| 1. |
- Jasmine Pemeena Priyadarsini, M., et al.
(author)
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Lung Diseases Detection Using Various Deep Learning Algorithms
- 2023
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In: Journal of Healthcare Engineering. - : Hindawi Publishing Corporation. - 2040-2295 .- 2040-2309. ; 2023
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Journal article (peer-reviewed)abstract
- The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient’s treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.
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| 2. |
- Kadam, Prachi, et al.
(author)
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FVEstimator : A novel food volume estimator Wellness model for calorie measurement and healthy living
- 2022
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In: Measurement. - : Elsevier. - 0263-2241 .- 1873-412X. ; 198
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Journal article (peer-reviewed)abstract
- Identifying the calorific value of food requires a correct estimate of its volume and size dimensions. The food volumetric estimation can be done rationally and efficiently by measuring the food dimensions in terms of surface parameters. Food volume estimation can be effectively implemented with a computer vision-based application. The food image size can be estimated for its volumetric and calorific calibration with food area measures. However, studies in this area are limited to finding dimensions of a food item with geometrically regular, irregular, amorphous, and solid food shapes. There is a particular challenge with amorphous food items which do not have any shape and are usually calibrated with subjective container sizes by the dietitians and hence cause relative measures. Instance segmentation techniques are implemented at the pixel level and classify a pixel into a food type leading to higher accuracy in classification and segmentation of food over the background. In this work, mask-based RCNN is employed that helps accurate segmentation of food images with regular and irregular shapes in multi-food dish scenarios. The RCNN based food segmentation is applied as a volume estimator model. It is developed by fine-tuning the pre-trained ResNet model and trained over a dataset of 8 different classes of Indian breakfast food images in all shapes. The estimator model yields a precision of 90.9% for convex-shaped food images, 90.46% for amorphous food images in regular serving containers, and 98.5% to 98.9% for regular shaped (square and circle) food items. The accuracy of the presented volume estimator thus opens opportunities for further research with diverse food types and shapes.
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| 3. |
- Mehta, Pooja, et al.
(author)
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Harvesting social media sentiment analysis to enhance stock market prediction using deep learning
- 2021
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In: PeerJ Computer Science. - : MDPI. - 2376-5992. ; 7
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Journal article (peer-reviewed)abstract
- Information gathering has become an integral part of assessing people's behaviors and actions. The Internet is used as an online learning site for sharing and exchanging ideas. People can actively give their reviews and recommendations for variety of products and services using popular social sites and personal blogs. Social networking sites, including Twitter, Facebook, and Google+, are examples of the sites used to share opinion. The stock market (SM) is an essential area of the economy and plays a significant role in trade and industry development. Predicting SM movements is a well-known and area of interest to researchers. Social networking perfectly reflects the public's views of current affairs. Financial news stories are thought to have an impact on the return of stock trend prices and many data minin g techniques are used address fluctuations in the SM. Machine learning can provide a more accurate and robust approach to handle SM-related predictions. We sought to identify how movements in a company's stock prices correlate with the expressed opinions (sentiments) of the public about that company. We designed and implemented a stock price prediction accuracy tool considering public sentiment apart from other parameters. The proposed algorithm considers public sentiment, opinions, news and historical stock prices to forecast future stock prices. Our experiments were performed using machine-learning and deep-learning methods including Support Vector Machine, MNB classifier, linear regression, Naive Bayes and Long Short-Term Memory. Our results validate the success of the proposed methodology.
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| 4. |
- Mishra, Nivedita, et al.
(author)
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Memcached : An Experimental Study of DDoS Attacks for the Wellbeing of IoT Applications
- 2021
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In: Sensors. - : MDPI. - 1424-8220. ; 21:23
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Journal article (peer-reviewed)abstract
- Distributed denial‐of‐service (DDoS) attacks are significant threats to the cyber world because of their potential to quickly bring down victims. Memcached vulnerabilities have been targeted by attackers using DDoS amplification attacks. GitHub and Arbor Networks were the victims of Memcached DDoS attacks with 1.3 Tbps and 1.8 Tbps attack strengths, respectively. The bandwidth amplification factor of nearly 50,000 makes Memcached the deadliest DDoS attack vector to date. In recent times, fellow researchers have made specific efforts to analyze and evaluate Memcached vulnerabilities; however, the solutions provided for security are based on best practices by users and service providers. This study is the first attempt at modifying the architecture of Memcached servers in the context of improving security against DDoS attacks. This study discusses the Memcached protocol, the vulnerabilities associated with it, the future challenges for different IoT applications associated with caches, and the solutions for detecting Memcached DDoS attacks. The proposed solution is a novel identification‐pattern mechanism using a threshold scheme for detecting volume‐based DDoS attacks. In the undertaken study, the solution acts as a pre‐emptive measure for detecting DDoS attacks while maintaining low latency and high throughput.
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| 5. |
- Mishra, Sushruta, et al.
