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A Cyber Physical System Enabled Intelligent Farming System with Artificial Intelligence, Machine Learning and Cloud computing 14

A Cyber Physical System Enabled Intelligent Farming System w...

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14th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2023

作者： Moses, Diana Kumar, T. Praveen Varalakshmi, Sharada Pamulaparty, Lavanya Methodist College of Engineering and Technology Hyd India

In this proposal, we study the advances of major core technologies and their applicability in creating an Intelligent farming System (IFS). As the world is trending into new technologies and implementations it is a necessary goal to trend up with agriculture also. Cyber Physical System (CPS) plays a very important role in Smart Farming. IOT sensors are capable of providing information about agriculture or Farming fields. We have proposed a Cyber Physical System (CPS) enabled smart agriculture system using different technologies like AI&ML, data science and Cloud computing. This CPS based Intelligent Farming system makes use of sensor networks that collects data from different sensors which as a result develop an Intelligent Village Farming. Several Utilities such as Pest management, Crop Stress management, Nutrient management, Water management and Deep Analysis can be done to suggest the farmer regarding the crop and climatic conditions. This smart agriculture or Smart Farming using Cyber Physical System (CPS) is powered by advances in sensor technology, wireless communication technologies and their applicability to farming Chatbot, Computer vision, technology enabling farming, it consists of sensor followed by technological techniques. © Grenze Scientific Society, 2023.

关键词： Cloud computing

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IoT-Enabled Black Box for Driver Behavior Analysis Using Cloud computing

IoT-Enabled Black Box for Driver Behavior Analysis Using Clo...

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2024 international conference on advances in data Engineering and Intelligent computing Systems, ADICS 2024

作者： Jegan, J. Suguna, M. Raja Shobana, M. Azath, H. Murugan, S. Rajmohan, M. School Of Technology The Apollo University Department Of Computer Science And Engineering Andhra Pradesh Chittoor India Rmk College Of Engineering And Technology Department Of Computer Science And Engineering Tamil Nadu Chennai India Srm Institute Of Science And Technology Department Of Networking And Communication Chennai Tamil Nadu Kattankulathur India School Of Computing Science And Engineering Vit Bhopal University Bhopal India Saveetha School Of Engineering Saveetha Institute Of Medical And Technical Sciences Saveetha University Department Of Computer Science And Engineering Tamil Nadu Chennai India Hindustan Institute Of Technology And Science Department Of Electronics And Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350364828

The Internet of Things (IoT)-powered black box for advanced driver behavior analysis and vehicle safety is a revolutionary strategy for improving driver behavior and road safety via the use of black boxes powered by the Internet of Things. This project captures realtime data on driving behavior, encompassing characteristics such as speed, acceleration, braking, and adherence to lanes via the seamless integration of IoT technologies into the vehicle's black box. It does this by providing in-depth insights into drivers' behaviors, which encourages adopting safe driving practices thanks to the advanced data analytics it employs. In addition to its behavior monitoring capabilities, the IoT-driven black box enhances the vehicle's safety by including collision detection technologies. If an accident occurs, the system immediately sends an alert, communicating the severity of the collision and the exact GPS locations to the emergency services, which speeds up the process of responding to the incident. This attempt combines cutting-edge Internet of Things capabilities with vehicle safety to develop responsible driving behaviors and, eventually, a safer highway environment for all parties involved. This invention can modify driving norms thanks to its comprehensive approach, highlighting the potential of technology-driven solutions in promoting safer roadways. © 2024 IEEE.

