Skip to main content


Journal Issues

Tropical Cyclone Intensity Estimation using Channel Attentive Dense Convolutional Neural Network
S. Jayasree
Pages: 1-7 | First Published: 05 Mar 2024
Full text | Abstract | Purchase | References | Request permissions

Abstract
One of the most important aspects of tropical cyclone (TC) forecasting and disaster warning/management is accurately calculating TC intensity. The Dvorak technique—as well as a few upgraded versions—has been used by forecasters worldwide for more than 40 years to estimate temperature intensity. Nevertheless, the operational Dvorak techniques that are mainly employed by different agencies have a number of drawbacks, including intrinsic subjectivity that results in conflicting intensity estimates across different basins. CNN model gives more training parameters to enhance the overall training process and this model is enhanced with channel attention (CA) and spatial attention (SA) layer to gain valuable results. SA layer tries to pay extra consideration to the semantic-interrelated regions, instead of considering all image area similarly. SA layer does not contain the visual feature to calculate the weight, so the CA layer presents the visual feature
Keywords: Tropical cyclone intensity, Channel attentive dense convolutional neural network, DenseNet-121 CNN, Channel attentive layer, Spatial attentive layer

References 
1. Jin, Qingwen, Xiangtao Fan, Jian Liu, Zhuxin Xue, and Hongdeng Jian. "Estimating
tropical cyclone intensity in the South China Sea using the XGBoost Model and FengYun Satellite images." Atmosphere 11, no. 4 (2020): 423.
2. Velden, Christopher S., and Derrick Herndon. "A consensus approach for estimating tropical cyclone intensity from meteorological satellites: SATCON." Weather and Forecasting 35, no. 4 (2020): 1645-1662.
3. Zhuo, Jing-Yi, and Zhe-Min Tan. "Physics-augmented deep learning to improve tropical cyclone intensity and size estimation from satellite imagery." Monthly Weather Review 149, no. 7 (2021): 2097-2113.
4. Chen, Rui, Weimin Zhang, and Xiang Wang. "Machine learning in tropical cyclone forecast modeling: A review." Atmosphere 11, no. 7 (2020): 676.
5. Asif, Amina, Muhammad Dawood, Bismillah Jan, Javaid Khurshid, Mark DeMaria, and Fayyaz ul Amir Afsar Minhas. "PHURIE: hurricane intensity estimation from infrared satellite imagery using machine learning." Neural Computing and Applications 32 (2020): 4821-4834.
6. Charrua, Alberto Bento, Rajchandar Padmanaban, Pedro Cabral, Salomão Bandeira, and Maria M. Romeiras. "Impacts of the tropical cyclone idai in mozambique: A multi-temporal landsat satellite imagery analysis." Remote Sensing 13, no. 2 (2021): 201.
7. Yu, Peng, Johnny A. Johannessen, Xiao-Hai Yan, Xupu Geng, Xiaojing Zhong, and Lin Zhu. "A study of the intensity of Tropical Cyclone Idai using dual-polarization Sentinel-1 data." Remote Sensing 11, no. 23 (2019):2837.
8. Lu, Xiaoqin, Hui Yu, Xiaoming Yang, and Xiaofeng Li. "Estimating tropical cyclone size in the Northwestern Pacific from geostationary satellite infrared images." Remote Sensing 9, no. 7 (2017): 728.
9. Mohan, Preeya, and Eric Strobl. "The short-term economic impact of tropical Cyclone Pam: an analysis using VIIRS nightlightsatellite imagery." International journal of remote sensing 38, no. 21 (2017): 5992-6006.
10. Hoque, Muhammad Al-Amin, Stuart Phinn, Chris Roelfsema, and Iraphne Childs. "Tropical cyclone disaster management using remote sensing and spatial analysis: A review." International journal of disaster risk reduction 22 (2017): 345-354.
11. Zhang, Caiyun, Sara Denka Durgan, and David Lagomasino. "Modeling risk of mangroves to tropical cyclones: A case study of Hurricane Irma." Estuarine, Coastal and Shelf Science 224 (2019): 108-116.
12. Kim, Minsang, Myung-Sook Park, Jungho Im, Seonyoung Park, and Myong-In Lee. "Machine learning approaches for detecting tropical cyclone formation using satellite data." Remote Sensing 11, no. 10 (2019): 1195.
13. Maskey, Manil, Rahul Ramachandran, Muthukumaran Ramasubramanian, Iksha
Gurung, Brian Freitag, Aaron Kaulfus, Drew Bollinger, Daniel J. Cecil, and Jeffrey Miller. "Deepti: Deep-learning-based tropical cyclone intensity estimation system." IEEE journal of
selected topics in applied Earth observations and remote sensing 13 (2020): 4271-4281.

A Diagnostic of Autism Spectrum Disorder approaches based on Machine Learning
T.Ravishankar
Pages: 8-16 | First Published: 05 Mar 2024
Full text | Abstract | Purchase | References | Request permissions

Abstract
Children with autism spectrum disorders (ASD) have some disorder activities. Usually, they cannot speak fluently. Instead, they use signs and pointing words to make a relationship. One of the most challenging tasks for caregivers understands their needs but the early diagnosis of the disease can make it much easier. In the beginning stage the main problem begins with the kids and it keeps going on till adult. Thestudyextents of medical diagnosis, in this article there is an effort to discover the opportunity to use Logistic Regression, SVM, Naïve Bayes and RF for forecasting and investigate of ASD problems in a child. The important features of this area are covered and represented with the literature-based classification of the research. The main features of fMRI and an overview of ML’s general classification pipeline are offered.To check the generalizability of the outcome the entire data set and severe methods are necessary. ML technologies are expected to advance significantly in the coming years, contributing to the diagnosis of ASD and helping clinicians rapidly.
Keywords : Autism spectrum disorder (ASD), biomarkers, functional magnetic resonance imaging (fMRI), Machine Learning, KNN, Logistic Regression, SVM.

