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Nandagopal M, Seerangan K, Govindaraju T, Abi NE, Balusamy B, Selvarajan S. A Deep Auto-Optimized Collaborative Learning (DACL) model for disease prognosis using AI-IoMT systems. Sci Rep 2024; 14:10280. [PMID: 38704423 PMCID: PMC11069552 DOI: 10.1038/s41598-024-59846-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2023] [Accepted: 04/16/2024] [Indexed: 05/06/2024] Open
Abstract
In modern healthcare, integrating Artificial Intelligence (AI) and Internet of Medical Things (IoMT) is highly beneficial and has made it possible to effectively control disease using networks of interconnected sensors worn by individuals. The purpose of this work is to develop an AI-IoMT framework for identifying several of chronic diseases form the patients' medical record. For that, the Deep Auto-Optimized Collaborative Learning (DACL) Model, a brand-new AI-IoMT framework, has been developed for rapid diagnosis of chronic diseases like heart disease, diabetes, and stroke. Then, a Deep Auto-Encoder Model (DAEM) is used in the proposed framework to formulate the imputed and preprocessed data by determining the fields of characteristics or information that are lacking. To speed up classification training and testing, the Golden Flower Search (GFS) approach is then utilized to choose the best features from the imputed data. In addition, the cutting-edge Collaborative Bias Integrated GAN (ColBGaN) model has been created for precisely recognizing and classifying the types of chronic diseases from the medical records of patients. The loss function is optimally estimated during classification using the Water Drop Optimization (WDO) technique, reducing the classifier's error rate. Using some of the well-known benchmarking datasets and performance measures, the proposed DACL's effectiveness and efficiency in identifying diseases is evaluated and compared.
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Affiliation(s)
- Malarvizhi Nandagopal
- Department of CSE, School of Computing, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, Tamil Nadu, 600062, India
| | - Koteeswaran Seerangan
- Department of CSE (AI&ML), S.A. Engineering College (Autonomous), Chennai, Tamil Nadu, 600077, India
| | - Tamilmani Govindaraju
- Department of Computational Intelligence, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, 603203, India
| | - Neeba Eralil Abi
- Department of Information Technology, Rajagiri School of Engineering and Technology, Kochi, Kerala, 682039, India
| | - Balamurugan Balusamy
- Shiv Nadar (Institution of Eminence Deemed to be University), Greater Noida, Uttar Pradesh, 201314, India
| | - Shitharth Selvarajan
- Department of Computer Science, Kebri Dehar University, 250, Kebri Dehar, Ethiopia.
- School of Built Environment, Engineering and Computing, Leeds Beckett University, LS1 3HE, Leeds, UK.
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Nallakaruppan MK, Gangadevi E, Shri ML, Balusamy B, Bhattacharya S, Selvarajan S. Reliable water quality prediction and parametric analysis using explainable AI models. Sci Rep 2024; 14:7520. [PMID: 38553492 PMCID: PMC10980827 DOI: 10.1038/s41598-024-56775-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Accepted: 03/11/2024] [Indexed: 04/02/2024] Open
Abstract
The consumption of water constitutes the physical health of most of the living species and hence management of its purity and quality is extremely essential as contaminated water has to potential to create adverse health and environmental consequences. This creates the dire necessity to measure, control and monitor the quality of water. The primary contaminant present in water is Total Dissolved Solids (TDS), which is hard to filter out. There are various substances apart from mere solids such as potassium, sodium, chlorides, lead, nitrate, cadmium, arsenic and other pollutants. The proposed work aims to provide the automation of water quality estimation through Artificial Intelligence and uses Explainable Artificial Intelligence (XAI) for the explanation of the most significant parameters contributing towards the potability of water and the estimation of the impurities. XAI has the transparency and justifiability as a white-box model since the Machine Learning (ML) model is black-box and unable to describe the reasoning behind the ML classification. The proposed work uses various ML models such as Logistic Regression, Support Vector Machine (SVM), Gaussian Naive Bayes, Decision Tree (DT) and Random Forest (RF) to classify whether the water is drinkable. The various representations of XAI such as force plot, test patch, summary plot, dependency plot and decision plot generated in SHAPELY explainer explain the significant features, prediction score, feature importance and justification behind the water quality estimation. The RF classifier is selected for the explanation and yields optimum Accuracy and F1-Score of 0.9999, with Precision and Re-call of 0.9997 and 0.998 respectively. Thus, the work is an exploratory analysis of the estimation and management of water quality with indicators associated with their significance. This work is an emerging research at present with a vision of addressing the water quality for the future as well.