(author)
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An Explainable Intelligence Driven Query Prioritization Using Balanced Decision Tree Approach for Multi-Level Psychological Disorders Assessment
- 2021
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In: Frontiers In Public Health. - : Frontiers Media S.A.. - 2296-2565. ; 9
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Journal article (peer-reviewed)abstract
- Human emotions affect psychological health to a great level. Positive emotions relate to health improvement; whereas negative emotions may aggravate psychological disorders such as anxiety, stress, and depression. Although there exist several computational methods to predict psychological disorders, most of them provide a black-box view of uncertainty. This research involves developing a novel predictive model for multi class psychological risk recognition with an accurate explainable interface. Standard questionnaires are utilized as data set and a new approach called a Q-Prioritization is employed to drop insignificant questions from the data set. Moreover, a novel balanced decision tree method based on repetitive oversampling is applied for the training and testing of the model. Predictive nature along with its contributing factors are interpreted with three techniques such as permuted feature importance, contrastive explanation, and counterfactual method, which together form a reasoning engine. The prediction outcome generated an impressive performance with an aggregated accuracy of 98.25%. The mean precision, recall, and F-score metric recorded were 0.98, 0.977, and 0.979, respectively. Also, it was noted that without applying Q-Prioritization, the accuracy significantly drops to 90.25%. The error rate observed with our model was only 0.026. The proposed multi-level psychological disorder predictive model can successfully serve as an assistive deployment for medical experts in the effective treatment of mental health.
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| 6. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(author)
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Pollution Weather Prediction System : Smart Outdoor Pollution Monitoring and Prediction for Healthy Breathing and Living
- 2020
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In: Sensors. - : MDPI. - 1424-8220. ; 20:18
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Journal article (peer-reviewed)abstract
- Air pollution has been a looming issue of the 21st century that has also significantly impacted the surrounding environment and societal health. Recently, previous studies have conducted extensive research on air pollution and air quality monitoring. Despite this, the fields of air pollution and air quality monitoring remain plagued with unsolved problems. In this study, the Pollution Weather Prediction System (PWP) is proposed to perform air pollution prediction for outdoor sites for various pollution parameters. In the presented research work, we introduced a PWP system configured with pollution-sensing units, such as SDS021, MQ07-CO, NO2-B43F, and Aeroqual Ozone (O3). These sensing units were utilized to collect and measure various pollutant levels, such as PM2.5, PM10, CO, NO2, and O3, for 90 days at Symbiosis International University, Pune, Maharashtra, India. The data collection was carried out between the duration of December 2019 to February 2020 during the winter. The investigation results validate the success of the presented PWP system. In the conducted experiments, linear regression and artificial neural network (ANN)-based AQI (air quality index) predictions were performed. Furthermore, the presented study also found that the customized linear regression methodology outperformed other machine-learning methods, such as linear, ridge, Lasso, Bayes, Huber, Lars, Lasso-lars, stochastic gradient descent (SGD), and ElasticNet regression methodologies, and the customized ANN regression methodology used in the conducted experiments. The overall AQI values of the air pollutants were calculated based on the summation of the AQI values of all the presented air pollutants. In the end, the web and mobile interfaces were developed to display air pollution prediction values of a variety of air pollutants.
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| 7. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(author)
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Smart epidemic tunnel : IoT-based sensor-fusion assistive technology for COVID-19 disinfection
- 2020
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In: International Journal of Pervasive Computing and Communications. - : Emerald Group Publishing Limited. - 1742-7371 .- 1742-738X. ; 18:4, s. 376-387
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Journal article (peer-reviewed)abstract
- Purpose – The purpose of the presented IoT based sensor-fusion assistive technology for COVID-19disinfection termed as “Smart epidemic tunnel” is to protect an individual using an automatic sanitizer spraysystem equipped with a sanitizer sensing unit based on individual using an automatic sanitizer spray system equipped with a sanitizer sensing unit based on human motion detection.Design/methodology/approach – The presented research work discusses a smart epidemic tunnel that can assist an individual in immediate disinfection from COVID-19 infections. The authors have presented a sensor-fusion-based automatic sanitizer tunnel that detects a human using an ultrasonic sensor from the height of 1.5 feet and disinfects him/her using the spread of a sanitizer spray. The presented smart tunnel operates using a solar cell during the day time and switched to a solar power-bank power mode during night timings using a light-dependent register sensing unit.Findings – The investigation results validate the performance evaluation of the presented smart epidemic tunnel mechanism. The presented smart tunnel can prevent or disinfect an outsider who is entering a particular building or a premise from COVID-19 infection possibilities. Furthermore, it has also been observed that the presented sensor-fusion-based mechanism can disinfect a person in a time of span of just 10 s. The presented smart epidemic tunnel is embedded with an intelligent sanitizer sensing unit which stores the essential information in a cloud platform such as Google Fire-base. Thus, the proposed system favours society by saving time and helps in lowering the spread of coronavirus. It also provides daily, weekly and monthly reports of the counts of individuals, along with in-out time stamps and power usage reports.Practical implications – The presented system has been designed and developed after the lock-down period to disinfect an individual from the possibility of COVID-19 infectionsSocial implications – The presented smart epidemic tunnel reduced the possibility by disinfecting an outside individual/COVID-19 suspect from spreading the COVID-19 infections in a particular building or apremise. Originality/value – The presented system is an original work done by all the authors which have been installed at the Symbiosis Institute of Technology premise and have undergone rigorous experimentation and testing by the authors and end-users.
