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Blockchain Based Cloud Storage System

Blockchain Based Cloud Storage System

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International Conference on Trends in Electronics and Informatics (ICEI)

作者： V. Sri Sairam M. Sathish Kumar Master of Computer Applications Hindustan Institute of Technology & Science Chennai Tamilnadu

ISBN: (数字)9798331544607

ISBN: (纸本)9798331544614

The rapid growth of data generation in various sectors, including healthcare, finance, and enterprise, has created an ever-increasing demand for secure, scalable, and cost-effective cloud storage solutions. Traditional cloud storage systems usually encounter difficulties with data security, privacy, and centralized control, leading to problems such as unauthorized access to data, data breaches, and single points of failure. Hence, we propose an active revocation-based three-factor Maka scheme for granting user access control using Schnorr signatures and provide a formal proof for the security of our scheme under the random oracle security model, which shows that our scheme can achieve certain requirements in the multi-server scenario. Performance evaluation shows that the proposed scheme is suitable for computation resource-limited mobile devices. Full-fledged implementations under meaningful performance assessments confirm the viability of the proposed scheme.

关键词： Performance evaluation Cloud computing Technological innovation Public key Passwords Medical services Market research Mobile handsets Blockchains Servers

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Classification and Discovery of Brain Tumor Using Ensemble Deep Learning 2

Classification and Discovery of Brain Tumor Using Ensemble D...

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

作者： Srilatha, Kasanagottu Sravani, Chekka Vasudevarao, Mikkilineni Kiran, Ajmeera Chitra, D. Reddy, Pundru Chandra Shaker Geethanjali College of Engineering and Technology Department of Data Science Hyderabad India Marri Laxman Reddy Institute of Technology and Management Computer Science and Engineering Telangana Hyderabad India Dayananda Sagar Business Academy Department Of Computer Applications Karnataka India Mlr Institute of Technology Computer Science and Engineering Dundigal Telangana Hyderabad India Panimalar Engineering College Master of Business Administration Tamil Nadu Chennai India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal India

ISBN: (纸本)9798350372892

As healthcare IT develops at a dizzying rate, the age of big data is drawing near. Tumor disease prediction, monitoring, diagnosis, and treatment are all profoundly affected by data mining and analysis. Brain-tumors(BT) are considered the most terrible and deadly disease due to their aggressive nature, short survival rate, and diverse spectrum of symptoms. Inadequate medical therapy for patients with misdiagnosed brain tumors lowers their survival prospects. The ability to differentiate amid abnormal &normal tissues makes BT detection extremely difficult. An accurate diagnosis allows the patient to undergo effective therapy and has a better chance of long-term survival. Because of the abnormal pattern of lesion distribution, there are still convinced difficulties in diagnosing BTs, even though there has been a lot of research on the subject. Because tiny areas may seem healthy, it might be challenging to locate a zone with few lesions. Classification accuracy is negatively impacted, and selecting and extracting useful characteristics becomes more difficult. An important part is the automated categorization of precancerous BTs utilizing deep &machine learning strategies. This study presents a Convolutional-Neural-Network-Long-Short-Term-Memory (CNN-LSTM) model, a hybrid DL-technique, for the purpose of MRI-based brain tumor classification and prediction. An MRI brain imaging collection is utilized for our experiments. An effective preprocessing of the data is the first step in using a CNN to extract major characteristics from images. With a 99.3% significant classification accuracy, 99.2% precision, 99.1% recall, and 99.1% F1-measure, the suggested model successfully predicts the brain tumor. © 2024 IEEE.

关键词： Convolutional neural networks

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Spice Vision: Deep Learning Enhanced Spice Classification System 3

Spice Vision: Deep Learning Enhanced Spice Classification Sy...

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3rd IEEE North Karnataka Subsection Flagship International Conference, NKCon 2024

作者： Pujari, Laxmi Belavatgi, Meenaz Sajjan, Mahantesh M. Kamatar, Vishwanath Surasura, Pradeep Ammanagi, Nausheen K.L.E. Institute of Technology Dept. of Master of Computer Applications Hubballi580027 India K.L.E Institute of Technolog Dept. of Computer Science and Engineering Hubballi580027 India

