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Osteoarthritis Prediction in Knee Joint Using Deep Learning Techniques 27

Osteoarthritis Prediction in Knee Joint Using Deep Learning ...

引用

27th International Symposium on Wireless Personal Multimedia Communications, WPMC 2024

作者： Singh, Shubham Kumar Chouhan, Kuldeep Agrawal, Arun Prakash Sharda University Sharda School of Engineering and Technology Department of Computer Science and Applications Greater Noida India Bennett University School of Computer Science Engineering and Technology Greater Noida India

ISBN: (纸本)9798350392319

Osteoarthritis is a disorder that transpires when the articular cartilage in the knee breaks down and changes to the underbelly of the cartilage. Normal joint fracture protection for the bones is provided by the slick substance known as articular cartilage. Early prediction can slow down the progression of the condition. Medical professionals' expertise is utilized to predict severity. In this research, we address the issue that arises when a model's validation accuracy is lower than its training accuracy. Specifically, we find that as dataset size increases, the validation accuracy of Osteoarthritis prediction falls. We used a Deep Convolutional Neural Network to develop an automatic osteoarthritis severity assessment system using the Kellgren and Lawrence (KL) grading system. With this method, X-ray images are used to predict the severity of osteoarthritis. This study's objective is to predict the severity stage of knee osteoarthritis in the form of prediction accuracy (in %) of osteoarthritis using a transfer learning and deep learning approach. As a result, EfficientNetB5 provides a prediction accuracy of over 97.45% as demonstrated in this work. © 2024 IEEE.

关键词： Convolutional neural networks

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Drug Consumption Detection by Eye Using Image Processing Techniques

Drug Consumption Detection by Eye Using Image Processing Tec...

引用

2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Pyingkodi, M. Thenmozhi, K. Wilfred Blessing, N.R. Archana, K. Arun Hiruthik, X. Dinesh Kumar, V. Kongu Engineering College Department of Computer Applications Erode India Kristu Jayanti College Department of Computer Science Bengaluru India University of Technology and Applied Sciences-Ibri Department of IT Oman Kongu Engineering College Computer Applications Erode India

ISBN: (纸本)9798350348217

Drug usage is a significant global economic issue that results in countless losses. This study suggests a convolutional neural network-based image processing method for identifying drugged eyes. The front-end packages already in use for this article collect information from eye images using the CNN algorithm. However, it can take some time. As a result, the suggested approach may be used to swiftly and automatically identify drugged eyes. The acquisition of the input image, picture pre-processing, and image processing are the main processes in the suggested *** the reddish regions, emphasising the afflicted regions, confirming the training set, and presenting the findings. Some eyes may go unnoticed, such those that have been socially poisoned. This technique was tried on drugged eyes at different phases, both with and without drug consumption. The technique was used to find a white region of the eye in a given input picture. Images of both drugged and unaffected eyes were presented for training purposes. In order to choose the optimum colour model for this strategy, photos were first transformed to colour models. The Support Erosion technique and the Local Binary Pattern were both used in the model construction for feature extraction. This technique can detect drugged eyes with an average accuracy of 95%. © 2023 IEEE.

关键词： Local binary pattern

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Analysis of Various Hyperparameters for Predicting Hypertension using Machine Learning Algorithms 8

Analysis of Various Hyperparameters for Predicting Hypertens...

引用

8th International Conference on Electronics, Communication and Aerospace Technology, ICECA 2024

作者： Duggal, Shallu Sood, Shivani Singh, Amanpreet Singh, Harjeet Malarvel, Muthukumaran Lovely Professional University School of Computer Applications Phagwara144411 India Chitkara Institute of Engineering and Technology Chitkara University Punjab 140401 India Department of Computer Science and Engineering India

ISBN: (纸本)9798350367904

Predicting hypertension, or high blood pressure, is crucial because it's a risky condition that frequently doesn't exhibit any obvious symptoms or indicators until major problems arise. By using predictive models for early identification, the risk of uncontrolled hypertension-related cardiovascular events, strokes, and organ damage can be decreased to take quick action. The study aims to predict the diagnosis of hypertension using three machine learning techniques: Logistic Regression, Gaussian Naive Bayes, and Support Vector Machine(SVM). With the focus on improving accuracy and reliability, the various hyperparameters of each individual algorithm are tuned using the cross validation and grid search approaches. After extensive testing and parameter modification, the SVM algorithm was found to be the most accurate predictor, which achieved a significant accuracy of 9 7. 0 0 %. The best hyper-parameters achieved for SVM are c value 4 and kernel as 'rbf'. The models' performance is evaluated using accuracy, specificity, sensitivity, F1-score, or AUC (Area Under the Curve). This study facilitates proactive healthcare measures by offering insightful information to determine the best algorithms for the early prediction of hypertension. © 2024 IEEE.

关键词： Prediction models

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Energy-efficiency Analysis of Different Scheduling Algorithms for Mobile Crowd Computing 15

Energy-efficiency Analysis of Different Scheduling Algorithm...

