Volume-11 , Special Issue-01 , Nov - 2023

In this study we proposed a new weighted aggregation operator for ranking the picture fuzzy numbers (PFNs) which is based on neutral membership value of PFN. As the picture fuzzy set (PFS) is an extension version of intuitionistic fuzzy set theory with introducing the neutral membership value during data analysis. The neutral membership value in PFS reflecting the ambiguous nature of the subject to judgment. The ambiguity is depending on the neutral membership value of PFN. The proposed weighted aggregation operator manages the ambiguity according to neutral membership value. Then, the aggregation operator applies in a multi attribute decision making method where attribute value of the alternative are picture fuzzy numbers. In the decision making process, the weight of attributes are calculated according to neutral values and aggregate the multiple attributes into a single PFN. Then estimate the individual score value of the alternatives. Lastly, ranking the alternative according to score value. Finally, a practical example for students’ performance in the multiple paper examination is highlighted for verifying the developed approach and demonstrates its practicality and effectiveness.

Aggregation operators, Decision-making, Picture fuzzy set, Weighted Aggregation operators.

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Amalendu Si, Surajit Dan, Sujit Das, "Multi-Attribute Decision Making Approach Based on Neutral Membership Degree of Picture Fuzzy Set," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.89-94, 2023.

Stress is our body`s emotional and physical reaction to a circumstance or event that we perceive as risky or difficult. Different levels of stress are experienced by people on a regular basis, which causes serious problems. Three main types of stress are acute stress, episodic stress, and chronic stress. A new or challenging situation generates acute stress in your body. It`s the emotion people experience as a deadline approaches or when they just miss getting hit by a car. We might even come across it as a result of a fun pastime. Like an exciting ride on a roller coaster or a stunning feat of personal achievement. Acute stress is a category for short-term stress. After a little period of time, the body and emotions usually return to normal. Then comes Continual acute pressures are referred to as episodic acute stresses. The cause of this can be persistently tight job deadlines. It might also be due to the frequent high-stress situations that some professions face. Long-lasting demands are ultimately what lead to chronic stress. One example is living in a high-crime area or constantly fighting with your significant other. These health issues have all been related to the onset of stress, dilated pupils, increased heart rate, anxiety, mood swings, lack of sleep, weight gain, insomnia, panic attacks, persistent headaches, and other variables. So, the stress management system will offer a technique of management from which we can identify the level of stress or kinds of stress and offer solutions for how we can lower the tension and release people from this type of stress.

stress, disease, man, woman, acute, chronic etc.

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Hrithick Paul, Nirbhay Mishra, Sayani Ghatak, Dhararmpal Singh, Debmitra Ghosh, Sahil Das, P. Singha, "Impact of Stress on Humans and Its Detection Using Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.95-100, 2023.

CCTV image classification using machine learning algorithm is a novel way of using machine learning for security. This paper investigates how classification can be done using statistical indicators of image pixels without employing extensive image processing techniques. It further performs comparative study of performances of some commonly used classification algorithms in classifying images. The highest performance was by the K-Nearest Neighbor classifier with accuracy, precision, and recall scores of 95\%, 90\%, and 100\% respectively.

CCTV image classification, Crime Scene Identification, CCTV Footage Analysis, Machine Learning, CCTV Security, Classification Algorithm.

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Saumitra Biswas, Sanchayan Bhaumik, Tanay Bag, "Crime Scene classification of Digital Images Sourced from CCTV Footage," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.101-106, 2023.

Telemedicine and its base, Telecare Medical Information System (TMIS), are becoming popular trend in the post COVID days. Faster Internet services and wireless technologies essentially provide faster data transfer facilities in Internet of Things (IoT) environment. To provide remote healthcare services, wearable and household medical devices are connected to form IoT networks. Thus, private data are exposed over the Internet and privacy maintenance is an issue. Elliptic Curve Cryptographic (ECC), a public key system, may be used since it can handle lightweight keys for small devices. One way hash-functions are very useful to protect any data but are irreversible. Hashing is useful to implement for data integrity. Our proposed system will use public key cryptosystem where keys are generated in ECC over a cyclic field. Beside this, One Time Password (OTP) will be used to provide another layer of user verification that will be transmitted in the GSM like telephone network. We will expect to provide security at different levels for all kind of users in a TMIS environment.

Internet of Things (IoT), Elliptic Curve Cryptography (ECC), One Time Password (OTP), Hashing, Encryption and Decryption.

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Asit Kumar Nayek, Radha Krishna Jana, Arpan Adhikary, "An improved algorithm with Multifactor Authentication Scheme for Telecare Medical Information System (TMIS)," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.107-113, 2023.

This article shows that predictive analytics using big data analytics has become a powerful tool for disease prediction and prevention in healthcare. This article provides an overview of the application of predictive analytics using big data analytics in healthcare. Machine learning models that use a wide variety of data, including medical data, genetic data, lifestyle data, and the environment, are used to identify and generate accurate predictions. Benefits of predictive testing in healthcare include early disease detection, personalised medicine, and lifestyle changes. Supports interventions that improve clinical outcomes. The allocation of resources and planning have also been simplified, and better treatment and prevention measures have been used. As a result, issues such as privacy concerns, data quality, and ethical considerations must be addressed. Predictive analytics from big data analytics has the potential to transform healthcare and improve patient care and public health outcomes.

