathavarthini, S. Balakumar, C. Sheeja Kumari, V. Sutherlin Subitha, G. Seldev Christopher, S. University of Technology and Applied Sciences-Ibri College of Computing and Information Sciences Ibri Oman Kongu Engineering College Department of Computer Applications Erode India University of Technology and Applied Sciences-Salalah College of Computing and Information Sciences Salalah Oman Kongu Engineering College Department of Computer Technology Erode India Karpagam Academy of Higher Education Department of Computer Science Coimbatore India Saveetha Institute of Medical and Technical Sciences Department of Computational Intelligence Chennai India Stella Mary's College of Engineering Department of Computer Science and Engineering Aruthenganvilai India St. Xavier's Catholic College of Engineering Department of Computer Science and Engineering Nagercoil India

ISBN: (纸本)9798350348637

Precision agriculture, driven by advancements in technology, aims to optimize farming practices by utilizing data and technology to enhance efficiency, productivity, and sustainability This research introduces an innovative pilot investigation centered on the convergence of two revolutionary technologies are the Internet of Things (IoT) and Deep Learning. With a specific focus on advancing precision agriculture, the primary objective of this study is to evaluate the combined effects of data collection facilitated by IoT and analytical capabilities of deep learning in enhancing and optimizing various agricultural processes. The integration of IoT in agriculture has revolutionized data acquisition, employing an array of sensors to monitor critical parameters such as soil moisture, temperature, and crop health. Concurrently, Deep Learning, a subset of artificial intelligence, exhibits the potential to glean actionable insights from voluminous datasets, offering advanced analytics and predictive capabilities. This study investigates the practical implementation and efficacy of this integration in a controlled agricultural setting. Sensors strategically positioned in the pilot study capture real-time data, while deep learning algorithms process and analyze this information. The primary objectives include evaluating the effectiveness of this combined technology in optimizing irrigation schedules, predicting crop yields, and identifying anomalies in crop health. Preliminary findings underscore the transformative potential of IoT and Deep Learning, empowering farmers with real-time data for informed decision-making. Key considerations encompassed in the study include IoT Sensors, Deep learning algorithms, and user adoption. The research not only sheds light on the technical intricacies of the integration but also delves into the challenges and opportunities inherent in merging these technologies within the agricultural landscape. As agriculture transitions towards the next

关键词： Smart agriculture

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Segmentation of Retinal Pigment Epithelium Layer in Retinal OCT Images 5

Segmentation of Retinal Pigment Epithelium Layer in Retinal ...

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5th IEEE International Conference on Advances in Electronics, computers and Communications, ICAECC 2023

作者： Sheeba, T.M. Antony Raj, S. Albert Anand, M. SRM Institute of Science and Technology Kattankulathur Faculty of Science and Humanities Department of Computer Applications Chennai India SRM Institute of Science and Technology Kattankulathur Faculty of Engineering and Technology Department of Networking and Communications Chennai India

ISBN: (纸本)9798350305449

Optical Coherence Tomography (OCT) is a relatively recent procedure for studying eyes that has proven to be quite beneficial. We propose automatic detection of the RPE (Retinal Pigment Epithelium) layer in Retinal OCT images in this work. The bottom layer of the retina in retinal OCT images is known as the RPE layer, which is extracted in this study and may be studied to diagnose retinal disorders. After effectively increasing the quality of the OCT image using the Wiener filter, we extract the Retinal Pigment Epithelium layer using the Threshold approach. The proposed approach is tested using 40 OCT images of the retina. The results demonstrate 95.50 percent accuracy in RPE layer segmentation. © 2023 IEEE.