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

作者： Pramanik, Pijush Kanti Dutta Biswas, Tarun Galgotias University School of Computer Applications and Technology Greater Noida India Indian Institute of Information Technology Department of Computer Science & Engineering Ranchi India

ISBN: (纸本)9798350370249

Mobile crowd computing (MCC) has emerged as a promising paradigm to leverage the underutilised computational resources of smart mobile devices (SMDs). However, the energy constraints of these devices pose a significant challenge in efficiently executing computational tasks. This study analyses the energy efficiency of various scheduling algorithms for MCC environments. The study compares the performance of seven scheduling algorithms: PPIA, RAS, PSO, GA, MaxMin, MinMin, and MCT. These algorithms were evaluated in terms of their energy efficiency when executing different sets of tasks with varied instruction lengths. The experiment used a simulated MCC environment with real data from five SMDs. The results demonstrate that the PSO algorithm consistently outperformed the other scheduling schemes regarding energy efficiency across all task sets. The GA algorithm also showed promising results. The MinMin algorithm exhibited the lowest energy efficiency. The findings of this study provide valuable insights for designing and implementing energy-efficient task scheduling strategies in MCC systems. Analysing the different algorithms under varying task loads can help MCC service providers make informed decisions when selecting appropriate scheduling techniques to optimise the energy utilisation of SMDs. © 2024 IEEE.

关键词： Crowd computing Energy-efficiency Mobile cloud Particle swarm optimisation Task scheduling

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Microplastic Identification in Seawater using Generative Adversarial Networks

Microplastic Identification in Seawater using Generative Adv...

引用

2024 International Conference on Advances in Data engineering and Intelligent Computing Systems, ADICS 2024

作者： Rajasekar, M. Geetha, Angelina Hindustan Institute Of Technology And Science Department Of Computer Applications Chennai India Departmnet Of Computer Science And Engineering Hindustan Insitute Of Technology And Science Chennai India

ISBN: (纸本)9798350364828

In recent years, microplastics-plastic particles smaller than five millimeters-have become a significant environmental contaminant. These tiny plastic pieces have been found permeating aquatic ecosystems globally, posing a threat to marine life upon ingestion. However, identification and quantification of microplastics in seawater samples remains an arduous task. The small size and translucent nature of microplastics makes them difficult to distinguish from natural particulates in seawater under a microscope. This has hindered efforts to accurately monitor microplastic pollution levels in the marine environment. Our goal in this study was to use cutting edge deep learning techniques to create an automated image based system for detecting microplastics in seawater. We chose to employ a generative adversarial network (GAN) architecture for its proven capability in generating highly realistic synthetic images. The GAN model was trained on a dataset of microscope images of microplastics and natural particulates extracted from seawater samples. Coastal seawater samples were first filtered to isolate particulates of a size range containing microplastics. Density separation was then used to separate the plastic microparticles from denser natural particulates. We implemented a deep convolutional GAN with a generator network to produce synthetic microplastic images, and a discriminator network to differentiate real from synthetic images. Through iterative adversarial training, the two networks evolved to produce and accurately classify images of microplastics with a high degree of realism. Advanced image processing techniques were used to enhance the training data. After training, the GAN model was evaluated on an annotated testing dataset of images containing real-world microplastics and natural particulates extracted from seawater. Our proposed GAN-based technique achieved a microplastic classification accuracy of 92.5% on the test set, demonstrating its effectiveness. The

关键词： Convolutional neural networks

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Accident Detection Using Fog Computing 6

Accident Detection Using Fog Computing

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Srivastava, Mayank Dixit, Pratibha Ranjan, Pulkit Gla University Mathura Dept. of Computer Engineering and Applications UP Mathura India

ISBN: (纸本)9798350346961

Emergencies, in terms of definition, are the irregular and contiguous answer is a necessary demand for emergency administration. Worldwide, an important number of deaths arise each year, resulting in necessary delays in saving operations. To minimize these delays and save the lives of people when have come up with a project that is based on the computer network that uses Fog computing. It contains three modules in which collision detection is to be carried out. Pre-recorded videos and live videos have been broken down into frames and sent to the MQTT broker which is a lightweight network protocol to send and receive messages. Video Frame is sent to Processing Unit for detection. Results are sent back to MQTT Broker and subscribed devices are informed accordingly. In this Live testing is being done on highways, apart from this Training of the model on various constraints using a mask and fast R-CNN is being done followed by a validation phase that shows the efficiency of this project. The result shows the need to save the lives which are in danger due to accidents by minimizing the time to contact the required department. This delay is minimized with the help of a concept called Fog Computing which serves as a middleware between devices and the cloud therefore saving time.. © 2023 IEEE.

关键词： Fog computing

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An IoT-based Approach to Air Circulation within a Specific Room Environment 3

An IoT-based Approach to Air Circulation within a Specific R...

