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International Conference on Energy, Power and Environment: Towards Sustainable Growth (ICEPE)

作者： Chitra Sabapathy Ranganathan Arshadh Ariff Mohamed Abuthahir Riazulhameed G. Swathi Pavithra M.R S. Senthil C. Srinivasan Associate Vice President Mphasis Corporation Chennai Tamil Nadu India Architect - Data Engineer Blackstraw Technologies Pvt Ltd Chennai Tamil Nadu India Assistant Professor Department of Computer Science and Engineering CMR Technical Campus Hyderabad Telangana India Assistant Professor Department of Management Studies Rajalakshmi School of Business Chennai Tamil Nadu India Assistant Professor Department of Mathematics Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Biomedical Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798350379297

ISBN: (纸本)9798350379303

This research paper offers a groundbreaking study on precision nutrition, an intelligent Internet of Things ecosystem that aims to transform the control of nutritional intake. The system provides continuous nutritional analysis and individualized health insights due to its integration of cutting-edge sensor technology with data analytics. Real-time monitoring of the food and drinks' nutritional content is made possible by the ecosystem thanks to a network of intelligent wearable devices equipped with spectroscopic and ion-selective sensors. The technology delivers tailored health insights and dietary recommendations by using algorithms for machine learning and an extensive library of nutrients. In this article, the architecture, sensor integration, data processing, and validation procedures of the Internet of Things ecosystem are further detailed. The suggested system is accurate and effective, as shown by the results of extensive validation studies. The findings of this study highlight the potentially game-changing role that the Internet of Things (IoT) ecosystem may play in enabling people to improve their health via educated dietary decisions, contribute to the development of precision nutrition research, and maximize their health results.

关键词： Data analysis Ecosystems Transforms Propulsion Nutrients Real-time systems Internet of Things

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Texture-based Feature Extraction and Machine Learning Model for the Detection of Acute Lymphoblastic Leukemia

Texture-based Feature Extraction and Machine Learning Model ...

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Mobile Computing and Sustainable Informatics (ICMCSI), International Conference on

作者： Rangini M Sumit Pundir Manzoore Elahi M. Soudagar Daxa Vekariya Harshal Patil S. Rajkumar Assistant Professor Department of Computer Science and Engineering R.M.D. Engineering College Kavarapettai Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of VLSI Microelectronics Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India Department of Computer Science & Engineering Parul Institute of Engineering and Technology Parul University Post Limda Gujarat India Computer Science and Engineering Symbiosis Institute of Technology Symbiosis International (Deemed University) Pune Maharashtra India Department of Mechanical Engineering Faculty of Manufacturing Institute of Technology Hawassa University Ethiopia

Acute Lymphocytic Leukaemia (ALL) is a dangerous malignancy that not only attacks adults but also contributes to roughly 25% of children. A precise and on-time diagnosis of cancer is a critical prerequisite for effective therapy to enhance survival rates. It is challenging to identify cancer cells from normal cells because leukemic cells seem extremely identical to normal cells beneath the microscope. Normally, morphological analysis for the detection of ALL is conducted physically on blood cells by qualified medical individuals, which has various drawbacks, like a lack of medical experts, slow analysis, and prognosis which depends on the knowledge of medical professionals. For this reason, we provide a robust method for improving the reliability of ALL identification using feature extraction and a Machine Learning (ML) model to discriminate between cancerous and healthy cells in images. To extract texture features for analysis, we employ the First Order Histogram (FOH) and the Gray-Level Co-occurrence Matrix (GLCM) in our technique. Logistic Regression (LR), Naive Bayes (NB), and Artificial Neural Network (ANN) are used in the categorizing method. The simulation results demonstrate that combining GLCM feature extraction with the ANN model yields the highest accuracy, correctly predicting 595 out of 600 data samples. The accuracy was 99.17%, which is quite remarkable.

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A Robust Statistical CNNCTC-Based AI Model for Tracking and Monitoring COVID-19 1st

A Robust Statistical CNNCTC-Based AI Model for Tracking and ...

