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2024 Ieee International Conference on Innovation and Novelty in engineering and technology, INNOVA 2024

作者： Jadhav, Ambaji s. Pawar, Rashmi Hattaraki, sunilkumar M. bagali, shwetha Patil, Rajeshwari s. Patil, bhagyashree Department of Elecrtical and Electronics Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Electronics and Communication Engineering B.L.D.E. A’s V.P. Dr. P.G.H. College of Engg. and Tech. Vijayapura India Department of Computer Science and Engineering Sri Krishna Institute of Technology Bengaluru India

ISBN: (纸本)9798331505134

Medical image processing is highly developed and critical in the medical field, encompassing methods like X-rays, MRI, and CT scans. These techniques help to identify minute defects in the human body. brain tumors, which disrupt normal brain function, are detected through MRI using segmentation, feature extraction, and classification, processes that are time-consuming and depend on the expertise of clinical professionals. computer-aided technology addresses these challenges. This work aims to diagnose brain tumors accurately and quickly using MRI images. Image preprocessing is done using CLAHe and median filter to enhance contrast and eliminate noise. Thresholding is used for segmentation and a lightweight U-net is used for tumor segmentation, achieving high accuracy and efficiency without extensive training data. Finally, an MsD-CNN classifies the tumor, capturing both local and global features for better image recognition. Our proposed model outperforms over existing methods in terms of accuracy, robustness, and efficiency in brain tumor detection. © 2024 Ieee.

关键词： Median filters

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Machine Learning-driven Quality Control strategies for Industrial IoT Applications 4

Machine Learning-driven Quality Control Strategies for Indus...

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4th Ieee Asian Conference on Innovation in technology, AsIANCON 2024

作者： Dankan Gowda, V. Irshad, Muhammad Aashir Mary, P. Arockia Prasad, Y. Durga Prasad, K.D.V. Tarigonda, Hariprasad Bms Institute of Technology and Management Department of Electronics and Communication Engineering Karnataka Bangalore India Northumbria University 110-114 Middlesex St LondonE17HT United Kingdom V.S.B. Engineering College Department of Information Technology Tamilnadu Karur India Aditya College of Engineering and Technology Department of Computer Science & Engineering Andhra Pradesh Surampalem India Symbiosis Institute of Business Management Hyderabad India Pune India Mohan Babu University Department of Mechanical Engineering School of Engineering Andhra Pradesh Tirupati India

ISBN: (纸本)9798350354218

This paper introduces a new solution for improving Quality of Control in Industrial Internet of Things (IIoT) systems by using a machine learning approach. The planned system utilizes monitoring data originating from different sensors installed in the production line to detect faults as well as estimate maintenance requirements. besides, the proposed system uses the state-of-art machine learning techniques like Convolutional Neural Networks for defect detection and Recurrent Neural Networks for Predictive maintenance with high accuracy and efficiency. computer simulations indicate a high increase in the coverage of defects, predictive capability, and overall product quality. The combination of IoT and machine learning presents a convenient and evolutionary solution that becomes progressively enhanced as more data becomes availab.e. and give a solid framework for the sustained optimization of standards in industrial practices. Future work seeks to enhance the developed models and extend the implemented system's functionalities to other application domains and bigger data sets. © 2024 Ieee.

关键词： smart manufacturing

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solar-Powered Irrigation systems for small-scale Farmers

International Water and Irrigation

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International Water and Irrigation 2025年第1期44卷 7-11页