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Affiliation(s)
- M K Nallakaruppan
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India
| | - E Gangadevi
- Department of Computer Science, Loyola College, Chennai, Tamil Nadu, 600034, India
| | - M Lawanya Shri
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India
| | | | - Sweta Bhattacharya
- School of Computer Science Engineering and Information Systems, Vellore Institute of Technology, Vellore, 632014, India
| | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS13HE, UK.
- Department of Computer Science, Kebri Dehar University, Kebri Dehar, Ethiopia.
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Natarajan SK, Shanmurthy P, Arockiam D, Balusamy B, Selvarajan S. Optimized machine learning model for air quality index prediction in major cities in India. Sci Rep 2024; 14:6795. [PMID: 38514669 PMCID: PMC10958024 DOI: 10.1038/s41598-024-54807-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2024] [Accepted: 02/16/2024] [Indexed: 03/23/2024] Open
Abstract
Industrial advancements and utilization of large amount of fossil fuels, vehicle pollution, and other calamities increases the Air Quality Index (AQI) of major cities in a drastic manner. Major cities AQI analysis is essential so that the government can take proper preventive, proactive measures to reduce air pollution. This research incorporates artificial intelligence in AQI prediction based on air pollution data. An optimized machine learning model which combines Grey Wolf Optimization (GWO) with the Decision Tree (DT) algorithm for accurate prediction of AQI in major cities of India. Air quality data available in the Kaggle repository is used for experimentation, and major cities like Delhi, Hyderabad, Kolkata, Bangalore, Visakhapatnam, and Chennai are considered for analysis. The proposed model performance is experimentally verified through metrics like R-Square, RMSE, MSE, MAE, and accuracy. Existing machine learning models, like k-nearest Neighbor, Random Forest regressor, and Support vector regressor, are compared with the proposed model. The proposed model attains better prediction performance compared to traditional machine learning algorithms with maximum accuracy of 88.98% for New Delhi city, 91.49% for Bangalore city, 94.48% for Kolkata, 97.66% for Hyderabad, 95.22% for Chennai and 97.68% for Visakhapatnam city.
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Affiliation(s)
- Suresh Kumar Natarajan
- School of Computer Science and Engineering, Jain (Deemed-to-be University), Bengaluru, Karnataka, India
| | - Prakash Shanmurthy
- School of Computer Science and Engineering and Information Science, Presidency University, Bengaluru, Karnataka, India
| | | | | | - Shitharth Selvarajan
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
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Nadana Ravishankar T, Ramprasath M, Daniel A, Selvarajan S, Subbiah P, Balusamy B. White shark optimizer with optimal deep learning based effective unmanned aerial vehicles communication and scene classification. Sci Rep 2023; 13:23041. [PMID: 38155207 PMCID: PMC10754923 DOI: 10.1038/s41598-023-50064-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 12/14/2023] [Indexed: 12/30/2023] Open
Abstract
Unmanned aerial vehicles (UAVs) become a promising enabler for the next generation of wireless networks with the tremendous growth in electronics and communications. The application of UAV communications comprises messages relying on coverage extension for transmission networks after disasters, Internet of Things (IoT) devices, and dispatching distress messages from the device positioned within the coverage hole to the emergency centre. But there are some problems in enhancing UAV clustering and scene classification using deep learning approaches for enhancing performance. This article presents a new White Shark Optimizer with Optimal Deep Learning based Effective Unmanned Aerial Vehicles Communication and Scene Classification (WSOODL-UAVCSC) technique. UAV clustering and scene categorization present many deep learning challenges in disaster management: scene understanding complexity, data variability and abundance, visual data feature extraction, nonlinear and high-dimensional data, adaptability and generalization, real-time decision making, UAV clustering optimization, sparse and incomplete data. the need to handle complex, high-dimensional data, adapt to changing environments, and make quick, correct decisions in critical situations drives deep learning in UAV clustering and scene categorization. The purpose of the WSOODL-UAVCSC technique is to cluster the UAVs for effective communication and scene classification. The WSO algorithm is utilized for the optimization of the UAV clustering process and enables to accomplish effective communication and interaction in the network. With dynamic adjustment of the clustering, the WSO algorithm improves the performance and robustness of the UAV system. For the scene classification process, the WSOODL-UAVCSC technique involves capsule network (CapsNet) feature extraction, marine predators algorithm (MPA) based hyperparameter tuning, and echo state network (ESN) classification. A wide-ranging simulation analysis was conducted to validate the enriched performance of the WSOODL-UAVCSC approach. Extensive result analysis pointed out the enhanced performance of the WSOODL-UAVCSC method over other existing techniques. The WSOODL-UAVCSC method achieved an accuracy of 99.12%, precision of 97.45%, recall of 98.90%, and F1-score of 98.10% when compared to other existing techniques.