ISBN: (纸本)9798350364569

Spices are admired for their distinctive flavors, fragrances, and health advantages. They are an essential part of industrial uses, medical procedures, and culinary customs. The automatic classification of 15 different varieties of Indian spices which are essential to both Indian food and traditional medicine is the main subject of this study. To guarantee quality and consistency, these spices underwent a thorough data preparation procedure after being painstakingly gathered. Then, Convolutional Neural Network (CNN) models were trained and tested using the preprocessed dataset. As a family of deep learning algorithms, CNNs are particularly good at classifying images because they extract hierarchical characteristics directly from the input. The study highlights the possibility for automated systems to precisely detect and classify distinct spices based on their visual properties and investigates the efficacy of various CNN architectures for spice classification. Gathering and preprocessing photos of the spices was part of the model creation procedure. Based on how well these models classified the test images, their performance was assessed. The accompanying picture illustrates the findings, which demonstrate the different degrees of accuracy attained by each model. © 2024 IEEE.

关键词： Convolutional neural networks

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Cricket shot classification using Deep Learning 2

Cricket shot classification using Deep Learning

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2nd IEEE Global Conference on Communications and Information Technologies, GCCIT 2024

作者： Kallur, Shanta Thotad, Puneeth N. Yaligar, Suman Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Computer Science & Engineering Hubballi580027 India Kle Institute of Technology Affiliated With Visvesvaraya Technological University Dept. of Master of Computer Applications Hubballi580027 India K.L.E. Institute of Technology Dept. of Computer Science and Engineering Hubballi India Visvesvaraya Technological University Belagavi India

ISBN: (纸本)9798350388916

Cricket is a popular sport that involves various batting techniques and shot types. Accurate and real-time detection of cricket shots can provide priceless visual images for coaches, players, and analysts. This study proposes an artificial intelligence-based approach for cricket shot detection aimed at automatically recognizing and classifying different shots played by batsmen. Images are extracted and pre-processed to enhance their quality. The pre-processed images extract key features like bat trajectory, player movement, and batsman position. A training dataset is used to enable shot classification, consisting of labeled samples of different cricket shots. Neural network models, such as a convolutional neural network, VGG16, VGG19, are trained using an image set to learn the patterns and characteristics of each shot type. The model is then employed to classify shots in real time for predicting the corresponding shot type based on the selected features. The proposed approach is evaluated on a diverse dataset of cricket match images, encompassing a broad extent of shots played by professional batsmen. Execution metrics such as accuracy, precision, recall, and F1-score are used to assess the effect of the shot detection system. A VGG16 model in cricket shot prediction of the dataset yielded an impressive accuracy of 95.81%. The solution establishes the efficiency of the planned approach in accurately detecting and classifying cricket shots in real-time. The developed system has the potential to enhance cricket coaching, player performance analysis, and broadcast coverage by providing valuable insights into the shot selection and execution of batters. © 2024 IEEE.

关键词： Convolutional neural networks

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Implementing Smart Grids via Cognitive Radio: An Overview

Implementing Smart Grids via Cognitive Radio: An Overview

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Kalra, Bharti Prakash, Febin Sharma, Himanshi Tula's Institute Department of Computer Science and Engineering Dehradun India Department of Computer Bangalore India Noida Institute of Engineering and Technology Master of Computer Applications Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350357769

Calidad of service issues related to antiquated communication frameworks in conventional electrical grids are addressed by the cognition smart grid connectivity paradigm. By incorporating cutting-edge communication and information technology into electric power lines, this novel strategy promotes a two-way exchange of information. However, there is a significant need for SG communications systems to manage large data volumes because to the rise in wireless services and apps, which is being driven by the growing adoption of Internet interconnected Things smart devices. Moreover, our work surpasses the state of the art in that it not only tackles the problems caused by current communication systems but also provides a proactive solution that foresees and handles the growing data needs resulting from the changing wireless application and Internet of Things (loT) landscape. The suggested hybrid design fosters adaptation and resilience in the face of rapidly advancing technology breakthroughs, marking a key step to future-proofing SG communication systems. © 2023 IEEE.

关键词： 5G technology and Fog Computing Cognitive Radio Internet of Things (loT) Quality of Service (QoS) Smart Grid

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Low-Cost Sensor-Based Irrigation System for Efficient Water Management in Remote Agricultural Regions

Low-Cost Sensor-Based Irrigation System for Efficient Water ...