作者： Chaudhari, Leena bharat Gavali, Ashwini b. Madan, bhagyashree s. Karve, swagat M. Jain, Naveen Prayagi, sunil V. Bharati Vidyapeeth's College of Engineering Lavale Maharashtra Pune India Computer Engineering Department S. B. Patil College of Engineering MH Indapur India Nagpur India E & TC Engineering Department S. B. Patil College of Engineering MH Indapur India Shri Shankaracharya Institute of Professional Management & Technology Raipur India Department of Mechanical Engineering Yeshwantrao Chavan college of Engineering Maharashtra Nagpur India

solar-powered watering systems are becoming a long-term option for small farmers, especially in places where water is scarce and power isn't always availab.e. This research looks at the pros and cons of using solar-powered watering systems in small farms. As the world's need for food grows, farmers must find the best ways to use water while keeping costs as low as possib.e. solar energy is a good option to traditional energy sources. For example, farmers can use the sun's power to run pumps and watering systems more effectively. The study looks at previous research on solar irrigation technologies, with a focus on how they can help increase crop yields, make water management better, and lower greenhouse gas emissions. Case studies from different countries show how solar-powered systems have helped small-scale farms by making it easier to get water, which is important for food growth and long-term viability. In addition, these methods give farms more power by making them less reliant on fossil fuels and more resistant to climate change. even though solar watering systems have many benefits, they are not widely used because of a number of prob.e.s. High starting investment costs, limited access to financing, and farmers' lack of technical understanding are all big prob.e.s that need to be fixed. This study also looks at possib.e.ways to make it easier for small-scale farmers to use solar irrigation systems. some of these are government grants, community-based financial models, and training programs. © 2025 International Water and Irrigation Review. All rights reserved.

关键词： solar energy

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Comparative study and Analysis of Cloud Container technology

Comparative Study and Analysis of Cloud Container Technology

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International Conference on Computing for sustainab.e.Global Development (INDIACom)

作者： e. saravana Kumar Raghu Ramamoorthy selvaraj Kesavan T shobha seema Patil b Vighneshwari Department of Computer Science and Engineering The Oxford College of Engineering Bengaluru India DXC Technology Bengaluru India Department of ISE B.M.S.C.E. Bengaluru India Department of CSE B.M.S.C.E. Bengaluru India Department of Electrical and Electronics Engineering The Oxford College Engineering Bengaluru India

The advent of digital transformation has revolutionized the way businesses operate. Applications have become the focal point of this transformation, shifting the focus from being organization-centric to user-centric. To realize the full potential of businesses, high-quality, secure, and agile applications are essential. Containers are a cutting-edge invention in the world of virtualization, gaining immense popularity in recent years. They have replaced traditional business continuity solutions and are now used to address highly demanding business needs. Multiple containers and orchestration frameworks are availab.e. both as standalone and cloud-based services. However, developers and industry experts face challenges in identifying and evaluating the appropriate containers and orchestration frameworks for their application needs. The selected tools may not always be feasib.e.due to a lack of availab.e.features, inability to provide agility, and lack of platform support. When applications are deployed across multiple containers, coordination and management among container clusters become critical. since containers play a pivotal role in edge deployment, cloud-native, continuous integration, and continuous deployment, it is vital to have centralized orchestration for proper re-source management and scheduling. This paper discusses and compares emerging container platforms and cloud-centric orchestration frameworks, highlighting the challenges involved.

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effective segmentation and Classification of brain Tumour MRI Images Using YOLOv8 Technique

Effective Segmentation and Classification of Brain Tumour MR...

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Innovation and Novelty in engineering and technology (INNOVA), International Conference on

作者： Aniket Prabhu Taradale suvarna Laxmikant Kattimani Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr. P. G. Halakatti College of Engineering and Technology Vijayapura Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

brain tumour classification is crucial for prompt diagnosis and life-saving treatment. A novel approach employing You Only Look Once (YOLO) technology for brain tumour segmentation and classification We used pre-existing data for tumour diagnosis and categorised in four main types: meningioma, glioma, pituitary, and no tumours. In diagnosis, segmentation is necessary to identify regions containing tumours seen in medical images such as MRI scans. Our method offers use of YOLO's real-time product search capabilities and makes them relevant for clinical use. The method achieves 95% Fl-score, 96.20% precision, 93.6% recall, and 97.2% mAP50 in identifying and segmenting tumour regions while using a large dataset of MRI scans as training. This method is superior to traditional segmentation methods as it provides a fast and accurate target detection method.