关键词： Optical tomography

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AI Based Security for Internet of Transportation Systems 4

AI Based Security for Internet of Transportation Systems

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4th International Conference on Smart Electronics and Communication, ICOSEC 2023

作者： Kousar, Heena Fatima, Shaista Ahmed, Syed Imran Sajithra, S. Kushwaha, Sumit Balaji, N. Alangudi East Point College of Engineering and Technology Department of Computer Science and Engineering Karnataka Bengaluru560049 India Mj College Department of Electronics and Communication Engineering Telangana Hyderabad500053 India R.M.K. Engineering College Department of Computer Science Engineering Tamilnadu Kavaraipettai601206 India University Institute of Computing Chandigarh University Department of Computer Applications Punjab 140413 India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Andhra Pradesh Vaddeswaram522302 India

ISBN: (纸本)9798350300888

The intelligent transportation systems (ITS) use a combination of sensors, cameras, and communication technologies to provide real-time data on traffic conditions. This data can be used to adjust traffic lights, but it can also be used to provide information to drivers, such as real-time traffic updates and recommended routes. Overall, control of traffic lights is an effective solution to address traffic congestion problems in urban areas. By optimizing traffic flow, cities can reduce travel times, improve safety, and reduce emissions from idling vehicles. However, the success of these systems depend on the effective planning, implementation, and ongoing maintenance to ensure that they continue to meet the changing needs of the city. In this work, the interest is to control of traffic lights in order to solve problems related to congestion;in this sense, it is proposed five mechanisms to make traffic lights intelligent and not automatic based on machine learning. The mechanisms implemented are based on four algorithms of machine learning: decision tree (DT), support vector machine (SVM), k nearest neighbor (KNN) and random forest (RF). The solutions obtained make it possible to predict the decision in order to act in traffic depending on several parameters such as the road, cars, passengers etc. © 2023 IEEE.

关键词： Traffic congestion

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Digital Forensics and the Law: A computer engineering Perspective 15

Digital Forensics and the Law: A Computer Engineering Perspe...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Agarwal, Gaurav Bhatt, Rahul Pathak, Paresh Kumar, Ashwani Kumar, Anup Pandey, Sakshi Invertis University Department of Computer Science & Engineering Uttar Pradesh Bareilly243123 India Dev Bhoomi Uttarakhand University School of Engineering and Computer Uttarakhand 248007 India IIMT University School of COMPUTER Science and Applications Meerut India K. R. Mangalam University School of Engineering & Technology Department of Computer Science & Engineering Haryana Gurugram122103 India ARKA JAIN University Department of Engineering & IT Jharkhand Jamshedpur India Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350370249

Digital Forensics and the law is an interdisciplinary subject that studies the legal implications of digital evidence in the context of legal guidelines, rules governing investigations, and data-driven tactics for preventing, detecting, and remediating crimes. It explores how the use of virtual forensics techniques impacts the legal manner in crook and civil instances and the expert obligation of those chargeable for using these strategies. The scope of virtual Forensics and the regulation additionally covers the felony implications of the generation-aided discovery system utilized in pursuing investigations into online activity and the capacity to find evidence for which there's no bodily trace or era platform to get admission. Ultimately, the implications of municipal, state, and federal laws governing digital Forensics are also examined. © 2024 IEEE.

关键词： Impacts Municipal Remediating Techniques Utilized

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Security of Cloud Computing Using Quantum Zero-Knowledge Proof System 1st

Security of Cloud Computing Using Quantum Zero-Knowledge Pro...

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1st International Conference of IoT and its applications, ICIA2020

作者： Kumar, Surya Bhushan Mandal, Ranjan Kumar Mukherjee, Kuntal Dwivedi, Rajiv Kumar Department of Computer Applications VBU University Jharkhand Hazaribag India Department of Computer Science and Engineering Ranchi University Jharkhand Ranchi India Department of Computer Science and Engineering BIT Mesra Jharkhand Ranchi India

ISBN: (纸本)9789811676369

Security and privacy play a significant role while using cloud computing. Users can use cloud services, if and only if they find it trust worthy. Users can outsource their sensitive data to cloud environment provided they fill that their data is secured at cloud environment. The popularity of cloud computing depends upon how it can provide the security and privacy of users’ valuable data. Thus, a secured cloud computing is the basic agenda prior to adoption of cloud computing. It is doubtless that the security of cloud computing can only be achieved provided by users of it is the authentic one. In this context, this endeavor has proposed the unique mechanisms to verify the authenticity of the users of the cloud. Apart from this trivial concept of verifying the authenticity, the Zero-Knowledge Proof concept has been introduced here. Furthermore, the concept of quantum mechanics has been incorporated here to provide Zero-Knowledge Proof Engine (ZKE) more effective. Thus, a quantum-based ZKE framework along with algorithm is the main concept of this manuscript. An experimental result has been shown to lay bare the effectiveness of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Cloud security