References 
1. American Psychiatric Association (2013). Diagnostic and statistical manual of mental disorders: Dsm-5. Arlington, TX: American Psychiatric Association. doi: 10.1176/appi.books.9780890425596.
2. Yasuhara, A. (2010). Correlation between eeg abnormalities and symptoms of autism spectrum disorder (asd). Brain Dev. 32, 791–798. doi: 10.1016/j.braindev. 2010.08.010
3. Huang, Z.-A., Liu, R., and Tan, K. C. (2020). “Multi-task learning for efficient diagnosis of asd and adhd using resting-state fmri data,” in Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN).
4. World Health Organization (2013). Meeting report: Autism spectrum disorders and other developmental disorders: From raising awareness to building capacity: World health organization, Geneva, Switzerland 16-18 september 2013. Geneva: World Health Organization.
5. Libero, L. E., DeRamus, T. P., Lahti, A. C., Deshpande, G., and Kana, R. K. (2015). Multimodal neuroimaging-based classification of autism spectrum disorder using anatomical, neurochemical, and white matter correlates.
6. Xu, M., Calhoun, V., Jiang, R., Yan, W., and Sui, J. (2021). Brain imaging-based machine learning in autism spectrum disorder: Methods and applications. J. Neurosci. Methods 361:109271. doi: 10.1016/j.jneumeth.2021.109 271.
7. Sivapalan, S., and Aitchison, K. J. (2014). Neurological structure variations in individuals with autism spectrum disorder: A review. KlinikPsikofarmakolojiBulteni Bull. Clin. Psychopharmacol. 24, 268–275. doi: 10.5455/bcp.20140903110206.
8. Zhang, Z., Li, G., Xu, Y., and Tang, X. (2021). Application of artificial intelligence in the mri classification task of human brain neurological and psychiatric diseases: A scoping review. Diagnostics 11:1402. doi: 10.3390/diagnostics11081402.
9. Thabtah, Fadi. "Machine learning in autistic spectrum disorder behavioral research: A review and ways forward. (2018) " Informatics for Health and Social Care : 1-20.
10. Dr.P.Sujatha , T.Ravishankar “The Role Of Machine Learning Models For Healthcare Applications” Volume 88, Issue 10 (Nov -2023).
11. Vaishali, R., and R. Sasikala. "A machine learning based approach to classify Autism with optimum behaviour sets. (2018) " International Journal of Engineering & Technology 7(4):
12. Beibin Li ; Sachin Mehta ; Deepali Aneja ; ClaireFoster ; PamelaVentola ; Frederick Shic ; Linda Shapiro, “A Facial Affect Analysis System for Autism Spectrum Disorder”, 2019.
13. M. Argumedes, M. J. Lanovaz, and S. Lariv´ee, “Brief report: Impact of challenging behavior on parenting stress in mothers and fathers of children with autism spectrum disorders,” Journal of autism and developmental disorders, vol. 48, no. 7, pp. 2585–2589, 2018.
14. Che Zawiyah Che Hasan, Rozita Jailani and Nooritawati Md Tahir, “ANN and SVM Classifiers in Identifying Autism Spectrum Disorder Gait Based on Three-Dimensional Ground Reaction Forces”, October 2018.
15. Jayatilleka I, Halgamuge MN (2020) Internet of Things in healthcare: Smart devices, sensors, and systems related to diseases and health conditions, in Real-Time Data Analytics for Large Scale Sensor Data. Elsevier, Amsterdam, pp 1–35.
16. Fadi Fayez Thabtah (2017), “Autistic Spectrum Disorder Screening Data for Adult”., https://archive.ics.uci.edu/ml/machine-learningdatabases/00426/.
17. E. Pisula and A. Porebowicz-D¨orsmann, “Family functioning, parenting stress and quality of life in mothers and fathers of polish children with high functioning autism or asperger syndrome,” PloS one, vol. 12, no. 10, 2017.
18. S. Alkhalifah and H. Aldhalaan, “Telehealth services for children with autism spectrum disorders in rural areas of the kingdom of saudiarabia: Overview and recommendations,” JMIR pediatrics and parenting, vol. 1, no. 2, p. e11402, 2018.

Managing Future Healthcare Industries Using Blockchain
Mr. P. Loganathan
Pages: 17-27 | First Published: 05 Mar 2024
Full text | Abstract | Purchase | References | Request permissions

Abstract
Blockchain is an emerging peer-to-peer distributed ledger technology with a decentralized architecture that ensures immutability, enhanced security, and faster settlement without third-party involvement. It’s growth is significant and revolutionary in healthcare as the privacy of health records is primary and data leakage is unacceptable. Also, the current managing data in healthcare is centralized with a possible risk of data loss in case of natural disasters. This can be avoided by adapting to this decentralized network. It’s ledger technology helps cryptographically to securely transfer patient medical records, resolve costly mistakes from miscommunication and help to achieve breakthroughs in genomics. Managing medical supply chain and drug traceability are guaranteed with true transparency, high security, speed, and efficiency as these are the key features of Blockchain Technology universal to any field. In this paper, we have discussed how blockchain-based applications can provide personalized patient health attention in the course of time. By incorporating all these important aspects through blockchain, managing healthcare industries could reshape the existing and have crucial improvements. Issues of implementing blockchain over a large scale and computational constraints are highlighted. The practicality of involving blockchain in healthcare is discussed through case studies and available technology trends. This paper outlines various approaches to augment the usage of blockchain technology in the healthcare industry in the future.
Keywords: Blockchain, Decentralized architecture, Enhanced Security, Distributed Ledger Technology, Cryptographically secure, Drug Traceability, Breakthrough in genomics.

References 
[1] Research on medical system based on blockchain technology. Qu Jia, 2021 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8078365/
[2] Blockchain Technology in Healthcare: A Systematic Review 2019, Cornelius C. Agbo ,Qusay H. Mahmoud andJ. Mikael Eklund
https://www.mdpi.com/2227-9032/7/2/56/htm
[3] BBDS: Blockchain-based data sharing for electronic medical records in cloud environments. Xia, Qi, Emmanuel Sifah, AblaSmahi, Sandro Amofa, and Xiaosong Zhang,2018 https://www.researchgate.net/publication/316354308_BBDS_Blockchain-Based_Data_Sharing_for_Electronic_Medical_Records_in_Cloud_Environments
[4] Lightweight Blockchain for Healthcare. L. Ismail, H. Materwala and S. Zeadally, 2019
https://ieeexplore.ieee.org/abstract/document/8869754
[5] Medrec: Using blockchain for medical data access and permission management Asaph Azaria, Ariel Ekblaw, Thiago Vieira and Andrew Lippman, 2019
http://dpnm.postech.ac.kr/cs490u/MedRec.pdf
[6] Blockchain in medical informatics. JiaQua, 2022
https://www.sciencedirect.com/science/article/abs/pii/S2452414X2100056X
[7] Multi-tier blockchain framework for IoT-EHRs systems Badr, Shaimaa, Ibrahim Gomaa, and EmadAbd-Elrahman, 2018
https://www.researchgate.net/publication/328757129_Multi-tier_Blockchain_Framework_for_IoT-EHRs_Systems
[8] Blockchain in healthcare and health sciences—A scoping review, 2020. Anton Hasselgren, Katina Kralevska, DaniloGligoroski, SindreA.Pedersen, ArildFaxvaag
https://www.sciencedirect.com/science/article/pii/S138650561930526X
[9] Blockchain for Healthcare Sector-Analytical Review. Nail Adeeb Ali Abdu and Zhaoshun Wang, 2021
https://iopscience.iop.org/article/10.1088/1757-899X/1110/1/012001/meta
[10] A simulation-based AHP approach to analyze the scalability of EHR systems using blockchain technology in healthcare institutions. Alexander Garrido , Leonardo Juan Ramírez Lopez , Nicolas Beltran Alvarez, 2021
https://www.sciencedirect.com/science/article/pii/S2352914821000666
[11] Book Referred:
https://www.researchgate.net/publication/345045424_BLOCKCHAIN_FUNDAMENTALS_TEXT_BOOK_Fundamentals_of_Blockchain
[12] https://medrec.media.mit.edu/technical/

Predicting Crime Rate using Machine Learning
Dr. V Sowmeya
Pages: 28-33 | First Published: 05 Mar 2024
Full text | Abstract | Purchase | References | Request permissions

Abstract
Crime is one of our society's most serious and pervasive problems, and preventing it is a critical duty. This necessitates keeping note of all offences and creating a database for future reference. The present issue is keeping a reliable crime record and analyzing this data to aid in the prediction and resolution of future crimes. The objective of this study is to analyze dataset, which consist of numerous crimes and predicting the type of crime, which may happen in future depending upon various conditions. In this work, we will be using the technique of machine learning and data science for crime prediction of Indian crime data set. Crime rate prediction is a methodical way to spotting crime. This algorithm can anticipate and depict crime-prone areas. Using the notion of machine learning, we may extract previously unknown, meaningful information from unstructured data. The extraction of new information is anticipated using current datasets. Crime is a perilous and widespread societal issue that affects people all around the world. Crime has an impact on people's quality of life, economic prosperity, and the nation's reputation. To safeguard their communities from crime, modern technology and novel techniques to enhancing crime analytics are required. We present a system that can analyse, identify, and predict various crime probabilities in a given location. This paper describes many sorts of criminal analysis and crime rate prediction using machine learning approaches.