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2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023

作者： Ashok, P. James, T. Prabahar Godwin Pathmanaban, J. Ranjith, P. Ragunthar, T. Baranidharan, K. Sri Sai Ram Institute of Technology Department of Computer Science and Engineering Tamilnadu India Sri Sai Ram Institute of Technology Department of Science and Humanities Tamilnadu India Srm Institute of Science and Technology Department of Computing Technologies Tamilnadu India Sri Sai Ram Institute of Technology Department of Master of Business Administration Tamilnadu India

ISBN: (纸本)9798350394580

Irrigation system with lower cost is considered to be increasingly significant, based on rising growth of human population, demand for food and global warming. This investigation attempts to provide a low cost sensor based irrigation system for monitoring and controlling from remote regions and to optimize water use for farming irrigation. A sensor based irrigation system that monitors and controls the supply of water uses sensors and actuators. It is specifically designed to automate the water supply from reservoirs and to crops over irrigation system. System lifecycle and waterfall model has been considered in development factor. The protocol suite, ardunio are integrated to provide the system model. Here, pumping approach has been utilized to provide water supply to soils. This system design offers sensing, power supply, monitoring and controlling factors and internet sources. Experimentation was carried out in MATLAB environment and simulation outcomes demonstrate practical and effectual way of implementing the anticipated system model. This irrigation system is need not only for farmers and also for increasing the economic functionalities. Moreover, this may diminish manual supervision that is needed for water supply by invoking remote monitoring and control system. © 2023 IEEE.

关键词： Irrigation system low cost remote monitoring sensor water supply

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Relevance Feature Discovery in Text Mining Using NLP 3

Relevance Feature Discovery in Text Mining Using NLP

引用

3rd International Conference for Emerging technology, INCET 2022

作者： Naik, Darshana A Mythreyan, S Seema, S Ramaiah Institute of Technology Department of Computer Science and Engineering Bengaluru India Ramaiah Institute of Technology Department of Master of Computer Applications Bengaluru India

ISBN: (纸本)9781665494991

The volume of text that is created and formed each day is dramatically increased and increasing. This colossal sum or volume of profoundly unstructured text cannot be basically and effortlessly handled and seen by computer systems. Therefore, we can get an useful patterns from the text using an efficient and effective techniques and algorithms. Text mining is a strategy for dissecting text records to extricate valuable data and knowledge from the text document, analysis is done using Text mining. Mining contains a few exercises that assists you with deriving data from unstructured text information. Before you'll apply diverse text mining procedures, you might want to begin with text preprocessing, which is that the act of cleaning and rebuilding text information into a usable organization. This training could likewise be a center part of Natural Language handling (NLP) and it generally includes the utilization of strategies like language identification, tokenization, filtering, lemmatization and stemming to design information suitably for examination. At the point when text preprocessing is finished, after that you will apply text mining calculations to get experiences from the information. However, ensuring the excellent of chose highlights from message is a test because of the monstrous measure of insignificant (boisterous) data in message report is a major test, to guarantee the norm of found significance highlights in the message archives for portraying client inclinations because of huge scope terms and information designs. © 2022 IEEE.

关键词： Data mining

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A Hybrid Approach for Detecting and Preventing Security Attacks in MANETs

A Hybrid Approach for Detecting and Preventing Security Atta...

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2023 International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023

作者： Rajalakshmi, D. Immaculate, P.S. Madhumitha, D. Saravanan, D. Rajeswari, S. Pandi, S. Senthil Sri Sai Ram Institute of Technology Computer Science and Engineering Chennai India Sri Sai Ram Institute of Technology Master of Business Administration Chennai India Pavendar Bharathidasan College of Engineering & Technology Computer Science and Engineering Trichy India K.Ramakrishnan College of Engineering Information Technology Trichy India Rajalakshmi Engineering College Computer Science and Engineering Chennai India

ISBN: (纸本)9798350300857

MANETs are new sort of organizational network that works in a profoundly powerful and unusual atmosphere. Though the flexible nature of MANET is attractive, the dynamic topology of MANETs brings in numerous threats. The existing Intrusion Detection Systems (IDS) doesn't strictly detect the generation of attacks namely Black Hole, Gray Hole, Worm Hole and additional routing attacks. Added to it, the traditional methods of protection techniques won't be sufficed as MANETs are capable of providing network even in the absence of fixed infrastructure. In this proposed system introduces a new technique to detect these attacks and also prevent the generation of attacks by using Black Hole, Gray Hole, Neuro-Fuzzy based (BGNF) based Intrusion Detection System. This BGNF based Particle Swarm Optimization (PSO) helps to optimize and minimize the false alarms and data loss. © 2023 IEEE.