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Evaluation of Subjective Answers Using Machine Learning 3

Evaluation of Subjective Answers Using Machine Learning

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3rd International Conference on Issues and Challenges in Intelligent Computing Techniques, ICICT 2022

作者： Sneha, G. Shreya Shree, G. Babu, Tina Nair, Rekha R. School of Engineering Dayananda Sagar University Department of Computer Science and Engineering Bengaluru India School of Engineering Dayananda Sagar University Department of Computer Applications Bengaluru India

ISBN: (纸本)9781665482684

Negative methods are currently used to evaluate subjective writing. The evaluation of the subjective responses is an essential responsibility. When a human analyses anything, the evaluation's quality can change depending on the person's emotions. All outcomes in machine learning are solely dependent upon the user's input data. To address this issue, our suggested method combines machine learning (ML) and natural language processing (NLP). To analyse the subjective response, our algorithm performs tasks including tokenizing words and phrases, classifying parts of speech, chunking and chinking, lemmatizing words, and word netting. Our suggested approach also offers the context's semantic meaning. There are two modules in our system. Extracting data from scanned photos is the initial step. Then arranging it properly, and the second is using ML and NLP to analyse the text obtained in the previous phase and assigning grades to it. © 2022 IEEE.

关键词： Machine learning

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Client Segmentation and Customization in E-Commerce: applications of Machine Learning from a Management Perspective

Client Segmentation and Customization in E-Commerce: Applica...

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2024 International Conference on Communication, computer sciences and engineering, IC3SE 2024

作者： Sriprasadh, K. Palit, Shamik Pravallika, B. Manjunatha Lenka, Reena Singla, Atul Srm Institute Of Science And Technology Faculty Of Science And Humanities Department Of Computer Science And Applications Chennai Vadapalani India University Of Stirling Rak Campus Department Of Computing Science And Software Engineering United Arab Emirates Institute Of Aeronautical Engineering Dundigal Hyderabad India New Horizon College Of Engineering Department Of Mechanical Engineering Bangalore India University Pune India Lovely Professional University Phagwara India

ISBN: (纸本)9798350366846

A machine learning-based e-commerce personalised recommendation system helps address the issue of information overload that inevitably arises when consumers have more and more options in e-commerce, leading to an increasingly complex structure. Customer segmentation is a crucial component of contemporary marketing strategy since it enables companies to effectively adjust their advertising campaigns and customise their communications with customers. Through the analysis of enormous quantities of consumer behaviour data and the discovery of trends that can be utilised for dividing customers into segments, ML algorithms provide a potent tool for automating the procedure of customer segmentation. We report our research and results for identifying a customer's segment. In order to perform machine learning, we integrate the Python Tensorflow library with Pandas for manipulating data frames. To address any discrepancies or gaps in the collected client data, it is refined. Key features are found, and to find hidden patterns and relationships, an exploratory analysis of the data is done. After that, we decide on a suitable machine learning technique to divide the clientele into various groups. We also experiment with several clustering models, including conventional machine learning techniques. © 2024 IEEE.

关键词： Python

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Joint aspect-opinion extraction and sentiment orientation detection in university reviews

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International Journal of Information Technology (Singapore) 2022年第6期14卷 3213-3225页

作者： M, Devi Sri Nandhini Gurunathan, Pradeep Department of Computer Science and Engineering A.V.C. College of Engineering Mannampandal Tamilnadu Mayiladuthurai India Department of Computer Applications A.V.C. College of Engineering Mannampandal Tamilnadu Mayiladuthurai India