Reference 
[1] A. Bogomolov, B. Lepri, J. Staiano, N. Oliver, A. Pent-land, “Once upon a crime: towards crime prediction from demographics and mobile data”, IEEE, Proceedings of the 16th international conference on multimodal interaction, 2014, pp. 427-434.
[2] H. Adel, and R. Mahmoud, ”Crime in relation to urban design. Case study: the greater Cairo region,” Ain Shams Eng. J., vol. 7, no. 3, pp. 925-938, 2016.
[3] J. L. LeBeau,”The Methods and Measure and the spatial Dynamics of Rape” Journal of Quantitative Criminology, Vol.3, No.2, pp.125-141, 1987.
[4] Andrey Bogomolov, Bruno Lepri, Jacopo Staiano, Nuria Oliver, Alex Pentland. ”Once Upon a Crime: Towards Crime Prediction from Demographics and Mobile Data”, in ACM International Conference on Multimodal Interaction (ICMI 2014).
[5] Shiju Sathya, M. S., Surya S Gangadharan, First,” Crime Analysis and Prediction Using Data Mining” International Conference on Networks Soft Computing (ICNSC), 2014.
[6] Sunil Yadav, Ajit Yadav, Rohit Vishwakarma and Nikhilesh Yadav,” Crime pattern detection, analysis and prediction, International Conference on Electronics, Communication and Aerospace Technology (ICECA), 2017.

Future of Industry 5.0 in Society
Manikandan K
Pages: 34-45 | First Published: 05 Mar 2024
Full text | Abstract | Purchase | References | Request permissions

Abstract
Industry 4.0 has been provided for the last 10 years to benefit the industry and the shortcomings; finally, the time for industry 5.0 has arrived. Smart factories are increasing the business productivity; therefore, industry 4.0 has limitations. In this paper, there is a discussion of the industry 5.0 opportunities as well as limitations and the future research prospects. Industry 5.0 is changing paradigm and brings the resolution since it will decrease emphasis on the technology and assume that the potential for progress is based on collaboration among the humans and machines. The industrial revolution is improving customer satisfaction by utilizing personalized products. In modern business with the paid technological developments, industry 5.0 is required for gaining competitive advantages as well as economic growth for the factory. The paper is aimed to analyze the potential applications of industry 5.0. At first, there is a discussion of the definitions of industry 5.0 and advanced technologies required in this industry revolution. There is also discussion of the applications enabled in industry 5.0 like healthcare, supply chain, production in manufacturing, cloud manufacturing, etc. The technologies discussed in this paper are big data analytics, Internet of Things, collaborative robots, Blockchain, digital twins and future 6G systems. The study also included difficulties and issues examined in this paper head to comprehend the issues caused by organizations among the robots and people in the assembly line
Keywords: Industry 5.0, Human machine collaboration, Supply chain, Disaster recovery

References 
1. Longo F, Padovano A, Umbrella S. Value-oriented and ethical technology engineering in industry 5.0: a human-centric perspective for the design of the factory of the future. Appl Sci. 2020;10(12):4182. doi: 10.3390/app10124182. [CrossRef] [Google Scholar]
2. Pathak A, Kothari R, Vinoba M, Habibi N, Tyagi VV. Fungal bioleaching of metals from refinery spent catalysts: a critical review of current research, challenges, and future directions. J Environ Manag. 2021;80:111789. doi: 10.1016/j.jenvman.2020.111789. [PubMed] [CrossRef] [Google Scholar]
3. He D, Ma M, Zeadally S, Kumar N, Liang K. Certificateless public key authenticated encryption with keyword search for industrial internet of things. IEEE Trans Ind Inf. 2017;14(8):3618–3627. doi: 10.1109/TII.2017.2771382. [CrossRef] [Google Scholar]
4. Leone LA, Fleischhacker S, Anderson-Steeves B, Harpe K, Winkler M, Racin E, Baquero B, Gittelsohn J. Healthy food retail during the COVID-19 pandemic: challenges and future directions. Int J Environ Res Public Health. 2020;17(20):7397. doi: 10.3390/ijerph17207397. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
5. Majumdar A, Garg H, Jain R. Managing the barriers of industry 4.0 adoption and implementation in textile and clothing industry: interpretive structural model and triple helix framework. Comput Ind. 2021;125:103372. doi: 10.1016/j.compind.2020.103372. [CrossRef] [Google Scholar]
6. Angelopoulos A, Michailidis ET, Nomikos N, Trakadas P, Hatziefremidis A, Voliotis S, Zahariadis T. Tackling faults in the industry 4.0 era—a survey of machine-learning solutions and key aspects. Sensors. 2020;20(1):109. doi: 10.3390/s20010109. [PMC free article] [PubMed] [CrossRef] [Google Scholar]
7. Nahavandi S. Industry 5.0—a human-centric solution. Sustainability. 2019;11(16):4371. doi: 10.3390/su11164371. [CrossRef] [Google Scholar]
8. Javaid M, Haleem A. Critical components of industry 5.0 towards a successful adoption in the field of manufacturing. J Industr Integr Manag. 2020;5(03):327–348. doi: 10.1142/S2424862220500141. [CrossRef] [Google Scholar]
9. Deepa N, Pham QV, Nguyen DC, Bhattacharya S, Prabadevi B, Gadekallu TR, ..., Pathirana PN (2022) A survey on blockchain for big data: approaches, opportunities, and future directions. Future Generation Computer Systems

An Introduction to Digital Twin
K.Kowsalya
Pages: 46-53 | First Published: 05 Mar 2024
Full text | Abstract | Purchase | References | Request permissions

Abstract
Digital Twin technology is an emerging concept that has become the centre of attention for industry and, in more recent years, academia. The advancements in industry 4.0 concepts have facilitated its growth, particularly in the manufacturing industry. The Digital Twin is defined extensively but is best described as the effortless integration of data between a physical and virtual machine in either direction. The challenges, applications, and enabling technologies for Artificial Intelligence, Internet of Things (IoT) and Digital Twins are presented. A review of publications relating to Digital Twins is performed, producing a categorical review of recent papers. The review has categorised them by research areas: manufacturing, healthcare and smart cities, discussing a range of papers that reflect these areas and the current state of research. The paper provides an assessment of the enabling technologies, challenges and open research for digital twin.

Reference 
1. Kritzinger, W.; Karner, M.; Traar, G.; Henjes, J.; Sihn, W. Digital Twin in Manufacturing: A Categorical Literature Review and Classification. IFAC-Pap. 2018, 51, 1016–1022.
2. Negri, E.; Fumagalli, L.; Macchi, M. A Review of the Roles of Digital Twin in CPS-Based Production Systems. Procedia Manuf. 2017, 11, 939–948.
1. Borovkov, A.I.; Gamzikova, A.A.; Kukushkin, K.V.; Ryabov, Y.A. Digital Twins in the High-Technology Manufacturing Industry. A Preliminary Research Report (September 2019); POLYTECH-PRESS: St. Petersburg, Russia, 2019; ISBN 978-5-7422-6922-9. Wang, Y.; Wang, X.; Liu, A. Digital Twin-Driven Analysis of Design Constraints. Procedia CIRP 2020, 91, 716–721.
2. Soderberg, R.; Wärmefjord, K.; Carlson, J.S.; Lindkvist, L. Toward a Digital Twin for Real-Time Geometry Assurance in Individualized Production. CIRP Ann. 2017, 66, 137–140.
3. Schleich, B.; Anwer, N.; Mathieu, L.; Wartzack, S. Shaping the Digital Twin for Design and Production Engineering. CIRP Ann. 2017, 66, 141–144.
4. Akintseva, A.V.; Pereverzev, P.P.; Omel’chenko, S.V.; Kopyrkin, A.A. Digital Twins and Multifactorial Visualization of Shaping in CNC Plunge-Cut Grinding. Russ. Eng. Res. 2021, 41, 671–675.
5. Boulos, M.K.; Zhang, P. Digital Twins: From Personalised Medicine to Precision Public Health. J. Pers. Med. 2021, 11, 745.
6. Gaebel, J.; Keller, J.; Schneider, D.; Lindenmeyer, A.; Neumuth, T.; Franke, S. The Digital Twin: Modular Model-Based Approach to Personalized Medicine. Curr. Dir. Biomed. Eng. 2021, 7, 223–226.