关键词： YOLO Training Image segmentation Technological innovation Accuracy Magnetic resonance imaging Resonance Real-time systems Medical diagnostic imaging Tumors

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A study on Cloud enab.e. Applications and their security Issues 11

A study on Cloud enabled Applications and their Security Iss...

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11th International Conference on Computing, Communication and Networking Technologies, ICCCNT 2020

作者： Mody, Vidhi Mody, Vrushti Parekh, soham B.Tech. Computer Engineering Mukesh Patel School of Technology Management and Engineering NMIMS India B.E. Computer Engineering Dwarkadas Jivanlal Sanghvi College of Engineering India

ISBN: (纸本)9781728168517

Cloud computing was first established to connect people and data at anytime from anywhere in the 1960s. Today cloud computing is one of the most widely used technologies. Its applications include Multi-cloud, healthcare, testing, finance, data storage and handling big data to increase efficiency and how it differs from traditional systems. We have stated how cloud computing can be used in various domains and have compared its efficiency to traditional systems in terms of data recovery, access control, management, security and cost. However, like any other technology, this one too might have flaws which can be exploited. This paper also lists the security challenges that will have to be kept in mind while implementing these systems. © 2020 Ieee.

关键词： Cryptography

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YOLOv8 based An Automated segmentation And Classification Of Thyroid Nodules Using UsG Images

YOLOv8 Based An Automated Segmentation And Classification Of...

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Innovation and Novelty in engineering and technology (INNOVA), International Conference on

作者： savita Gondi M. U. Nagaral Department of Computer Science and Engineering B.L.D.E.A's V.P. Dr.P.G Halakatti College of Engineering and Technology Vijaypur (Affiliated to Visvesvaraya Technological University Belagavi Karnataka India

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

Thyroid nodules are a common clinical ob.e.vation that needs to be accurately classified and segmented in order to be managed and treated. The use of the sophisticated ob.e.t detection model You Only Look Once (YOLO) version 8 to the issue of nodule of the thyroid identification and categorization is investigated in this work. We gathered and interpreted 701 thyroid ultrasound images for this study, identifying two different nodule types as benign (315 images) and malignant (344 images). To assess the model's performance thoroughly, the dataset was divided into training (70%), validation (20%), and test (10%) sets. After training YOLOv8 on these images, metrics including Precision (78.1%), Recall (81.9%), F1 score (80%), and mean average precision (mAPall) (88.1% mAP@50) are used, with benign (91.8% mAP) and Malign (84.5% mAP. The findings show that YOLOv8, outperforms earlier model YOLOv5, achieves good accuracy in both detecting and categorizing thyroid nodules. The ability of the model to accurately distinguish between benign and malignant nodules is critical for clinical decision-making.

关键词： YOLO Training Image segmentation Technological innovation Accuracy Ultrasonic imaging smoothing methods Real-time systems Thyroid Cancer

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Prediction of stock Price Using XG boost Integrated bi-Channel LsTM

Prediction of Stock Price Using XG Boost Integrated Bi-Chann...

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Advances in computer science, electrical, electronics, and Communication Technologies (Ce2CT), International Conference on

作者： Amey Adinath Choudhari Rajeswaran Ayyadurai Jayant Maruti Hudar V.K. Arthi Tasriqul Islam G Jayashree Hareesh JSPM's Rajashi Shahu College of Engineering Pune India IL Health & Beauty Natural Oils Co Inc California USA DBA BHS University Switzerland DBA in Data Science and AI PGDBM B.E. Electronics. Diploma in Computer Technology Navi Mumbai India Department of Management Studies Sri Ramakrishna Engineering College Coimbatore India Harvard University Cambridge Massachusetts USA Fahad Bin Sultan University Tabuk Saudi Arabia