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1st International Virtual Conference on Industry 4.0, IVCI4.0 2020

作者： Chelliah, Balika J. Arunkumar, S. Prabavathi, R. Aarthi, B. Department of Computer Science and Engineering SRM Institute of Science and Technology Ramapuram Campus Chennai India Assistant Professor Sri Sairam Institute of Technology West Tambaram Chennai India

ISBN: (纸本)9789811612435

The world today battles the new coronavirus (COVID-19) spread that can lead to a major endemic or pandemic outbreak with devastating outcomes which has overturned all our usual calculus seemingly. Because of the unavailability of a vaccine and the serious extent of infectiousness, social distancing is perhaps the only way left to fight the infection. The tracking system is still a challenge for monitoring the COVID-19 affected person and the community spread. This leads to an epidemic situation, where the number of secondary infections keeps going higher and higher which intern leads to the difficulty of tracking and treating each infected person. An AI-based mathematical modelling is proposed for tracking and monitoring the pandemic outbreak. A GPS tracking system, geolocation using Fit bit and Wi-Fi tracking modules are developed to locate the secondary infections and the carriers. Calculation of real-time coordinates and the conditional probabilities with respect to time is done followed by the classification based on risk using CNN connectionist temporal classification (CTC) model, a type of neural network which is used to train the recurrent process. Monitoring the affected person using sensors like thermal sensors and a tracking chip inbuilt in a wristband for real-time updates. A comparative study is done on various classification models. Therefore, the system provides a robust model with 96.5% accuracy to combat this pandemic situation. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Enhancing Plant Leaf Disease Identification with a CNN and DenseNet Hybrid Model 23

Enhancing Plant Leaf Disease Identification with a CNN and D...

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Proceedings of the 5th International Conference on Information Management & Machine Intelligence

作者： Sukhvinder Singh Dari Shweta Tiwaskar Jambi Ratna Raja Kumar Vaishali Langote Gopal Madhukar Mohadikar Rashmi Ashtagi Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) Pune India Assistant Professor Computer Engg. VIIT Pune Maharashtra Assistant Professor Computer Engg. VIIT Pune Maharashtra India Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra Department of Computer Engineering Genba Sopanrao Moze College of Engineering Balewadi Pune Maharashtra India Department of Polytechnic Dr. Vishwanath karad World Peace University Pune Maharashtra Department of Polytechnic Dr. Vishwanath karad World Peace University Pune Maharashtra India Tolani Maritime Institute Induri Talegaon Dabhade Maharashtra Tolani Maritime Institute Induri Talegaon Dabhade Maharashtra India Department of Computer Engineering Vishwakarma Institute of Technology Bibwewadi Pune Department of Computer Engineering Vishwakarma Institute of Technology Bibwewadi Pune India

ISBN: (纸本)9798400709418

The identification of plant leaf diseases is a essential section of agriculture, serving as a critical measure in mitigating the reduction in crop productivity. This study presents a novel methodology for improving the precision of plant leaf disease detection. This is achieved by employing a hybrid architecture that combines Convolutional Neural Networks (CNNs) and DenseNet models. The main aim of our study is to enhance the accuracy and effectiveness of disease detection by leveraging the combined capabilities of Convolutional Neural Networks (CNNs) and DenseNet. These two deep learning architectures are recognized for their proficiency in extracting and reusing features. In this study, we conduct a comparative analysis of our proposed hybrid model by evaluating its performance against traditional Convolutional Neural Networks (CNNs) as well as other advanced models such as Recurrent Neural Networks (RNNs) and Capsule Networks (CapsNets). The findings of our study demonstrate that the hybrid model, combining CNN and DenseNet architectures, showed an accuracy rate of 98.79%. This performance surpassed that of other models under comparison. The hybrid model demonstrated higher levels of precision, recall, F1 score, and AUC-ROC values, shows its effectiveness in the identification of plant leaf diseases. This study highlights the potential of hybrid deep learning models in the domain of precision agriculture and emphasizes the importance of integrating diverse architectural paradigms for accurate disease identification. The research conducted in this study makes a significant contribution to the progress of agricultural technology by providing a dependable mechanism for the early detection of diseases. As a result, this research contributes to the enhancement of crop health and the promotion of food security.

关键词： Image processing

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Design, Modelling, and Analysis of Novel Solar PV System using MATLAB

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Materials Today: Proceedings 2022年 51卷 756-763页

作者： Ayan Banik Anurag Shrivastava Ravindra Manohar Potdar Sanjiv Kumar Jain Santosh Gopal Nagpure Mukesh Soni National Institute of Technical Teachers' Training & Research Kolkata India Principal and Professor ECE Lakshmi Narain College of Technology and Science Indore India Associate Professor Department of E&TC Engineering Bhilai Institute of Technology Chhattisgarh India Assistant Professor Department of Electrical Engineering Medi-Caps University Indore India Assistant Professor Department of E&TC Engineering D. Y. Patil School of Engineering Academy Pune India Research Scholar Department of Computer Science and Engineering Jagran Lakecity University Bhopal India

In order to meet the world's future energy demand, alternative renewable green energy supplies must be widely promoted. Among all DERs, the most prevalent is Solar PV for a number of important reasons. In this study, the authors analyze several elements of the Single-Phase Solar Photovoltaic Rooftop System with MATLAB. In order to analyze the characteristics of the output current, a new grid synchronization methodology has been devised. For maximum power tracking, the INC control technique has been adopted. The output of the solar DC power system has been synchronized with the grid through hysteresis current loop control. Extensive testing verifies the practicality of the proposed power grid model and promising outcomes and results.