A System for Monitoring Medical Care based on the IOT and Sensors
Vijay M
Pages: 54-62 | First Published: 05 Mar 2024
Full text | Abstract | Purchase | References | Request permissions

Abstract
Due to the spread of the new coronavirus, medical care has become very important in many countries these days. IoT-based health monitoring systems are thus the ideal answer for such an epidemic from this perspective. The Internet of Things (IoT) is a recent internet revolution and a burgeoning field of study, particularly in the field of medicine. With the growing use of wearable sensors and smartphones, this remote healthcare monitoring has evolved at such a pace. IoT health monitoring helps doctors correctly diagnose health conditions, not only to prevent the spread of diseases, even when they are out of reach. In this paper, we present a portable physiological monitoring system that can continuously monitor heart rate, temperature, and other basic parameters of a patient's room. Designed a continuous monitoring and control device that uses remote communication based on Wi-Fi module to display patient status and store patient information on the server. A remote health monitoring system built with the Internet of Things is intended to give authorized users access to the data using any IoT platform. Based on the received data, clinicians can remotely manage your health. Diagnose the disease. Oxygen impairment can occur at different stages of Covid-19 and is not limited to critically ill patients. In fact, a phenomenon called "happy hypoxia" has been observed clinically in patients with COVID-19 who have very low oxygen levels but otherwise appear to be fine. A pulse oximeter, usually a small device that you slip on your fingertip or rub against your earlobe, uses refraction of infrared light to measure the amount of oxygen bound to red blood cells. The proposed system monitors SPO2 levels to track Covid-19 symptoms.
Index Terms— IoT, Wi-Fi, Oxygen, SPO2, ear-lobe, hypoxia.

References 
[1]. S.H. Almotiri, M. A. Khan, and M. A. Alghamdi. Mobile health (m- health) system in the context of iot. In 2016 IEEE 4th International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), pages 39–42, Aug 2016.
[2]. Gulraiz J. Joyia, Rao M. Liaqat, Aftab Farooq, and Saad Rehman, Internet of Medical Things (IOMT): Applications, Benefits and Future Challenges in Healthcare Domain, Journal of Communications Vol. 12, No. 4, April 2017.
[3]. Shubham Banka, Isha Madan and S.S. Saranya, Smart Healthcare Monitoring using IoT. International Journal of Applied Engineering Research ISSN 0973-4562 Volume 13, Number 15, pp. 11984-11989, 2018.
[4]. K. Perumal, M. Manohar, A Survey on Internet of Things: Case Studies, Applications, and Future Directions, In Internet of Things: Novel Advances and Envisioned Applications, Springer International Publishing, (2017) 281- 297.
[5]. S.M. Riazulislam, Daehankwak, M.H.K.M.H., Kwak, K.S.: The Internet of Things for Health Care: A Comprehensive Survey. In: IEEE Access (2015).
[6]. P. Rizwan, K. Suresh. Design and development of low investment smart hospital using Internet of things through innovative approaches, Biomedical Research. 28(11) (2017).
[7]. K.R. Darshan and K.R. Anandakumar, “A comprehensive review on usage of internet of things (IoT) in healthcare system,” in Proc. International Conference on Emerging Research in Electronics, Computer Science and Technology, 2015.
[8]. Internet of Things (IoT): Number of Connected Devices Worldwide From 2012 to 2020 (in billions). [Online].Available:https://www.statista.com/statistics/471264/iotnumberof- connected- devices-worldwide/
[9]. P. Chavan, P. More, N. Thorat, S. Yewale, and P. Dhade, “ECG - Remote patient monitoring using cloud computing,” Imperial Journal of Interdisciplinary Research, vol. 2, no. 2, 2016.
[10]. Ruhani Ab. Rahman, NurShima Abdul Aziz, MurizahKassim, Mat IkramYusof, IoT- based Personal Health Care Monitoring Device for Diabetic Patients ,978-1-5090- 4752-9/17/2017 IEEE.
[11]. Valsalan P, Surendran P, Implementation of an Emergency Indicating Line Follower and Obstacle Avoiding Robot, 16th International Multi-Conference on Systems, Signals and Devices, SSD 2019.
[12]. Valsalan P, Shibi O, CMOS-DRPTL Adder Topologies, Proceedings of the 2018 International Conference on Current Trends towards Converging Technologies, ICCTCT 2018.
[13]. Valsalan P, Manimegalai P, Intend of power-delay optimized Kogge-Stone based Carry Select Adder, ARPN Journal of Engineering and Applied Sciences, 2018.
[14]. Valsalan P, Surendran P, Iot based breath sensor for mycobacterium tuberculosis, Journal of Advanced Research in Dynamical and Control Systems, 2018.
[15]. Firas Hasan Bazzari. "Available Pharmacological Options and Symptomatic Treatments of Multiple Sclerosis." Systematic Reviews in Pharmacy 9.1 (2018), 17-21. Print. doi:10.5530/srp.2018.1.4
[16]. Valsalan P, Manimegalai P, Analysis of area delay optimization of improved sparse channel adder, Pakistan Journal of Biotechnology, 2017.
[17]. Valsalan P, Sankaranarayanan K, Design of adder circuit with fault tolerant technique for power minimization, International Journal of Applied Engineering Research, 2014.
[18]. Rajendran T et al. “Recent Innovations in Soft Computing Applications”, Current Signal Transduction Therapy. Vol. 14, No. 2, pp. 129 – 130, 2019.

Artificial Intelligence(AI) & Machine Learning (ML)
Jayashree.S
Pages: 63-72 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Abstract
This article delves into the intricate relationship between Artificial Intelligence (AI) and Machine Learning (ML), highlighting their collaborative role in reshaping industries, driving innovation, and addressing contemporary challenges. AI, as a broad discipline, seeks to imbue machines with cognitive abilities to mimic human intelligence, while ML, a subset of AI, focuses on algorithms and statistical models enabling systems to learn and improve from experience without explicit programming. The article provides a comprehensive overview of foundational concepts in AI and ML, including supervised, unsupervised, and reinforcement learning paradigms. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are highlighted for their efficacy in tasks such as image recognition, natural language processing, and sequential data analysis. Despite these advancements, the article underscores challenges confronting AI and ML adoption, including data privacy concerns, algorithmic biases, and ethical considerations surrounding autonomous decision-making. It emphasizes the importance of transparency, fairness, and accountability in the development and deployment of AI and ML systems to foster trust and mitigate societal risks.Looking forward, the article envisions the transformative impact of AI and ML across diverse sectors, from healthcare and finance to transportation and manufacturing. It discusses emerging applications such as personalized medicine, predictive maintenance, and autonomous vehicles, showcasing how AI and ML are revolutionizing processes, enhancing efficiency, and driving business innovation. In conclusion, the article advocates for a holistic approach to AI and ML integration, balancing technological advancements with ethical considerations and societal implications. By fostering interdisciplinary collaboration, responsible governance, and continuous research, the synergistic partnership between AI and ML holds the promise of addressing global challenges and ushering in a more prosperous and inclusive future.