关键词： Intrusion detection

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Diagnosing Phytophthora infestans infestations on Solanum tuberosum leaves using Machine Learning Classifiers

Diagnosing Phytophthora infestans infestations on Solanum tu...

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2023 International Conference on Computational Intelligence and Sustainable Engineering Solution, CISES 2023

作者： Kumar, Pijush Kanti Kumar, Indrajeet Kumar, Sanjeev Kumar, Padmanavan Harshith, John Dutta, Anurag Government College of Engineering and Textile Technology Department of Information Technology Serampore India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India G L Bajaj Institute of Technology and Management Department of Master of Computer Applications Greater Noida India Kalinga Institute of Industrial Technology Department of Computer Science and Engineering Bhubaneswar India Vellore Institute of Technology Department of Computer Science and Engineering Vellore Campus Vellore India Government College of Engineering and Textile Technology Department of Computer Science and Engineering Serampore India

ISBN: (纸本)9798350323917

In reaction to recent technology breakthroughs, modern computational fields such as the IoT, Data science, Machine Learning, AI, and several more have arisen. Humans benefit greatly from these fields. In this study, we would employ parts of machine learning to forecast plant illnesses. Particularly amongst the potato leaves, severe Late Blight Disease was discovered. The potato plant, also known as Solanum tuberosum, is a significant crop that is planted all over the world and yields copious amounts of nutritious tubers. The potato is a frequent staple food and has several health benefits. When ingested in moderation, the fluid extracted from tubers can cure stomach ulcers and reduce inflammation and acidity. Both Early and Late Blight are dangerous and widespread potato diseases. We would concentrate on Late Blight in this effort. A fatal fungus that harms potatoes is called Late Blight. It is accessible everywhere in the world. It can be found in areas of the world where potatoes are grown. Winter is the primary growing season for potatoes in India. It is followed by sweltering summer months in the lowlands. Heat and dryness kill the fungus that lives in the soil. The fungus Phytophthora Infestans, the most prevalent agricultural disease that causes the Late blight of potatoes, can quickly result in crop failure if appropriate control measures are not adopted. Potato crop declines in epidemic years might be as high as 80%. We acquired a collection of roughly one thousand samples of Late Blight-affected leaves of potatoes for this investigation. With the help of conventional classifier algorithms, we have developed a machine-learning classification paradigm that may be able to forecast the onset of Late Blight disease. Medical science has developed significantly. Plant pathology could be able to prevent the threat from happening if we could perhaps predict the illness in its early stages. © 2023 IEEE.

关键词： Diseases

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Lake Ice Infrared Image De-noising using Deep Learning Convolutional Neural Network 4

Lake Ice Infrared Image De-noising using Deep Learning Convo...

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4th International Conference on Emerging Research in Electronics, computer science and technology, ICERECT 2022

作者： Bharathi, P.T. Kiran, K. Prashanth, G.K. Virupakshaiah, H.K. Karegowda, Asha Gowda Siddganga Institute of Technology Department of Computer Science & Engineering Tumakuru India Siddganga Institute of Technology Department of Master of Computer Applications Tumakuru India

ISBN: (纸本)9781665456357

Elimination of noise from the acquired images is one of the fundamental tasks so as to restore the high quality of images to increase the results of segmentation, classification, recognition, detection etc. In this research work, Infrared imaging is used to capture the images with various temperature ranges during day and night time. Infrared thermography is used as a nondestructive image acquisition sensor. So infrared imaging sensors are used to identify and classify different ice types formed on the rivers during winter season in western countries. However, you can observe blur and degradation in the acquired images through infrared imaging, as the climatic nature is involved in the process. These factors affect the quality of the image and also the quantitative analyses of the images. Especially, when there are deeper faults which are found in an under laying substance and high thermal conductivity materials can also be studied. Acquired infrared images are usually affected with noises. In this paper, de-noising of infrared images is carried out using convolution neural network model in deep learning. In this work Gaussian white noise is added to the infrared images with varying noise intensities from 1% to 10% and then the convolution neural network model is applied to de-noise the infrared images. In the qualitative analysis of the infrared images edge factor, uniform region, texture, smoothness, non-uniform region, structure of the region of interest is considered. For quantitative analysis, mean square error, peak-signal- noise-ratio and structure similarity index measure is used. The results of traditional methods of image de-noising are compared with convolution neural network based method. It is concluded from the experimental work that the convolution neural network based method proves to be better for removal of Gaussian white noise when compared with traditional methods. © 2022 IEEE.

关键词： Mean square error

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