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Handling tactful data in cloud using PKG encryption technique

Handling tactful data in cloud using PKG encryption techniqu...

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4th Smart Cities Symposium (SCS 2021)

作者： B.C. Gajarla A.V. Rebba K.S. Kakathota M. Kummari S. Shitharth Student Department of Computer science and EngineeringVardhaman College of Engineering Assistant Professor Department of Computer science and EngineeringVardhaman College of Engineering

With the availability of the cloud storage services, users can store data to the cloud and have the access to share the data to others. The cloud file may contain some sensitive information, this information shouldn't be exposed to others. The possible solution could be to encrypt the whole file which is shared before sending it to the cloud and to generate the signature to verify the encrypted file. Then, upload the encrypted file and signatures to the cloud. This enables the sensitive information to be private and only the owner can decrypt the file. Hence, it is not feasible to use by others. Distributing the decryption is the possible way to access it by others. However, it is not the efficient solution to implement this method in real scenarios. In order to overcome this approach, an efficient solution is implemented. In this new approach, sensitive information is kept private and can easily execute without any complications. Here, a sanitizer is used to sanitize the block which contains the sensitive information of the file. Firstly, the user binds the data blocks with respect to the sensitive information of the original data and generates a digital signature to that block and then sends it to the sanitizer. The sanitizer filters these data blocks and also sanitizes the data corresponding to the sensitive information. It also evaluates the corresponding signatures into valid ones. The signature is used to prove that the cloud truly possesses the data blocks. The third-party auditor is a public verifier, it verifies the data stored in the cloud on behalf of the user. This method supports the data sharing so that the sensitive information is protected in the cloud.

关键词： cryptography data integrity digital signatures storage management cloud computing

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Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images

IAENG International Journal of Computer Science

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IAENG International Journal of computer Science 2021年第3期48卷 1-9页

作者： Desiani, Anita Erwin, Member Suprihatin, Bambang Yahdin, Sugandi Putri, Ajeng I. Husein, Fathur R. Assistant Professor of Mathematics Department and Phd candidate of Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Computer Engineering Department Computer Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Associate Professor of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Head of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia Undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia An undergraduate student of Mathematics Department Mathematics and Natural Science Faculty Universitas Sriwijaya Indralaya30662 Indonesia

This paper is concern about segmentation on pap smear images to use for classification cervical cancer. A cervical cancer screening by recognizing the cell shape pattern on the Pap-smear image can provide information about the presence of cervical cancer Pap-smear. The manual screening process for classifying cells is a challenging endeavor prone to the risk of error. There are several studies on the classification of cervical cell images which are sometimes not segmented first. Segmentation is very important to get the features contained in the image of cervical cells including the cell nucleus and cytoplasm. However, the segmentation results are also determined by the quality of images. This study proposed 2 paths that had a combination of image segmentation and classification. Before segmentation, image enhancement was carried out to improve image quality using Normalization, CLAHE and Adaptive Gamma Correction. The first path, the segmentation based on the CNN architecture. The second path is a classification process to test the segmentation results used by applying the KNN and ANN methods. The accuracy (ACC) result at the segmentation path was used to measure the match between the location of the segmented pixels and the results on ground truth, while the accuracy at the classification path was the success of the machine in classifying or predicting the segmentation results based on the class label for each group according normal and abnormal Pap-smear's label. The ACC result obtained in segmentation was only 0.77. However, the results of the segmentation when used in classification to classify which Pap-smears were normal and which are abnormal could provide excellent accuracy results, which were above 0.9. The results of the performance of Sensitivity (SN), Specificity (SP) and F1-score on the segmentation path also only gave results below 0.72, but the performance on the classification path that used the results on the segmentation path gave well results, wh

关键词： Image enhancement

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Optimized fuzzy C-means clustering algorithm for cotton fibre quality analysis

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Materials Today: Proceedings 2021年 45卷 2082-2086页

作者： R. Dhanapal D. Selvapandian Assistant Professor Department of Computer Science and Engineering Karpagam Academy of Higher Education Coimbatore 638107 India

Exact determination of cotton fibre quality is a huge help for turning factories to purchase cotton as per the least prerequisites. The nature of cotton fibre is decided on numerous elements, for example, staple length, development, finesses, neatness, tenacity , and quality, to make reference to. The essential target of the framework is to accomplish the institutionalization at various levels in the cotton production network the board in the conveyed mechanical systems condition with exact estimation and characterization procedures. The current framework is executed in information mining procedures to foresee the nature of cotton fibre and to establish the most noteworthy connection between's short fibre file and yellowness. The proposed framework incorporates a bunching strategy to group cotton quality fibre. The proposed optimized principal component analysis (OPCA), and Fuzzy C-means bunching in huge dataset. These proposed calculations are executed inside the Map-Reduce method and cuckoo searching technique is applied for reduces the time complexity it is implemented in Hadoop stage to anticipate dampness content, micronaire, extension, and group quality.