Blockchain Technology: Architecture, Consensus, and Future Trends
P Shalini
Pages: 73-84 | First Published: 05 Mar 2024
Full text | Abstract | Purchase | References | Request permissions

Abstract
Blockchain, the foundation of Bitcoin, has received extensive attentions recently. Blockchain serves as an immutable ledger which allows transactions take place in a decentralized manner. Blockchain-based applications are springing up, cov- ering numerous fields including financial services, reputation system and Internet of Things (IoT), and so on. However, there are still many challenges of blockchain technology such as scalability and security problems waiting to be overcome. This paper presents a comprehensive overview on blockchain technology. We provide an overview of blockchain architechture firstly and compare some typical consensus algorithms used in different blockchains. Furthermore, technical challenges and recent advances are briefly listed. We also lay out possible future trends for blockchain.
Index Terms—Blockchain, decentralization, consensus, scala- bility

References 
[1] “State of blockchain q1 2016: Blockchain funding overtakes bitcoin,” 2016. [Online]. Available: http://www.coindesk.com/ state-of-blockchain-q1-2016/
[2] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2008. [Online]. Available: https://bitcoin.org/bitcoin.pdf
[3] G. W. Peters, E. Panayi, and A. Chapelle, “Trends in crypto-currencies and blockchain technologies: A monetary theory and regulation perspective,” 2015. [Online]. Available: http://dx.doi.org/10.2139/ssrn. 2646618 [4] G. Foroglou and A.-L. Tsilidou, “Further applications of the blockchain,” 2015.
[5] A. Kosba, A. Miller, E. Shi, Z. Wen, and C. Papamanthou, “Hawk: The blockchain model of cryptography and privacy-preserving smart contracts,” in Proceedings of IEEE Symposium on Security and Privacy (SP), San Jose, CA, USA, 2016, pp. 839–858.
[6] B. W. Akins, J. L. Chapman, and J. M. Gordon, “A whole new world: Income tax considerations of the bitcoin economy,” 2013. [Online].
Available: https://ssrn.com/abstract=2394738
[7] Y. Zhang and J. Wen, “An iot electric business model based on the protocol of bitcoin,” in Proceedings of 18th International Conference on Intelligence in Next Generation Networks (ICIN), Paris, France, 2015, pp. 184–191.
[8] M. Sharples and J. Domingue, “The blockchain and kudos: A distributed system for educational record, reputation and reward,” in Proceedings of 11th European Conference on Technology Enhanced Learning (EC-TEL 2015), Lyon, France, 2015, pp. 490–496.
[9] C. Noyes, “Bitav: Fast anti-malware by distributed blockchain consensus and feedforward scanning,” arXiv preprint arXiv:1601.01405, 2016.
[10] I. Eyal and E. G. Sirer, “Majority is not enough: Bitcoin mining is vulnerable,” in Proceedings of International Conference on Financial Cryptography and Data Security, Berlin, Heidelberg, 2014, pp. 436– 454.
[11] A. Biryukov, D. Khovratovich, and I. Pustogarov, “Deanonymisation of clients in bitcoin p2p network,” in Proceedings of the 2014 ACM SIGSAC Conference on Computer and Communications Security, New York, NY, USA, 2014, pp. 15–29.
[12] F. Tschorsch and B. Scheuermann, “Bitcoin and beyond: A technical survey on decentralized digital currencies,” IEEE Communications Sur- veys Tutorials, vol. 18, no. 3, pp. 2084–2123, 2016.
[13] NRI, “Survey on blockchain technologies and related services,” Tech. Rep., 2015. [Online]. Available: http://www.meti.go.jp/english/press/ 2016/pdf/0531 01f.pdf
[14] D. Lee Kuo Chuen, Ed., Handbook of Digital Currency, 1st ed. Elsevier, 2015. [Online]. Available: http://EconPapers.repec.org/RePEc: eee:monogr:9780128021170
[15] V. Buterin, “A next-generation smart contract and decentralized appli- cation platform,” white paper, 2014.
[16] D. Johnson, A. Menezes, and S. Vanstone, “The elliptic curve digital signature algorithm (ecdsa),” International Journal of Information Se- curity, vol. 1, no. 1, pp. 36–63, 2001.
[17] V. Buterin, “On public and private blockchains,” 2015. [Online]. Available: https://blog.ethereum.org/2015/08/07/ on-public-and-private-blockchains/
[18] “Hyperledger project,” 2015. [Online]. Available: https://www. hyperledger.org/
[19] “Consortium chain development.” [Online]. Available: https://github. com/ethereum/wiki/wiki/Consortium-Chain-Development
[20] L. Lamport, R. Shostak, and M. Pease, “The byzantine generals prob- lem,” ACM Transactions on Programming Languages and Systems (TOPLAS), vol. 4, no. 3, pp. 382–401, 1982.
[21] S. King and S. Nadal, “Ppcoin: Peer-to-peer crypto-currency with proof- of-stake,” Self-Published Paper, August, vol. 19, 2012.
[22] “Bitshares - your share in the decentralized exchange.” [Online].
Available: https://bitshares.org/
[23] D. Schwartz, N. Youngs, and A. Britto, “The ripple protocol consensus algorithm,” Ripple Labs Inc White Paper, vol. 5, 2014.

Introduction to Neural Networks
M Lavanya
Pages: 88-101 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Abstract
The coupling of computer science and theoretical bases such as nonlinear dynamics and chaos theory allows the creation of ‘intelligent’ agents, such as artificial neural networks (ANNs), able to adapt themselves dynamically to problems of high complexity. ANNs are able to reproduce the dynamic interaction of multiple factors simultaneously, allowing the study of complexity; they can also draw conclusions on individual basis and not as average trends. These tools can offer specific advantages with respect to classical statistical techniques. This article is designed to acquaint gastroenterologists with concepts and paradigms related to ANNs. The family of ANNs, when appropriately selected and used, permits the maximization of what can be derived from available data and from complex, dynamic, and multidimensional phenomena, which are often poorly predictable in the traditional ‘cause and effect’ philosophy. Eur J Gastroenterol Hepatol 19:1046–1054
Ⓧc 2007 Wolters Kluwer Health | Lippincott Williams &
Wilkins.