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| 8. |
- Pandya, Sharnil, Researcher, 1984-, et al.
(author)
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Smart Home Anti-Theft System : A Novel Approach for Near Real-Time Monitoring and Smart Home Security for Wellness Protocol
- 2018
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In: Applied System Innovation. - : MDPI. - 2571-5577. ; 1
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Journal article (peer-reviewed)abstract
- The proposed research methodology aims to design a generally implementable framework for providing a house owner/member with the immediate notification of an ongoing theft (unauthorized access to their premises). For this purpose, a rigorous analysis of existing systems was undertaken to identify research gaps. The problems found with existing systems were that they can only identify the intruder after the theft, or cannot distinguish between human and non-human objects. Wireless Sensors Networks (WSNs) combined with the use of Internet of Things (IoT) and Cognitive Internet of Things are expanding smart home concepts and solutions, and their applications. The present research proposes a novel smart home anti-theft system that can detect an intruder, even if they have partially/fully hidden their face using clothing, leather, fiber, or plastic materials. The proposed system can also detect an intruder in the dark using a CCTV camera without night vision capability. The fundamental idea was to design a cost-effective and efficient system for an individual to be able to detect any kind of theft in real-time and provide instant notification of the theft to the house owner. The system also promises to implement home security with large video data handling in real-time. The investigation results validate the success of the proposed system. The system accuracy has been enhanced to 97.01%, 84.13, 78.19%, and 66.5%, in scenarios where a detected intruder had not hidden his/her face, hidden his/her face partially, fully, and was detected in the dark from 85%, 64.13%, 56.70%, and 44.01%.
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| 9. |
- Shah, Apeksha, et al.
(author)
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Smart Cardiac Framework for an Early Detection of Cardiac Arrest Condition and Risk
- 2021
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In: Frontiers In Public Health. - : Frontiers Media S.A.. - 2296-2565. ; 9
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Journal article (peer-reviewed)abstract
- Cardiovascular disease (CVD) is considered to be one of the most epidemic diseases in the world today. Predicting CVDs, such as cardiac arrest, is a difficult task in the area of healthcare. The healthcare industry has a vast collection of datasets for analysis and prediction purposes. Somehow, the predictions made on these publicly available datasets may be erroneous. To make the prediction accurate, real-time data need to be collected. This study collected real-time data using sensors and stored it on a cloud computing platform, such as Google Firebase. The acquired data is then classified using six machine-learning algorithms: Artificial Neural Network (ANN), Random Forest Classifier (RFC), Gradient Boost Extreme Gradient Boosting (XGBoost) classifier, Support Vector Machine (SVM), Naïve Bayes (NB), and Decision Tree (DT). Furthermore, we have presented two novel gender-based risk classification and age-wise risk classification approach in the undertaken study. The presented approaches have used Kaplan-Meier and Cox regression survival analysis methodologies for risk detection and classification. The presented approaches also assist health experts in identifying the risk probability risk and the 10-year risk score prediction. The proposed system is an economical alternative to the existing system due to its low cost. The outcome obtained shows an enhanced level of performance with an overall accuracy of 98% using DT on our collected dataset for cardiac risk prediction. We also introduced two risk classification models for gender- and age-wise people to detect their survival probability. The outcome of the proposed model shows accurate probability in both classes.
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| 10. |
- Srivastava, Arpan, et al.
(author)
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Deep learning based respiratory sound analysis for detection of chronic obstructive pulmonary disease
- 2021
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In: PeerJ Computer Science. - : PeerJ. - 2376-5992. ; 7
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Journal article (peer-reviewed)abstract
- In recent times, technologies such as machine learning and deep learning have played a vital role in providing assistive solutions to a medical domain’s challenges. They also improve predictive accuracy for early and timely disease detection using medical imaging and audio analysis. Due to the scarcity of trained human resources, medical practitioners are welcoming such technology assistance as it provides a helping hand to them in coping with more patients. Apart from critical health diseases such as cancer and diabetes, the impact of respiratory diseases is also gradually on the rise and is becoming life-threatening for society. The early diagnosis and immediate treatment are crucial in respiratory diseases, and hence the audio of the respiratory sounds is proving very beneficial along with chest X-rays. The presented research work aims to apply Convolutional Neural Network based deep learning methodologies to assist medical experts by providing a detailed and rigorous analysis of the medical respiratory audio data for Chronic Obstructive Pulmonary detection. In the conducted experiments, we have used a Librosa machine learning library features such as MFCC, Mel-Spectrogram, Chroma, Chroma (Constant-Q) and Chroma CENS. The presented system could also interpret the severity of the disease identified, such as mild, moderate, or acute. The investigation results validate the success of the proposed deep learning approach. The system classification accuracy has been enhanced to an ICBHI score of 93%. Furthermore, in the conducted experiments, we have applied K-fold Cross-Validation with ten splits to optimize the performance of the presented deep learning approach.
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