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Affiliation(s)
- T Nadana Ravishankar
- Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chennai, Tamil Nadu, India
| | - M Ramprasath
- Department of Data Science and Business Systems, SRM Institute of Science and Technology, Kattankulathur, Chennai, India
| | - A Daniel
- Computer Science & Engineering. Amity School of Engineering and Technology (ASET), Amity University, Gwalior, Madhya Pradesh, India
| | - Shitharth Selvarajan
- Department of Computer Science, Kebri Dehar University, Kebri Dehar, Ethiopia.
- School of Built Environment, Engineering and Computing, Leeds Beckett University, Leeds, LS1 3HE, UK.
| | - Priyanga Subbiah
- Department of Networking and Communications, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Chengalpattu District, Tamil Nadu, 603203, India
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Krishnan SD, Pelusi D, Daniel A, Suresh V, Balusamy B. Improved graph neural network-based green anaconda optimization for segmenting and classifying the lung cancer. Math Biosci Eng 2023; 20:17138-17157. [PMID: 37920050 DOI: 10.3934/mbe.2023764] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
Normal lung cells incur genetic damage over time, which causes unchecked cell growth and ultimately leads to lung cancer. Nearly 85% of lung cancer cases are caused by smoking, but there exists factual evidence that beta-carotene supplements and arsenic in water may raise the risk of developing the illness. Asbestos, polycyclic aromatic hydrocarbons, arsenic, radon gas, nickel, chromium and hereditary factors represent various lung cancer-causing agents. Therefore, deep learning approaches are employed to quicken the crucial procedure of diagnosing lung cancer. The effectiveness of these methods has increased when used to examine cancer histopathology slides. Initially, the data is gathered from the standard benchmark dataset. Further, the pre-processing of the collected images is accomplished using the Gabor filter method. The segmentation of these pre-processed images is done through the modified expectation maximization (MEM) algorithm method. Next, using the histogram of oriented gradient (HOG) scheme, the features are extracted from these segmented images. Finally, the classification of lung cancer is performed by the improved graph neural network (IGNN), where the parameter optimization of graph neural network (GNN) is done by the green anaconda optimization (GAO) algorithm in order to derive the accuracy maximization as the major objective function. This IGNN classifies lung cancer into normal, adeno carcinoma and squamous cell carcinoma as the final output. On comparison with existing methods with respect to distinct performance measures, the simulation findings reveal the betterment of the introduced method.
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Affiliation(s)
- S Dinesh Krishnan
- Assistant professor, B V Raju Institute of Technology, Narsapur, Telangana, India
| | - Danilo Pelusi
- Department of Communication Sciences, University of Teramo, Italy
| | - A Daniel
- Associate Professor, Amity University, Gwalior, Madhya Pradesh, India
| | - V Suresh
- Assistant professor, Dr. N. G. P Institute of Technology, Coimbatore, India
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Velu M, Dhanaraj RK, Balusamy B, Kadry S, Yu Y, Nadeem A, Rauf HT. Human Pathogenic Monkeypox Disease Recognition Using Q-Learning Approach. Diagnostics (Basel) 2023; 13:diagnostics13081491. [PMID: 37189591 DOI: 10.3390/diagnostics13081491] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 03/15/2023] [Accepted: 04/12/2023] [Indexed: 05/17/2023] Open
Abstract
While the world is working quietly to repair the damage caused by COVID-19's widespread transmission, the monkeypox virus threatens to become a global pandemic. There are several nations that report new monkeypox cases daily, despite the virus being less deadly and contagious than COVID-19. Monkeypox disease may be detected using artificial intelligence techniques. This paper suggests two strategies for improving monkeypox image classification precision. Based on reinforcement learning and parameter optimization for multi-layer neural networks, the suggested approaches are based on feature extraction and classification: the Q-learning algorithm determines the rate at which an act occurs in a particular state; Malneural networks are binary hybrid algorithms that improve the parameters of neural networks. The algorithms are evaluated using an openly available dataset. In order to analyze the proposed optimization feature selection for monkeypox classification, interpretation criteria were utilized. In order to evaluate the efficiency, significance, and robustness of the suggested algorithms, a series of numerical tests were conducted. There were 95% precision, 95% recall, and 96% f1 scores for monkeypox disease. As compared to traditional learning methods, this method has a higher accuracy value. The overall macro average was around 0.95, and the overall weighted average was around 0.96. When compared to the benchmark algorithms, DDQN, Policy Gradient, and Actor-Critic, the Malneural network had the highest accuracy (around 0.985). In comparison with traditional methods, the proposed methods were found to be more effective. Clinicians can use this proposal to treat monkeypox patients and administration agencies can use it to observe the origin and current status of the disease.