Predictive analytics, big data analytics, Disease prediction, Disease prevention, Healthcare, Machine learning models, Medical data, Genetic data etc.

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P. Chatterjee, A. Barman, D. Dey, A. Dutta, R.K. Jana, D. Singh, S. Dutta, N. Mishra, "Unlocking the Future: Leveraging Big Data Analytics for Predictive Healthcare Insights," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.114-119, 2023.

Support Vector Machines (SVMs) are used in large-scale linear and non-linear non-probabilistic binary or multi-class classification. Classification using SVM techniques gives better accuracy than other machine learning classification methods. Various Support Vector Classification (SVC) algorithms are available in the literature, and many researchers are facing the problem of choosing the best methods for real-world applications. This paper integrates LibSVM and LibLINEAR tools with the Weka tool. The Radial Basis Function (RBF), Polynomial, Sigmoid and Linear kernel-based C-SVC and nu-SVC models, as well as predictive linear SVM models, are applied to six UCI machine learning datasets. The presentations of various SVC methods are empirically matched using Classification Accuracy (CA), Root Mean Square Error (RMSE), and Area Under Curve (AUC) metrics. The proposed method for this article is RBF kernel and linear kernel in C-SVC and nu-SVC models. The performance of the proposed models is trained and tested with UCI machine learning datasets for non-linear and linear classification. The results are compared with state-of-the-art SVC models. RBF kernel in C-SVC and nu-SVC models has achieved an accuracy of 97.3% and 98%, respectively, for non-linear classification on the Iris dataset. The linear kernel in C-SVC and nu-SVC models has achieved 96.6% and 98% accuracy for linear classification on the Iris dataset. L2-Regularized L2-Hinge Loss dual and primal SVC model has a classification accuracy of 96% for large-scale linear classification on the Iris dataset. Therefore, some conclusions based on overall performances on six datasets are as follows. (i) RBF kernel-based C-SVC model performs better than other non-linear SVC methods. (ii) Linear kernel-based C-SVC and nu-SVC methods perform better in the case of linear classification. (iii) In large-scale linear classification, L1-Regularized L2-Loss SVC, Multi-class SVC by Crammer Singer and L2-Regularized L2-Loss SVC methods perform better than other linear SVC methods. (iv) Most of these methods give good results in the case of datasets having all the most numeric attributes or dimensions and a large number of instances or vectors.

Support Vector Machines, Kernel Functions, Regularized Losses, Classification

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[39]. Datta, R. P., and Sanjib Saha. "Applying rule-based classification techniques to medical databases: an empirical study." International Journal of Business Intelligence and Systems Engineering 1.1 (2016): 32-48.
[40]. Blake, C. and Merz, C. J. "UCI repository of machine learning datasets." University of California, Irvine, Dept. of Information and Computer Sciences.(http://www.cs.waikato.ac.nz/~ml/weka/)
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[44]. Saha, Sanjib. "Non-rigid Registration of De-noised Ultrasound Breast Tumors in Image Guided Breast-Conserving Surgery." Intelligent Systems and Human Machine Collaboration. Springer, Singapore, 2023. 191-206.
[45]. Saha, Sanjib, et al. "ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images." Biomedical Signal Processing and Control 85 (2023): 104974.
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Sanjib Saha, "An Empirical Comparison of Linear and Non-linear Classification Using Support Vector Machines," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.120-126, 2023.

This summary presents the application of an integrated simulation platform for the analysis of poor health in practical nursing care. The platform uses advanced techniques to model and analyse the complex health behaviours of nursing home residents. The platform leverages implicit health analysis to capture hidden patterns and subtle changes in people`s health to better understand their needs. Through simulation-based evaluation, various aspects of the strategy can be analysed, including employee engagement, community health, and the need for assistance. The platform provides the framework for improving employee decisions by identifying the best strategies to meet the diverse and changing needs of nursing home residents. Using this new approach, nursing homes can improve care, reduce labour costs and ultimately improve residents` overall quality of life.