With the exponential growth of the Internet and the various forms of social media, the number of people using such platforms to share their views and experiences is also greatly increased. The reviews posted by people regarding a service or entity serve as valuable sources for decision making. However, acquiring valuable insights from the abundantly available unstructured information is not so straightforward. There is a need for more effective methodologies to carry out aspect-based sentiment analysis in a fine-grained manner. The proposed work performs joint aspect-opinion extraction and sentiment orientation detection (JAESOD) using an integrated approach of complex dependency rule-based extraction and proper treatment of various forms of adjectives that contribute to the effective extraction of subjective sentiment-bearing terms. The objective of the proposed work is three-fold: to extract aspect-sentiment word pairs, to detect implicit aspects, and to perform sentiment orientation detection at the aspect level along with sentiment scoring. In this paper, we propose a novel methodology to classify the sentiment orientation on a fine-tuned nine-point scale in contrast to the existing three to five-point scale found in the literature works. Concerning the task of sentiment detection, the goal of the proposed work is to achieve optimality in sentiment classification by giving due weightage to the modifies or intensifiers based on their degree of intensification and fair treatment of crucial factors such as double intensification, extended forms of adjectives, negations, sentiments expressed in numerical forms that play a vital role in fine-tuning the sentiment orientation detection. © 2022, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer applications and Management.

关键词： Appositive adjectives Aspect-based sentiment analysis Aspect-sentiment word extraction Degree of intensification Implicit aspect extraction Nonary scale of sentiment strength Sentiment orientation detection

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Enhanced Image Concealment: Leveraging LSB and Brotli Compression 3

Enhanced Image Concealment: Leveraging LSB and Brotli Compre...

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3rd International Conference for Advancement in Technology, ICONAT 2024

作者： Yacoob, Shaik Sabnis, Vikrant Jain, Jay Kumar Mansarovar Global University MP Bhopal India Mansarovar Global University Faculty of Engineering and Technology Department of Computer Science MP Bhopal India Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics and Computer Applications MP Bhopal India

ISBN: (纸本)9798350354171

Steganography a cryptographic technique enables the concealment of confidential data within multimedia files like images and videos. This method facilitates the exchange of encrypted information through unconventional means, ensuring that only intended recipients can decipher the hidden message. The LSB technique has attracted significant attention in the literature due to its capability to conceal secret message bits within the least significant parts of image pixels. Although LSB has performed well in image steganography, there is some way to go in terms of its development, especially in the ways in which messages are dealt with. This paper presents a new technique of image steganography basing on the LSB method and the BROTLI algorithm. It uses the compression algorithm to compress message text. After that, to scramble the text, one LSB embedding technique is then used to hide the compressed text in the cover image. Applying the methods of data analysis on the benchmark images, the new approach developed outperforms the standard methods that exist at the present stage. This supports the idea of performing BROTLI on data with a view of compressing the data to be embedded in LSBs. © 2024 IEEE.

关键词： Steganography

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Privacy-Preserving Human Activity Recognition in Smart Homes using Deep Learning and Edge Computing for Real-Time Processing 5

Privacy-Preserving Human Activity Recognition in Smart Homes...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Mariappan, K. Gopal, Baskar Parthasarathy, J. Sreekanth, G.R. Nanmaran, R. Jegajothi, B. SRM Institute of Science and Technology Department of Computer Science and Applications Chennai India College of Engineering University of Technology & Applied Science Nizwa Oman Computer Science & Engineering Jayalakshmi Institute Of Technology Dharmapuri India Information Technology Nandha Engineering College Erode52 India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India SRS Tech Solutions Chennai India

ISBN: (纸本)9798331530013

The rapid advancement of smart home technologies necessitates efficient human activity recognition (HAR) systems while ensuring user privacy. This research presents a novel architecture that integrates deep learning and edge computing for real-time HAR, emphasizing privacy protection. By employing differential privacy methods and federated learning, the approach minimizes personal data exposure risks. Using the UCI HAR dataset, the model achieves an accuracy of 96.50%, with precision, recall, and Fl-score metrics of 95.75%, 96.20%, and 95.97%, respectively. The results indicate high accuracy and adaptability to varying smart home environments, while edge computing ensures minimal latency. This work highlights the importance of balancing performance and privacy in HAR systems, paving the way for secure smart home applications. Additionally, it points to the potential of deep learning in medical diagnostics, suggesting future research could enhance diagnostic capabilities across various medical imaging modalities, including CT scans and MRIs, to improve healthcare outcomes. © 2024 IEEE.

关键词： Smart homes

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