关键词： Vehicle safety

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data Deduplication using Machine Learning 15

Data Deduplication using Machine Learning

引用

15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Revanth, Narasimhula Varun, Ravuri Harshitha, Ebbili Angelina, J. Jane Rubel Subhashini, S.J. Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Tamil Nadu Virudhnagar India Department of IT SRM Madurai College for Engineering and Technology Tamil Nadu Madurai India

ISBN: (纸本)9798331300579

Reducing data in storage systems is becoming more crucial as a practical way to cut down on data Center management expenses. Current post-deduplication delta-compression techniques combine delta compression with lossless compression and conventional data deduplication to optimize data reduction efficiency. Regretfully,we observe that because of their poor accuracy in identifying similar data blocks, current techniques achieve noticeably lower data-reduction ratios than the optimal. data deduplication is a computing technique that removes duplicate copies of data that repeats. A similar and almost interchangeable term is single-instance data storage. This method will be used to reduce bytes that need to be sent during network data transfers same as to increase storage utilization. During the de-duplication process, distinct data segments, also known as byte patterns, are found and saved after analysis. Further chunks are compare with the stored copy as the analysis progresses, and if a match is found, the redundant chunk is substituted with a brief reference pointing to the stored chunk. Reduced data storage or transfer requirements result from the possibility same as byte pattern occurring hundreds, thousands, or even more times (match frequency depends on chunk size). Comparing data segments to find duplicates is how most recognized methods for data deduplication is implemented. © Grenze Scientific Society, 2024.

关键词： data transfer

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Revolutionizing Healthcare through the Internet of Things: Current State and Future Implications 15

Revolutionizing Healthcare through the Internet of Things: C...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Shantinath, Barwade Rohit Sabnis, Vikrant Jain, Jay Kumar Department of Computer Science Mansarovar Global University Bhopal India Department of Computer Science Faculty of Engineering and Technology Mansarovar Global University Bhopal India Indian Institute of Information Technology Madhya Pradesh Bhopal India

ISBN: (纸本)9798331300579

The Internet of Things (IoT) has the potential to completely transform healthcare by allowing medical professionals to remotely monitor patients, gather data on their activity and health in real-time, and create more efficient treatment regimens. IoT technologies have the potential to save healthcare expenditures while also enhancing patient involvement and improving patient outcomes. However, the adoption of IoMT has been slow, and To ensure that the advantages of IoT are realised while reducing the hazards connected with its use, a number of hurdles must be overcome. The purpose of this research paper is to examine the current state of IoT implementation in healthcare, identify the benefits and challenges of IoMT, and explore the future implications of IoMT. The study starts off with a survey of the literature that has already been written on the use of IoT technology in healthcare, highlighting both the potential advantages and difficulties of doing so. The paper then presents the results of a survey of healthcare providers on the use of IoT technologies in healthcare, providing insights into the current state of IoT implementation in healthcare and the challenges that must be addressed to facilitate its adoption. © Grenze Scientific Society, 2024.

关键词： Patient treatment

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Enhancing Healthcare through the Symbiosis of Blockchain and Machine Learning in Preprocessing Skin Disease Images: A Review 15

Enhancing Healthcare through the Symbiosis of Blockchain and...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Rokade, Sonali Mishra, Nilamadhab School of Computer Science and Engineering Vellore Institute of Technology Bhopal India

ISBN: (纸本)9798331300579

Recent advancements in skin disease identification leverage machine learning for automated diagnosis. However, safeguarding the integrity of sensitive medical data remains a top priority. This paper explores the integration of machine learning and blockchain in skin disease identification preprocessing, promising trust, transparency, and accuracy in dermatological diagnosis. The preprocessing workflow starts with gathering a diverse, labeled skin image dataset, followed by data cleaning and augmentation to enhance quality and quantity. Standardization methods like resizing and normalization ensure data consistency, while addressing class imbalances prevents model bias. Blockchain technology is introduced to secure data integrity. Each skin image and its metadata are encrypted and timestamped on a decentralized blockchain, guaranteeing tamper-proof authenticity. Machine learning models, typically based on CNNs, extract features and classify diseases, benefiting from high-quality preprocessed data. During training, blockchain securely records model parameters and progress, ensuring transparency and auditability, crucial for medical accountability and *** fusion of machine learning and blockchain enhances diagnostic accuracy while addressing data privacy and security concerns, potentially revolutionizing dermatological healthcare with a trustworthy, automated diagnosis system. © Grenze Scientific Society, 2024.