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Affiliation(s)
- Malathi Velu
- School of Computer Science and Engineering, Panimalar Engineering College, Poonamallee, Chennai 600123, India
| | - Rajesh Kumar Dhanaraj
- School of Computing Science and Engineering, Galgotias University, Greater Noida 203201, India
| | - Balamurugan Balusamy
- Associate Dean-Student Engagement, Shiv Nadar Institution of Eminence, Delhi-National Capital Region (NCR), Gautam Buddha Nagar 201314, India
| | - Seifedine Kadry
- Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway
- Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos 13-5053, Lebanon
| | - Yang Yu
- Centre for Infrastructure Engineering and Safety (CIES), The University of New South Wales, Sydney, NSW 2052, Australia
| | - Ahmed Nadeem
- Department of Pharmacology and Toxicology, College of Pharmacy, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
| | - Hafiz Tayyab Rauf
- Centre for Smart Systems, A.I. and Cybersecurity, Staffordshire University, Stoke-on-Trent ST4 2DE, UK
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Sahoo KS, Mishra P, Tiwary M, Ramasubbareddy S, Balusamy B, Gandomi AH. Improving End-Users Utility in Software-Defined Wide Area Network Systems. IEEE Trans Netw Serv Manage 2020. [DOI: 10.1109/tnsm.2019.2953621] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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Balusamy B, Deep BV, Ramasubbareddy S, Sahoo KS. Analysing control plane scalability issue of software defined wide area network using simulated annealing technique. IJGUC 2020. [DOI: 10.1504/ijguc.2020.10032055] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Sahoo KS, Ramasubbareddy S, Balusamy B, Deep BV. Analysing control plane scalability issue of software defined wide area network using simulated annealing technique. IJGUC 2020. [DOI: 10.1504/ijguc.2020.110898] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Pandi V, Perumal P, Balusamy B, Karuppiah M. A Novel Performance Enhancing Task Scheduling Algorithm for Cloud-Based E-Health Environment. International Journal of E-Health and Medical Communications 2019. [DOI: 10.4018/ijehmc.2019040106] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The fast-growing internet services have led to the rapid development of storing, retrieving and processing health-related documents from a public cloud. In such a scenario, the performance of cloud services offered is not guaranteed, since it depends on efficient resource scheduling, network bandwidth, etc. The trade-off which lies between the cost and the QoS is that the cost should be variably low on achieving high QoS. This can be done by performance optimization. In order to optimize the performance, a novel task scheduling algorithm is proposed in this article. The main advantage of this proposed scheduling algorithm is to improve the QoS parameters which comprises of metrics such as response time, computation time, availability and cost. The proposed work is simulated in Aneka and shows better performance compared to existing paradigms.
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Affiliation(s)
- Vijayakumar Pandi
- Department of Computer Science and Engineering, University College of Engineering Tindivanam, Tindivanam, India
| | - Pandiaraja Perumal
- Department of Computer Science and Engineering,M.Kumarasamy College of Engineering, Thalavapalayam, India
| | - Balamurugan Balusamy
- Department of Computer Science and Engineering, Galgotias University, Greater Noida, India
| | - Marimuthu Karuppiah
- School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India
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Assaf I, Brieteh F, Tfaily M, El-Baida M, Kadry S, Balusamy B. Students university healthy lifestyle practice: quantitative analysis. Health Inf Sci Syst 2019; 7:7. [PMID: 30956789 DOI: 10.1007/s13755-019-0068-2] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2018] [Accepted: 02/25/2019] [Indexed: 11/26/2022] Open
Abstract
The development of human being passes through several transition phases throughout the life span. The most critical phase that may influence the individuals' lifestyle is the college admission. During this phase, the students are independent and they are responsible for their own lives especially if they are far away from parental home. A healthy lifestyle is identified by regular exercises, healthy diet, and organized sleeping pattern. However, the transfer into a new environment may alternate the usual habits and cause major fluctuations in lifestyle. The students may be vulnerable to several stressful factors including inability to organize time, stress of exams and deadlines, irregular sleeping pattern, new peer's relationships, and inability to accommodate with the new surroundings. These factors may result in decreased level of physical activity and increased consumption of fast food that may lead to changes in body weight. The exposure to these changes in lifestyle may influence the well-being of individual and overall health. The aim of this study is to focus on the lifestyle of university students including the level of physical activity and type of diet followed and how it affects their weight. The result of our study, showed most of the students do not follow a certain diet, do not consider their food choices healthy and would like to take more care about their health. This is mostly due to the fact that they don't exercise as much as they like to.