引用

3rd International Conference on Artificial Intelligence and Smart Energy, ICAIS 2023

作者： Gopal, Madhwaraj Kango Asha, V. Hegde, Adarsh Ishwar Jaiswal, Adarsh New Horizon College of Engineering Dept. of Master of Computer Applications Bangalore India

ISBN: (纸本)9781665462167

IoT (Internet of Things) is a concept that has been used extensively due to its acceptance and benefits if offers while used across several domains. Good fresh air with the right mix of particulates is what is needed today to ward off respiratory disorders. An important concern that needs to be addressed is how to provide this fresh air in today's living environment. In this work, an IoT-based approach has been used to effectively increase the efficiency and overall air quality within indoor spaces. Several parameters like using temperature sensor to sense temperature, humidity sensor to sense humidity, Smoke sensor to collect data from the different gases, and many more other sensors are used to collect data from dust and air particulates. Fuzzy logic algorithms are used to check whether the levels of the gases will cause discomfort or breathing difficulties in the people who are currently within the confines of the area. An automated air ventilation and filtration system is used to either increase or decrease the airflow of the system to mitigate the air quality degradation and bring the system back to an equilibrium state therefore maintaining fresh air circulation. © 2023 IEEE.

关键词： Internet of things

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Chronic Kidney Disease Prediction Using Random Forest, Decision Tree and Ada Boost Classifier 4

Chronic Kidney Disease Prediction Using Random Forest, Decis...

引用

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

作者： Agrawal, Mayank Mohan, Narendra Jain, Vinod Gla University Department of Computer Engineering and Applications U.P. Mathura India

ISBN: (纸本)9798350300888

Millions of individuals worldwide are afflicted with the common and possibly fatal ailment known as chronic kidney disease (CKD). By allowing for prompt diagnosis and care, early identification and precise prediction of CKD may greatly improve patient outcomes. Machine learning algorithms have become effective resources for forecasting illness outcomes based on patient data in recent years. In order to better predict CKD, this research compares three well-known machine learning algorithms - Random Forest, Decision Tree, and Ada Boost Classifier performance. The clinical and laboratory data from a cohort of CKD patients were gathered to create the dataset utilized in this investigation. Demographic data, medical history, vital signs, and the findings of laboratory tests are among the characteristics. To improve the prediction accuracy of these ML algorithms, K-Fold validation techniques is applied. The findings show that Random Forest, Decision Tree, and Ada Boost Classifier might be useful tools for the early diagnosis and prediction of CKD. Among these three the prediction accuracy of Random Forest Classifier is found 99.98% which is maximum among the three. © 2023 IEEE.

关键词： Decision trees

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e-Health: A Modern Approach to Healthcare Delivery 14

e-Health: A Modern Approach to Healthcare Delivery

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Maithani, Ankit Pradhan, Rahul Dit University Dehradun Department of Computer Science & Engineering Dehradun India Gla University Mathura Department of Computer Engineering & Applications Mathura India

ISBN: (纸本)9798350335095

e-Health had various attributes other than doctors, patients and medicine. Some of the components are health records, prescriptions, decision support system, online consultation videos, transcripts and telemedicine. Information technology is now integral and one of the core components of eHealth. Prescriptions generated from computer and Artificial intelligent expert systems, include diagnosis, basis of diagnosis, medicine, their side-effects and drug combination and their dosages. © 2023 IEEE.

关键词： Diagnosis

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Comparative Analysis of applications of Machine Learning in Credit Card Fraud Detection 6

Comparative Analysis of Applications of Machine Learning in ...

引用

6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Ghosh, Soumili Bilgaiyan, Saurabh Gourisaria, Mahendra Kumar Sharma, Ashish Deemed to Be University School of Computer Engineering Kiit Bhubaneshwar India Ganeshi Lal Agarwal University School of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350346961

Recent statistics show that Credit card fraud had increased by a hefty 20% in 2021, marking a stark rise in criminal activity from the previous year. Several techniques have been applied so far to counter the rise of fraudulent activity. However, there still lies scope for improvement in the techniques applied. One of the many ways that a fraud detection system can be improved is by the introduction of machine learning into its applications. The key purpose of a card fraud detection program is to identify fraudulent or suspicious activity and report them to an analyst whilst letting the normal transactions pass through. With the application of supervised algorithms, like Logistic regression and K-Nearest Neighbors, the efficiency for detecting fraudulent cases, by systems that have trained on detection of suspicious transactions, becomes all the more efficient. In this paper, we present a comparative analysis detection of fraud cases through usage of supervised algorithms, an AutoEncoder (neural network architecture), and one more model, OCSVM (One-Class Support vector Machine), from a library built specifically for outlier detection. Metrics like precision, recall and F1-score are used to evaluate the models and select the optimal algorithm for credit card fraud detection, which results in a balanced combination of the metrics used to evaluate them. The selected algorithm will help prevent such suspicious activities with regard to credit card transactions, with prompt accuracy and precision.. © 2023 IEEE.

关键词： Anomaly detection

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