References
K of special interest
KK of outstanding interest
1 McCulloch WS, Pitts WH. A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 1943; 5:115–133.
KK The first mathematical model of logical functioning of brain cortex (formal neuron) is exposed in the famous work of McCulloch and Pitts.
2 McClelland JL, Rumelhart DE, editors. Explorations in parallel distributed processing. Cambridge, Massachusetts: MIT Press; 1986.
KK This book is the historical reference text on the neurocomputing origins, which contains a comprehensive compilation of neural network theories and research.
3 Anderson JD, Rosenfeld E, editors. Neurocomputing: foundations of research. Cambridge, Massachusetts: MIT Press; 1988.
K An interesting book that collects the most effective works on the development of neural networks theory.
4 Hebb DO. The organization of behavior. New York: Wiley; 1949.
KK In this landmark book is developed the concept of the ‘cell assembly’ and explained how the strengthening of synapses might be a mechanism of learning.
5 Marr D. Approaches to biological information processing. Science 1975;
190:875–876.
K In this article, Marr, writing about his theoretical studies on neural networks, expanded such original hypotheses.
6 Rosenblatt F. The Perceptron. A probabilistic model for information storage and organization in the brain. Psychol Rev 1958; 65:386–408.
KK The first neural network learning by its own errors is developed in this hystorical work. 7 Widrow G, Hoff ME. Adaptive switching circuits. Institute of radio engineers, Western Electronic show & Convention, Convention record. 1960,
part 4:96–104.
8 Rumelhart DE, Hinton GE, Williams RJ. Learning internal representations by error propagation. In: Rumelhart DE, McClelland JL, editors. Parallel distributed processing. Vol. I. Boston: MIT Press; 1986. pp. 318–362.
9 Personnaz L, Guyon I, Dreyfus G. Collective computational properties of neural networks: new learning mechanisms. Phys Rev A 1986; 34: 4217–4228.
10 Gallant SI. Perceptron-based learning algorithms. IEEE Transaction on Neural Networks 1990; 1:179–192.
K An important paper that describes the main learning laws for training the neural networks models.
11 Wasserman PD. Neural computing: theory and practice. New York: Van Nostrand; 1989.
12 Aleksander I, Morton H. An introduction to neural computing. London: Chapman & Hall; 1990.
Two books containing systematic expositions of several neural networks models.
13 Fahlman SE. An empirical study of learning speed in back-propagation networks technical report CMU-CS-88-162. Pittsburg: Carnegie-Mellon University; 1988.
14 Le Cun Y. Generalization and network design strategie. In: Pfeifer R, Schreter Z, Fogelman-Soulie F, Steels L, editors. Connectionism in perspective. North Holland: Amsterdam; 1989. pp. 143–156.
K The performances and possible generalizations of Back-Propagation Algorithm are described by Fahlman and Le Cun.
15 Hinton GE. How neural networks learn from experience. Sci Am 1992;
267:144–151.
In this article there is a brief and more accessible introduction to Connectionism.
16 Matsumoto G. Neurocomputing. Neurons as microcomputers. Future Gen comp 1988; 4:39–51.
K The Matsumoto article is a concise and interesting review on Neural Networks.
17 NeuralWare. Neural computing. Pittsburgh, Pennsylvania: NeuralWare Inc.; 1993.
18 CLEMENTINE user manual. Integral Solutions Limited; 1997.
19 Von der Malsburg C. Self-organization of orientation sensitive cells in the striate cortex. Kybernetik 1973; 14:85–100.
20 Willshaw DJ, Von der Malsburg C. How patterned neural connection can be set up by Self-Organization. Proc R Soc London B 1976; 94: 431–445.
K The early network model that performs self-organization processes has been exposed in papers from Von der Malsburg and Willshaw.
21 Kohonen T. Self-organization and associative memories. Berlin-Heidelberg- New York: Springer; 1984.
22 Kohonen T. The self-organizing map. Proceedings IEEE 1990; 78: 1464–1480.
KK The most well-known and simplest self-organizing network model has been proposed by T. Kohonen.
23 Carpenter GA, Grossberg S. The ART of adaptive pattern recognition by a self-organizing neural network. Computer 1988; 21:77–88. 

24 Carpenter GA, Grossberg S. A massively parallel architecture for a self-organizing neural pattern recognition machine. In: Carpenter GA, Grossberg S, editors. Pattern recognition by self-organizing neural networks. Cambridge, MA: MIT Press; 1991.
K These works of Grossberg and Carpenter are very interesting contributions regarding the competitive learning paradigm.
25 Dietterich TG. Approximate statistical tests for comparing supervised classification learning algorithms. Neural Comput 1998; 7:1895–1924.
KK This article describes one of the most popular validation protocol, the 5 × 2 cross validation.
26 Buscema M. Genetic doping algorithm (GenD): theory and applications.
Exp Syst 2004; 21:63–79.
K A seminal paper on the theory of evolutionary algorithms.
27 Buscema M, Grossi E, Intraligi M, Garbagna N, Andriulli A, Breda M. An optimized experimental protocol based on neuro-evolutionary algorithms: application to the classification of dyspeptic patients and to the prediction of the effectiveness of their treatment. Artificial Intelligence Med 2005; 34:279–305.
KK A complex work that used techniques based on advanced neuro/evolutionary systems (NESs) such as Genetic Doping Algorithm (GenD), input selection (IS) and training and testing (T&T) systems to perform the discrimination between functional and organic dyspepsia and also the prediction of the outcome in dyspeptic patients subjected to Helicobacter pylori eradication therapy.
28 Andriulli A, Grossi E, Buscema M, Festa V, Intraligi M, Dominici PR, et al. Contribution of artificial neural networks to the classification and treatment of patients with uninvestigated dyspepsia. Digest Liver Dis 2003; 35: 222–231.
K A paper assessing the efficacy of neural networks to perform the diagnosis of gastro-oesophageal reflux disease (GORD). The highest predictive ANN’s performance reached an accuracy of 100% in identifying the correct diagnosis; this kind of data processing technique seems to be a promising approach for developing non-invasive diagnostic methods in patients suffering of GORD symptoms.
29 Pagano N, Buscema M, Grossi E, Intraligi M, Massini G, Salacone P, et al. Artificial neural networks for the prediction of diabetes mellitus occurrence in patients affected by chronic pancreatitis. J Pancreas 2004;
5 (Suppl 5):405–453.
K In this work several research protocols based on supervised neural networks are used to identify the variables related to diabetes mellitus in patients affected by chronic pancreatitis and presence of diabetes was predicted with an accuracy higher than 92% in single patients with this disease.
30 Sato F, Shimada Y, Selaru FM, Shibata D, Maeda M, Watanabe G, et al. Prediction of survival in patients with esophageal carcinoma using artificial neural networks. Cancer 2005; 103:1596–1605.

Quantum Computing : The Next Era of Computing
Dr. S. Babu
Pages: 102-106 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Abstract
Quantum computing is a Quantum Mechanism based computational framework, which has acquired a lot of interest in the early few decades. In contrast with the conventional computers, it has obtained an enhanced performance on various tasks. The study of Quantum Computers is Quantum Computing. Annealing, Entanglement, Tunneling and Superposition are some of the phenomena of Quantum mechanics used in Quantum computers to give the solutions to the problems which were unable to solve by human in their lifetime. The main objective of this paper is to reveal a brief idea about what is occurring in the Quantum Computing field and also the current state. In addition, the features of Quantum computing like Quantum parallelism, reverse computing and qubit computation is also summarized. The article also reveals the cause of great computing capabilities of Quantum computers in view of utilization of quantum entangled state. Based on the review concludes that the research on quantum computers requires the advanced sciences like Mathematics, Micro-Physics and Computer Technology.
Key Words: Quantum Computers, Quantum computing, Quantum Parallelism, Entanglement, Qubit.