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Affiliation(s)
- Israa Assaf
- 1Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
| | - Fatima Brieteh
- 1Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
| | - Mona Tfaily
- 1Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
| | - Mariam El-Baida
- 1Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
| | - Seifedine Kadry
- 2Faculty of Sciences, Beirut Arab University, Beirut, Lebanon
| | - Balamurugan Balusamy
- 3School of Computer Science and Engineering, Galgotias University, Greater Noida, UP India
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Nagasubramanian G, Sakthivel RK, Patan R, Gandomi AH, Sankayya M, Balusamy B. Securing e-health records using keyless signature infrastructure blockchain technology in the cloud. Neural Comput Appl 2018. [DOI: 10.1007/s00521-018-3915-1] [Citation(s) in RCA: 67] [Impact Index Per Article: 11.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Dwaidy J, Dwaidy A, Hasan H, Kadry S, Balusamy B. Survey of energy drink consumption and adverse health effects in Lebanon. Health Inf Sci Syst 2018; 6:15. [PMID: 30279985 DOI: 10.1007/s13755-018-0056-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2018] [Accepted: 09/08/2018] [Indexed: 10/28/2022] Open
Abstract
Consumption of energy is a national and international phenomenon that showed increase in market spread and profits from 1990 and made the emergence of many brands. Energy drinks are aggressively marketed with the claim that these products give an energy boost to improve physical and cognitive performance. However, studies supporting these claims are limited. The study examines the new phenomena of energy drinks among university students in Lebanon, based on the participants' personnel characteristics, university grade and the impact on health status. The study also determined whether high frequency of consumption was correlated with negative physical health symptoms. A cross-sectional study survey was undertaken on students aged between 18 and 30 years in private university over three branches (Beirut, Tripoli and Saida). A self-administered questionnaire was used inquiring about socio-demographic characteristics, consumption patterns and side effect of energy drinks. Data was analyzed using SPSS 24. Findings showed a serious concern exists for the health and safety of the most at risk students who engaged in daily energy drink usage when two-thirds of these reported difficulties sleeping, more than one experienced heart palpitation and blood pressure; one-third had anxiety, nervousness and feeling thirsty, and one fifth indicated tiredness and headache. Such symptoms are reported with excessive consumption of caffeine that had adverse health effect on the body.
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Affiliation(s)
- Jana Dwaidy
- 1Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
| | - Anjie Dwaidy
- 1Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
| | - Hanin Hasan
- 1Faculty of Health Sciences, Beirut Arab University, Beirut, Lebanon
| | - Seifedine Kadry
- 2Department of Mathematics and Computer Science, Faculty of Science, Beirut Arab University, Beirut, Lebanon
| | - Balamurugan Balusamy
- 3School of Computer Science and Engineering, Galgotias University, Greater Noida, UP India
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Balusamy B, Varma VTS, Grandhi SSMY. Challenges in Big Data Analytics. Big Data Analytics 2017. [DOI: 10.1201/b21822-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
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Lawanyashri M, Balusamy B, Subha S. Energy-aware hybrid fruitfly optimization for load balancing in cloud environments for EHR applications. Informatics in Medicine Unlocked 2017. [DOI: 10.1016/j.imu.2017.02.005] [Citation(s) in RCA: 32] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022] Open
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16
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Balusamy B, Krishna PV. Simplified and efficient framework for managing roles in cloud-based transaction processing systems using attribute-based encryption. IJCSE 2017. [DOI: 10.1504/ijcse.2017.082878] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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17
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Balusamy B, Karthikeyan K, Sangaiah AK. Ant colony-based load balancing and fault recovery for cloud computing environment. ACTA ACUST UNITED AC 2017. [DOI: 10.1504/ijaip.2017.082980] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Krishna PV, Balusamy B. Simplified and efficient framework for managing roles in cloud-based transaction processing systems using attribute-based encryption. IJCSE 2017. [DOI: 10.1504/ijcse.2017.10003828] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Murali T, Balusamy B, Thangavelu A, Sangaiah AK, Priya GN. Providing efficient user management for large-scale enterprise by achieving high scalability over cloud. IJIPT 2017. [DOI: 10.1504/ijipt.2017.10008427] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Priya GN, Sangaiah AK, Thangavelu A, Murali T, Balusamy B. Providing efficient user management for large-scale enterprise by achieving high scalability over cloud. IJIPT 2017. [DOI: 10.1504/ijipt.2017.087545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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