关键词： data integrity

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data Security and Privacy in Cloud computing 14

Data Security and Privacy in Cloud Computing

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14th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2023

作者： Gaikwad, Nileema Prasad Ghadage, Dipali Indrajeet Computer Science & Engineering Rajarambapu Institute of Technology Sakhrale India

In these several previous year, Information technology has constantly been a prey to the data securities. As the data’s are considerably scattering all over globe on the environment of clouds computing, it sort of gives rise to numerous issues. There are two primary reasons because of which users do have concerns regarding data security, privacy about cloud technology. data securities, privacy protections are evidently becomes a important factor for growth in future of cloud computing technologies in any other sectors like government, industroy, businesss, even though, several different approach have been researched on the issues of clouds in sectors like academies as well as business. Hardware as well as the software in the cloud computing architecture are affected due to several concerns regarding data security as well as privacy protections. This case study intended to bring attention over the improvement of data securities and privacies protections for of reliable cloud environments can which had been done by reviewed many securities strategy as well as difficulty from softwares as well as hardware sides in sense to achieve the securities of data. © Grenze Scientific Society, 2023.

关键词： data privacy

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Integrating Tesseract OCR with Large Language Models for Enhancing Textual Understanding 15

Integrating Tesseract OCR with Large Language Models for Enh...

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15th international conference on advances in computing, Control, and Telecommunication technologies, ACT 2024

作者： Sriharsha, A.V. Bhavana, Mekala Tejaswee, Syamala Ahmed, Mynasaheb Bushra Reddy, Peddi Reddi Jaipal Tirupati India Department of Computer Science and Systems Engineering Sree Vidyanikethan Engineering College Tirupati India

ISBN: (纸本)9798331300579

Optical Character Recognition (OCR) is a technology that automatically extracts textual information from document images. An LLM, a large language model, is a neural network designed to understand, generate, and respond to human-like text. These models are deep neural networks trained on massive amounts of text data, sometimes encompassing large portions of the entire publicly available text on the internet. LLMs require textual data to be converted into numerical vectors, known as embeddings since they can't process raw text. Embeddings transform discrete data (like words or images) into continuous vector spaces, making them compatible with neural network operations. The combined strengths of Tesseract OCR's image-based text extraction and the deep language understanding of LLMs enhance the accuracy and depth of textual comprehension. The proposed integrated model establishes a novel paradigm in enhancing textual understanding, progress in document analysis, information retrieval, and various NLP applications. © Grenze Scientific Society, 2024.

关键词： Deep neural networks

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Predicting System Performance and Resource Exhaustion with Machine Learning: Tackling Class Imbalance 1

Predicting System Performance and Resource Exhaustion with M...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Swati, Shipra Kumar, Naween Dubey, Ankit Saini, Vaibhav Sharma, Subham Pranav, Ayushman Bennett University SCSET U.P. Greater Noida India

ISBN: (纸本)9798331518578

This study introduces a robust machine learning pipeline for event classification, utilizing a large-scale dataset from eight Google Borg compute clusters (May 2019) available via Google Big- Query. The dataset includes detailed job submissions, scheduling decisions, and resource usage data, with enhancements such as 5-minute CPU usage histograms, shared resource reservations, and job-parent relationships critical for MapReduce task analysis. data preprocessing involves imputation for missing values, label encoding for categorical data, and SMOTE to address the class imbalance. Feature scaling using StandardScaler and dimensionality reduction via Principal Component Analysis (PCA) retained 95% of the variance. The dataset was divided into training and testing sets, preserving class distribution. Seven machine learning models were evaluated, spanning linear, non-linear, ensemble, and neural network approaches. The Support Vector Machine (SVM) achieved exceptional performance, with 98.0% accuracy, 97.5% cross-validation score, 98.2% precision, 97.8% recall, and 98.0% F1-score. This study highlights the evolution of workloads and resource usage patterns, contributing to developing scalable and efficient event classification frameworks in cloud computing. © 2025 IEEE.