Reference 
[1] Quantum Architectures and Computation Team (Microsoft and Google), “Defining and detecting quantum speedup”, Center for Quantum Information Science & Technology, University of Southern California, January 2014.
[2] Vitányi P., “Time, space, and energy in reversible computing’, In Proceedings of the 2nd conference on Computing Frontiers , PP 435-444, Ischia, Italy May 04 - 06, 2005.
[3] Scott Aaronson, “The Learnability of Quantum States”, University of Waterloo Institute for Quantum Computing, June 2005.
[4] D- Wave Computing Company, Computational Power Consumption and Speedup Summery, D-wave white paper, 2017
[5] I.D James, (August 2017), “A History of Microprocessor Transistor Count 1971 to 2017” Available: https://en.wikipedia.org/wiki/Transistor_count
[6] Yuanhao Wang,Ying Li, Zhang-qi Yin, and Bei Zeng, “16-qubit IBM universal quantum computer can be fully entangled”, March 2018, Unpublished.
[7] Gabriel Târziu, “Quantum Vs. Classical Logic:The Revisionist Approach”, Logos & Episteme,Vol. 3, Iss. 4, pp 579-590 , 2012.
[8] Janet Anders, Saroosh Shabbir, Stefanie Hilt, Eric Lutz, “Landauer’s principle in the quantum domain, Developing in computational model”, Cornell University Library quant-Phy, Vol-1 pp. 13-18, 2010
[9] Vishal Kumar, Asif Ali Laghari, Shahid Karim, Muhammad Shakir, Ali Anwar Brohi , “Comparison of Fog Computing & Cloud Computing” I.J. Mathematical Sciences and Computing, 2019, 1, 31-41, DOI: 10.5815/ijmsc.2019.01.03
[10] Zuhi Subedar, Ashwini Araballi, “ Hybrid Cryptography: Performance Analysis of Various Cryptographic Combinations for Secure Communication” I. J. Mathematical Sciences and Computing, 2020, 4, 35-41.
[11] Peter W. Shor, “Polynomial-Time Algorithms for Prime Factorization and Discrete Logarithms on a Quantum Computer”, IEEE Computer Society Press, January 1996
[12] Rodney Van Meter, “Quantum Computing’s Classical Problem, Classical Computing’s Quantum Problem”, Keio University, Japan Foundations of Physic, August 2014
[13] Andrew Lutomirski, Scott Aaronson, Edward Farhi, Peter Shor, “Breaking and making quantum money: toward a new quantum cryptographic protocol”, Massachusetts Institute of Technology, Cambridge, December 2009 [14] Scott Aaronson, Adam Bouland, Joseph Fitzsimons, and Mitchell Lee, “The Space Just Above BQP”, Massachusetts Institute of Technology, Cambridge, December 2014
[15] Paul Isaac Hagouel and Ioannis G. Karafyllidis, “Quantum Computers: Registers, Gates and Algorithms”, Proc. 28th International Conference on Microelectronics, Serbia, 2012,

Students Performance Analysis Using Moocs Platform
M. Kannan
Pages: 109-113 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Abstract
Our goal in this work was to provide interested academics with up-to-date information on the latest findings and investigations concerning student performance analysis and learning analytics in massively open online courses (MOOCs). Examining the application of performance prediction and learning analytics in MOOCs is the goal of this. To introduce readers to our work, we first provide literature-based explanations of key concepts. To help understand the relative relevance of the qualities and their relationships, details on a number of studies were then provided. Next is a summary of the outcomes of learning analytics and student performance prediction applied to MOOCs.

References 
[1] Pappano, Laura. "The Year of the MOOC." The New York Times 2.12 (2012): 2012.
[2] Kerr, J., et al. "Building and executing MOOCs: A practical review of GlasgowâA˘ Zs first two MOOCs (massive open online courses). ´ University of Glasgow." (2015).
[3] Ripley, Brian D. "The R project in statistical computing." MSOR Connections. The newsletter of the LTSN Maths, Stats and OR Network 1.1 (2001): 23-25.
[4] Paradis, Emmanuel, Julien Claude, and Korbinian Strimmer. "APE: analyses of phylogenetics and evolution in R language." Bioinformatics 20.2 (2004): 289-290.
[5] KlÃijsener, Marcus, and Albrecht Fortenbacher. "Predicting students’ success based on forum activities in MOOCs." Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), 2015 IEEE 8th International Conference on. Vol. 2. IEEE, 2015.
[6] Ashenafi, Michael Mogessie, Giuseppe Riccardi, and Marco Ronchetti. "Predicting students’ final exam scores from their course activities." Frontiers in Education Conference (FIE), 2015. 32614 2015. IEEE. IEEE, 2015.
[7] Wolff, Annika, et al. "Predicting student performance from combined data sources." Educational Data Mining. Springer International Publishing, 2014. 175-202.
[8] DeBoer, Jennifer, and Lori Breslow. "Tracking progress: predictors of students’ weekly achievement during a circuits and electronics MOOC." Proceedings of the first ACM conference on Learning@ scale conference. ACM, 2014.
[9] Ramesh, Arti, et al. "Uncovering hidden engagement patterns for predicting learner performance in MOOCs." Proceedings of the first ACM conference on Learning@ scale conference. ACM, 2014.
[10] Feng, Yunping, et al. "The Impact of Students And TAs’ Participation on Students’ Academic Performance in MOOC." Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining 2015. ACM, 2015.
[11] Hughes, Glyn, and Chelsea Dobbins. "The utilization of data analysis techniques in predicting student performance in massive open online courses (MOOCs)." Research and Practice in Technology Enhanced Learning 10.1 (2015).
[12] Ye, Cheng, and Gautam Biswas. "Early Prediction of Student Dropout and Performance in MOOCs using Higher Granularity Temporal Information." test 1.3 (2014): 169-172.
[13] Xu, Bin, and Dan Yang. "Motivation classification and grade prediction for MOOCs learners." Computational intelligence and neuroscience 2016 (2016): 4.
[14] Coffrin, Carleton, et al. "Visualizing patterns of student engagement and performance in MOOCs." Proceedings of the fourth international conference on learning analytics and knowledge. ACM, 2014.
[15] MÅCynarska, Ewa, Derek Greene, and PÃ ˛adraig Cunningham. "Indi- ´ cators of Good Student Performance in Moodle Activity Data." arXiv preprint arXiv:1601.02975 (2016).

Navigating Gestational Diabetic Mellitus:Challenges And Management
T. Sujatha
Pages: 114-120 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Abstract
One common pregnancy problem is gestational diabetes mellitus (GDM), which affects 16% of pregnant women worldwide. However, as early and accurate GDM prediction may lower the disease's risk, it is preferred. Creating a smart healthcare monitoring model to analyze data, forecast disease, and identify fetal monitoring is the main goal of this endeavor. Hence, this work presents an IoT based GDM prediction using multi-modality data. In first step, the ultrasound images are enhanced by Contrast Adaptive Limited Histogram (CLAHE). Once the enhancement is done, next step is feature extraction. The features are extracted using pre-trained Inception-V3 model based on CNN. The GDM data obtained from the Kaggle repository is also gathered and pre-processed. The dataset is balanced, standardised, and outliers are removed during the pre-processing stage. Adaptive Golden Eagle Optimisation (AGEO) is used to choose the critical features required for the GDM prediction.
Keywords : Gestational Diabetes Mellitus, Convolutional Neural Network, Deep Learning

References 
[1] Brand, J.S., West, J., Tuffnell, D., Bird, P.K., Wright, J., Tilling, K. and Lawlor, D.A., 2018. Gestational diabetes and ultrasound-assessed fetal growth in South Asian and White European women: findings from a prospective pregnancy cohort. BMC medicine, 16(1), pp.1-13.
[2] Scifres, C.M., Feghali, M., Dumont, T., Althouse, A.D., Speer, P., Caritis, S.N. and Catov, J.M., 2015. Large-for-gestational-age ultrasound diagnosis and risk for cesarean delivery in women with gestational diabetes mellitus. Obstetrics & Gynecology, 126(5), pp.978-986.
[3] Popova, P.V., Klyushina, A.A., Vasilyeva, L.B., Tkachuk, A.S., Vasukova, E.A., Anopova, A.D., Pustozerov, E.A., Gorelova, I.V., Kravchuk, E.N., Li, O. and Pervunina, T.M., 2021. Association of common genetic risk variants with gestational diabetes mellitus and their role in GDM prediction. Frontiers in endocrinology, 12, p.628582.
[4] Artzi, N.S., Shilo, S., Hadar, E., Rossman, H., Barbash-Hazan, S., Ben-Haroush, A., Balicer, R.D., Feldman, B., Wiznitzer, A. and Segal, E., 2020. Prediction of gestational diabetes based on nationwide electronic health records. Nature medicine, 26(1), pp.71-76.
[5] Zhang, X., Zhao, X., Huo, L., Yuan, N., Sun, J., Du, J., Nan, M. and Ji, L., 2020. Risk prediction model of gestational diabetes mellitus based on nomogram in a Chinese population cohort study. Scientific Reports, 10(1), pp.1-7.
[6] Amirian, A., Mahani, M.B. and Abdi, F., 2020. Role of interleukin-6 (IL-6) in predicting gestational diabetes mellitus. Obstetrics & Gynecology Science, 63(4), pp.407-416.
[7] El-Rashidy, N., ElSayed, N.E., El-Ghamry, A. and Talaat, F.M., 2022. Utilizing fog computing and explainable deep learning techniques for gestational diabetes prediction. Neural Computing and Applications, pp.1-20.
[8] Naseem, A., Habib, R., Naz, T., Atif, M., Arif, M. and Allaoua Chelloug, S., 2022. Novel Internet of Things Based Approach Towards Diabetes Prediction Using Deep Learning Models. Frontiers in Public Health, p.2848.
[9] Liu, Y., Wang, Y., Zhang, Y. and Cheng, R., 2021. Detection of Gestational Diabetes Mellitus and Influence on Perinatal Outcomes from B-Mode Ultrasound Images Using Deep Neural Network. Scientific Programming, 2021, pp.1-8. 