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IoT Based Human Biometric System Using Alcohol Level Detection for Driver Security

IoT Based Human Biometric System Using Alcohol Level Detecti...

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Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), International Conference on

作者： Anurag Sinha Nitish Kumar Amrita Ticku Vyom Modi Meghal Shah Kapil Shrivastava School of Computing and Information Sciences IGNOU New Delhi India Department of Computer Science Lovely Professional University Phagwara Punjab India Bharti Vidyapeeth's College of Engineering India India Faculty of Engineering Marwadi Education Foundation Rajkot Gujarat India Department of Computer Science & Engineering Charotar University of Science & Technology Gujarat India Assistant professor Department of computer engineering and applications GLA University mathura India

In today's society, driving after intoxication has become a major factor in traffic accidents. It is common knowledge that operating a vehicle while intoxicated is a major contributing factor in a significant number of accidents. The effects of drunk driving go beyond the drunk driver; they also affect other drivers and passengers in the same vehicle. As a result, this growing threat needs to be addressed immediately. Our project's goal is to create a system that uses the Internet of Things (IoT) to find traces of alcohol in someone's breath and apply preventive measures in order to effectively address this issue. Although there are laws against drunk driving, it is difficult to enforce them widely because law enforcement cannot constantly be present on the roads to spot drunk drivers. This restriction increases the chance of accidents significantly. In order to reduce future risks, a trustworthy system that can recognize and discourage drunk drivers is urgently needed. Drunk driving deaths are increasing, so we need to figure out how to reduce these kinds of deaths. The breath analyzing sensor used in the suggested system is the MQ-3, which calculates the breath alcohol content of an individual. The amount of alcohol that is allowed in the blood in India is 0.03. The engine won't start if the person's blood alcohol content is higher than this limit, making it impossible for them to drive. Additionally, the system will send an alert, possibly through a Raspberry Pi, to a friend or relative, providing the exact location using GPS coordinates. This notification serves as a precautionary measure to keep others informed about the situation. Implementing such a system can contribute significantly to reducing accidents related to alcohol consumption. In conclusion, detectors like the one proposed in this project hold immense significance in preventing the loss of life and property caused by reckless driving under the influence of alcohol.

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Machine Learning in K–12 Educational Settings

Machine Learning in K–12 Educational Settings

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Communication, Computing and Energy Efficient Technologies (I3CEET), International Conference on

作者： Ramy Riad Al-Fatlawy Dadamuxamedov Alimjon R. Udayakumar Ashish Sharma Jaspreet Kaur S. Padmanaban Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Senior Lecturer at the Department of "Modern Information and Communication Technologies" of International Islamic Academy of Uzbekistan Tashkent Uzbekistan Dean CS & IT Kalinga University India Department of Computer Engineering and Applications GLA University Mathura India Assistant Professor Department of Computer Science Engineering Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Mohali Punjab India Department of Science and Humanities Karpagam Institute of Technology Coimbatore India

ISBN: (数字)9798331541583

ISBN: (纸本)9798331541590

The many real-world applications of machine learning-based approaches over the past several decades have shown the potential of data-driven methodology in a variety of computing fields. Higher education computer curricula are beginning to include machine learning, and more and more institutions are incorporating it into K–12 computer instruction. Given the growing prevalence of computational learning in K–12 computer teaching, it is imperative to investigate how agency and intuition develop in these kinds of settings. However, considering the challenges educators and schools already have in integrating conventional learning, understanding the challenges connected with teaching algorithms for learning across grades K–12 provides an even more challenging barrier for computer education research. the curriculum's incorporation of artificial intelligence and *** article outlines the potential for data mining education in grades K–12. These advancements include modifications to technology, philosophy, and practice. The study puts the present findings in the larger framework of computing education and discusses some differences that K–12 computer educators should keep in mind while tackling this issue. The study focuses on key components of the fundamental shift that is required in order to successfully integrate machine learning into more thorough K–12 computing curricula. A critical first step is to abandon the notion that next-generation computational thinking requires rule-based, "traditional" programming.

关键词： Training Technological innovation Philosophical considerations Education Machine learning Learning (artificial intelligence) Market research Telecommunication computing Programming profession Next generation networking

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