关键词： Cloud computing Event classification Google Borg clusters Machine learning Principal component analysis (PCA) Resource usage patterns Synthetic minority oversampling technique (SMOTE)

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Standardized Post-Quantum Cryptography and Recent Developments in Quantum Computers 1

Standardized Post-Quantum Cryptography and Recent Developmen...

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1st international conference on advances in Computer science, Electrical, Electronics, and Communication technologies, CE2CT 2025

作者： Chib, Shiney Devarajan, Harshitha Raghavan Chundru, Swathi Pulivarthy, Padmaja Isaac, R. Augustian Oku, KrishnaMurthy Meghe Institute of Management Studies Department of Management Datta Maharashtra Nagpur India New York University Department of Computer Science United States Motivity labs INC United States Samsung Korea Republic of Saveetha Engineering College Department of Artificial Intelligence and Machine Learning Tamil Nadu Chennai602105 India AT&T Services Inc Tech Lead Infrastructure/Cloud Architecture Department of Automation platform Development India

ISBN: (纸本)9798331518578

Traditional cryptographic frameworks are seriously threatened by the quick development of quantum computing, especially those that depend on public-key cryptosystems like RSA and ECC. Post-quantum cryptography (PQC) seeks to create cryptographic methods that are impervious to quantum computer assaults in order to overcome this difficulty. The National Institute of Standards and Technology (NIST), which has selected and standardised quantum-resistant techniques like CRYSTALS-Kyber for key encapsulation and CRYSTALS-Dilithium for digital signatures, has sparked recent advancements in PQC. At the same time, the need for reliable PQC solutions has increased due to developments in quantum computing, such as IBM's improvements in qubit stability and Google's 'quantum supremacy' assertion. In order to protect data security in the post-quantum age, PQC regulations must be adopted globally, as highlighted by the intersection of these fields. Current developments in PQC standardisation and the changing state of quantum computing technology are highlighted in this paper. © 2025 IEEE.

关键词： Quantum cryptography

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Prediction of Covid and Covid-19 and Omicron data with ESPRINT, ERF, CSDLM and HMC-19HRP Models 5

Prediction of Covid and Covid-19 and Omicron data with ESPRI...

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5th IEEE international conference on advances in Electrical, computing, Communication and Sustainable technologies, ICAECT 2025

作者： Ramya, V. Thirunavakkarasu, K.S. Vel's Institute of Science Technology and Advanced Studies Department of Computer Science Tamil Nadu Chennai India

ISBN: (纸本)9798350356236

SARS-CoV-2, also known as the Covid-19 Corona Virus, has caused widespread damage globally, and the situation is only becoming worse. Officials from all across the world are using a number of COVID-19 outbreak prediction models to help them make choices and implement pertinent control measures. Simple epidemiological and statistical models are well-liked by the media and have garnered more attention from authorities among the conventional models for COVID-19 global pandemic prediction. It's a pandemic illness that spreads daily from person to person. As a result, it's critical to monitor the total patient population impacted. The way that the computerised data is now provided by the system makes it extremely difficult to analyse and forecast the spread of disease both locally and globally. An enhanced Sprint algorithm, Enhanced Random forest algorithm, Customized sequential Deep learning Model (CSDLM), and HMC-19HRP model is proposed in this study and analysed each with two dataset that is Covid dataset and OMICRON dataset. Finally results obtained from all the method are compared. On overall comparison HMC-19HRP outperforms other methods on both the dataset with precision of 94.92 and 98.26, Recall of 99.03 and 95.86, F1-score of 96.93 and 97.04, Specificity of 78.71 and 97.06 and Accuracy of 94.98 and 96.30 on covid data and Omicron dataset respectively. © 2025 IEEE.

关键词： COVID-19

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