 [10] Davidson, S.J., Susan, J., Britten, F.L., Wolski, P., Sekar, R. and Callaway, L.K., 2021. Fetal ultrasound scans to guide management of gestational diabetes: Improved neonatal outcomes in routine clinical practice. Diabetes Research and Clinical Practice, 173, p.108696.

Block Chain Techonology
Sathyajothi R
Pages: 121-128 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Introduction 
Blockchain can be defined as a digital record of all the transactions that take place in a computer network. The name comes from two words, one is block which means that there are blocks of individual data which has all the information about the transaction and the second is a chain which means that all this data is arranged in a single list which is here referred as a chain. Block chain is a system of storing and transferring information in a distributed and decentralized way. You can think of it as a ledger or a record book that is shared and updated by many participants, instead of being controlled by a single authority. Each entry or transaction in the ledger is verified and encrypted by a network of computers, called nodes, using cryptography. These transactions are grouped into blocks, and each block is linked to the previous one by a unique code, called a hash. This creates a chain of blocks, or a block chain, that is secure, transparent, and immutable One of the major tasks performed by Blockchain is to store the transaction data of Cryptocurrencies. The network required for Blockchain is a peer-to-peer network in which every user of the network has the information of all the transactions made in that specific network. Transactions are made using Cryptocurrency wallets. All the transactions that are made are encrypted. Some of the common examples of Cryptocurrency are Bitcoin (BTC), Litecoin (LTC), Ethereum (ETH), Ripple (XRP) etc.

Conclusion
As fundamental technologies with transformative potentials, Blockchain and Cryptocurrencies have found a wide spectrum of application scenarios in various types of industries, ranging from the underlying techniques of data storage, encryption, and verification, to the middle level of finance and asset management, and to a variety of high-level business models. The property of its security, privacy, traceability, inherent data provenance and time-stamping has seen its adoption beyond its initial application areas. Its decentralized application across the already established global Internet is also very appealing in terms of ensuring data redundancy and hence survivability. Thus the invention of the Blockchain and Cryptocurrencies can be seen to be a vital and much needed additional component of the Internet that was lacking in security and trust before. Blockchain and Cryptocurrencies technology still has not reached its maturity with a prediction of five years as novel applications continue to be implemented globally.

IOT Based Air Pollution Monitoring System
J.Johnsirani
Pages: 129-133 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Abstract
The air pollution level is fastly increased due to factors like industries, urbanization, increasing population, vehicle use which can affect human health. IOT Based Air Pollution Monitoring System is used to monitor Air Quality over a web server using the Internet. It will activate an alarm when the air quality goes out of range a certain level, means when there is acceptable amount of harmful gases present in the air like NH3, benzene, smoke, CO2, alcohol, and NOx. It will display the air quality in PPM on the LCD and on the webpage so we can easily monitor air pollution.
The device uses MQ135 and MQ6 sensors to detect the most harmful gases and can measure their amount accurately.

References 
[1] https://securedstatic.greenpeace.org/india/Global/india/Airpoclypse--Not-just-Delhi--Air-in-mostIndian-cities-hazardous--Greenpeace-report.pdf.
[2] content/uploads/2008/04/5v-regulator-using7805.JPG
[3] https://store.arduino.cc/arduino-uno-rev3
[4] https://www.arduino.cc/
[5] https://www.aliexpress.com/item/1PCS-LOTSolution-PH-valuo-Temperature-detector-sensormodule-for-arduino-Freeshipping/32620995019.html?spm=2114.4001

Data Driven Approach for Eye Disease Classification with Machine Learning
G.Rajkumar
Pages: 134-152 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Abstract
Medical health systems have been concentrating on artificial intelligence techniques for speedy diagnosis. However, the recording of health data in a standard form still requires attention so that machine learning can be more accurate and reliable by considering multiple parameters. The aim of this study is to develop a general framework for recording diagnostic data in an international standard format to facilitate auto-prediction of eye diseases. Efforts were made to ensure error-free data entry by developing a user-friendly interface. Furthermore, different machine learning algorithms were used to analyze patient data based on multiple parameters, including age, illness history, and clinical observations. This data was formatted according to structured hierarchies designed by medical experts, whereas diagnosis was made as per the ICD-10 coding developed by the American Academy of Ophthalmology. Furthermore, the system is designed to evolve through self-learning by adding new classifications for both diagnosis and symptoms. The classification results from tree-based methods demonstrated that the proposed framework performs satisfactorily given a sufficient amount of data. The random forest and decision tree algorithms predicted more accurately as compared to neural networks and the naïve Bayes algorithm owing to structured data arrangement.

Deep learning
Kapilkrishnan A B
Pages: 153-161 | First Published: 05 Mar 2024
Full text | Abstract | PDF | References | Request permissions

Abstract
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech recognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the back propagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shown light on sequential data such as text and speech.

Reference 
1. Krizhevsky, A., Sutskever, I. & Hinton, G. ImageNet classification with deep convolutional neural networks. In Proc. Advances in Neural Information Processing Systems 25 1090–1098 (2012)
2. Farabet, C., Couprie, C., Najman, L. & LeCun, Y. Learning hierarchical features for scene labeling. IEEE Trans. Pattern Anal. Mach. Intell. 35, 1915–1929 (2013)
3. Tompson, J., Jain, A., LeCun, Y. & Bregler, C. Joint training of a convolutional network and a graphical model for human pose estimation. In Proc. Advances in Neural Information Processing Systems 27 1799–1807 (2014)
4. Szegedy, C. et al. Going deeper with convolutions. Preprint at http://arxiv.org/ abs/1409.4842 (2014).
5. Mikolov, T., Deoras, A., Povey, D., Burget, L. & Cernocky, J. Strategies for training large scale neural network language models. In Proc. Automatic Speech Recognition and Understanding 196–201 (2011)
6. Hinton, G. et al. Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine 29, 82–97 (2012)
7. Sainath, T., Mohamed, A.-R., Kingsbury, B. & Ramabhadran, B. Deep convolutional neural networks for LVCSR. In Proc. Acoustics, Speech and Signal Processing 8614–8618 (2013)
8. Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E. & Svetnik, V. Deep neural nets as a method for quantitative structure-activity relationships. J. Chem. Inf. Model. 55, 263–274 (2015)
9. Helmstaedter, M. et al. Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174 (2013)