ISBN: (数字)9798331518578

ISBN: (纸本)9798331518585

Investors' biggest prob.e. is determining the appropriate stock price by studying financial information, which is a difficult process due to enormous and related inefficiencies. so, among the biggest challenges for investors is identifying a stock. Currently, it's very challenging to carry out research in the forecasts for the finance industry, including the stock market, currency value, and stock price. Due to its critical importance in financial consumers' decision-making, the prediction and examination of the stock price is yet another important research sub.e.t. A novel Xgboost Integrated bichannel Long/short-Term Memory (X-bLsTM) technique is implemented in this article to provide a sophisticated and perfect framework for the share market's price prediction The share value dataset is the first thing we collect to assess the way the suggested X-bLsTM strategy performs. Due to the existence of noisy data, min-max normalization is performed to preprocess the raw dataset. The efficiency of the proposed approach is assessed using several measures, and comparative between the proposed and current methods is also provided. The studies' findings show that the recommended approach outperforms alternative techniques for share value prediction.

关键词： Training Industries electric potential Current measurement Decision making Noise measurement stock markets Forecasting Long short term memory Currencies

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enhancing Alzheimer's Disease Prediction with bayesian Optimization and ensemb.e.Methods 23

Enhancing Alzheimer's Disease Prediction with Bayesian Optim...

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

作者： D. s. Wankhede Nuvita Kalra Ritika Dhabliya Vinit Khetani Tushar Waykole Aparna s. shirkande Computer Engineering VIIT India Symbiosis Law School Nagpur Campus Symbiosis International (Deemed University) India Computer Yashika Journal Publications Pvt. Ltd India Computer Cybrix Technologies India Department of Computer Engineering Nutan Maharashtra Institute of Information & Technology India E & TC Assistant Professor S. B. Patil College of Engineering India

ISBN: (纸本)9798400709418

The global health crisis of Alzheimer's Disease highlights the importance of early and accurate diagnosis. This study aimed to thoroughly evaluate the effectiveness of three ensemb.e.machine learning models, namely Random Forest, Adaboost, and XGboost, in accurately predicting the occurrence of Alzheimer's Disease. This study holds significant importance as the timely and precise identification of Alzheimer's Disease can have a substantial influence on the provision of patient care and the implementation of effective management strategies. Multiple evaluation metrics were utilized to conduct a comprehensive evaluation of the predictive capabilities of each model. The findings of this study revealed significant insights regarding the predictive capabilities of these models. The performance of Adaboost indicates its potential applicability in predicting Alzheimer's Disease, particularly when there is a need to strike a balanced compromise between false positives and false negatives. The ensemb.e.models collectively performed well, with XGboost emerging as the most notab.e.performer, exhibiting a remarkab.e.accuracy rate of 98.32%. It is worth mentioning that XGboost demonstrated exceptional performance in terms of specificity (98.65%) and sensitivity (98.63%), indicating its remarkab.e.ability to accurately classify cases of both Alzheimer's and non-Alzheimer's. This study highlights the potential of ensemb.e.machine learning techniques, specifically XGboost, in greatly improving the precision and dependability of Alzheimer's Disease prognosis. The timely and accurate identification of Alzheimer's Disease is crucial for the implementation of effective clinical interventions and the provision of appropriate care. The results of this study have significant implications for enhancing patient outcomes and advancing the field of medical diagnostics in the realm of neurodegenerative diseases.

关键词： Alzheimer's Disease

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barcode Recognition Using sequential and Parallel Approach

Barcode Recognition Using Sequential and Parallel Approach

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Innovation and Novelty in engineering and technology (INNOVA), International Conference on

作者： Chidanand s. Kusur M. s. shirdhonkar Department of Computer Science and Engineering B.L.D.E.A's V. P. Dr. P. G. H. College of Engineering and Technology Vijayapur Karnataka India Visvesvaraya Technological University Belagavi

ISBN: (数字)9798331505134

ISBN: (纸本)9798331505141

In a computer vision and image processing, the barcode detection and recognition systems play very important role for the interpretation of the encoded information for the system automation and inventory management. Hence, we proposed an enhanced approach to barcode recognition from video frames by extending our previous work focused on barcode detection in static images i.e. for the detection and recognition of barcodes from the videos of racks of books. The proposed system showed that, the parallel approach is time efficient in comparison with sequential approach.

关键词： Technological innovation computer vision Image recognition Automation Inventory management Time complexity

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