Skilled Nurse, Registred Nurse,NB,Simulator

[1] Xuxue Sun , Nan Kong , Nazmus Sakib , Chao Meng , Kathryn Hyer , Hongdao Meng , Chris Masterson , Mingyang Li, “ A Latent Survival Analysis Enabled Simulation Platform For Nursing Home Staffing Strategy Evaluation”.
[2] Ridong Wanga,, Karmel S. Shehadehb,, Xiaolei Xiea,, Lefei Lia, “Integrated optimization approaches for nursing home staffing strategy evaluation”,https://arxiv.org/pdf/2203.14430.pdf.
[3] U.S. Centers for Medicare and Medicaid Services, 2014. Nursing Home Penalty Data 2014. Baltimore, MD: CMS; 2014.https://data.medicare.gov/Nursing-Home-Compare/Penalty-Counts/t8q7-k6ku
[4] C. Harrington, H. Carrillo and R. Garfield, 2015. “Nursing facilities, staffing, residents and facility deficiencies, 2009 Through 2014”. Menlo Park: The Henry J. Kaiser Family Foundation.
[5]Xuxue Sun , Nan Kong , Youbing Zhao , Nazmus Sakib , Chao Meng , Hongdao Meng , Kathryn Hyer , Ying Li f, Chris Masterson , Mingyang Li, “A latent survival analysis integrated simulation platform for nursing home staffing strategy evaluation”,Computers & Industrial Engineering, Volume 177, March 2023, 109074.
[6] Janiszewski Goodin, H., 2003. “The nursing shortage in the United States of America: an integrative review of the literature”. Journal of advanced nursing, 43(4), pp.335-343.
[7] R Tamara Konetzka, S Clark Stearns and J Park, 2008. “The staffing-outcomes relationship in nursing homes”. Health services research, 43(3), pp.1025-1042.
[8] K. Spilsbury, C. Hewitt, L. Stirk and C. Bowman, 2011. “The relationship between nurse staffing and quality of care in nursing homes: a systematic review”. International journal of nursing studies, 48(6), pp.732-750.
[9] C. Mueller, G. Arling, R. Kane, J. Bershadsky,D., Holland, & A.Joy (2006). Nursing home staffing standards: Their relationship to nurse staffing levels. The Gerontologist, 46(1), 74-80.
[10] J.R. Bowblis, 2011. Staffing ratios and quality: An analysis of minimum direct care staffing requirements for nursing homes. Health services research, 46(5), pp.1495-1516.
[11] X. Zhang, S. Barnes, B. Golden, M. Myers and P. Smith, 2019. Lognormal-based mixture models for robust fitting of hospital length of stay distributions. Operations Research for Health Care, 22, p.100184.

Abhisan Paul, Diganta Biswas, Radha Krishna Jana, "Utilizing an Implicit Health Analysis Integrated Simulation for Hospital-Nurse Staffing Strategy," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.127-133, 2023.

Ensuring a sustainable livelihood to the larger counts of the population is the most challenging agenda of any developing country. So this work wants to quantify the level of under achievements of different economies in the field of sustainable livelihood. The contribution of influencing variables of under-achievement and contributing variables to achievement in the construction of under achievement livelihood space is also determined under this work. The interaction between these two types of variables and their juxtaposed effects are evaluated through the use of artificial neural network. Finally this method of artificial intelligence is used to achieve a self-sustained monotonic high rate of development. The whole work is presented through a set theoretic approach which is followed by the testing of the same. It is expected that the application of neural network in the process of self-sustained growth of sustainable livelihood is unique in academic discourses.

Artificial Intelligence, Sustainable livelihood, Graph theory, Artificial Neural Network, Sustainable Livelihood, Under achievement.

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Soumya Sengupta, Dharmpal Singh, "Measure Underachievement’s in the Space of Livelihoods through Artificial Intelligence," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.134-140, 2023.

This paper explores the different techniques of association rule mining (ARM) and clustering in unsupervised learning and data mining. As many works have already been done on the Apriori algorithm of ARM, but there was very limited work on the other algorithms such as Predictive Apriori, Tertius and Filtered Associator. The main problem of ARM is handling a large dataset and then scanning it repeatedly. A pre-clustering effort would reduce the dataset size for each such scan for each such cluster and thus would offer overall less time requirement. The different algorithms of ARM are executed on two different datasets such as Breast Cancer and Zoo. There is a scope for improvement in performance by applying filters and clustering techniques on ARM. The best model has been proposed as follows: (i) Use data source; (ii) Apply filters (numeric to nominal and replace missing value); (iii) Apply additional filters (attribute selection or merge two values or remove folds) or evaluation method (training set maker); (iv) Apply clustering methods (K-Means, Farthest Fast, Expectation Maximization, Hierarchical and Make Density Based); (v) Apply ARM methods (Apriori, Predictive Apriori, Tertius and Filtered Associator); (vi) View result. The different ARM algorithms are evaluated with certain metrics and compared against each other based on accuracy, lift value and execution time. However, the best rules found from each ARM algorithm are different. The paper discusses the effect of clustering on ARM and claims that clustering the data before applying ARM is better.

Unsupervised Learning, Data Mining, Association Rule Mining, Apriori, Clustering, K-Means

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[23] WEKA3 tool for machine learning and knowledge analysis. Online available at http://www.cs.waikato.ac.nz/~ml/weka/
[24] Blake, C. and Merz, C. J. "UCI repository of machine learning datasets." University of California, Irvine, Dept. of Information and Computer Sciences.(http://www.cs.waikato.ac.nz/~ml/weka/)
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[26] Datta, R. P., and Sanjib Saha. "Applying rule-based classification techniques to medical databases: an empirical study." International Journal of Business Intelligence and Systems Engineering 1.1: pp.32-48, 2016.
[27] Saha, Sanjib, and Debashis Nandi. "Data Classification based on Decision Tree, Rule Generation, Bayes and Statistical Methods: An Empirical Comparison." Int. J. Comput. Appl 129.7: pp.36-41, 2015.
[28] Das, Subhankar, and Sanjib Saha. "Data mining and soft computing using support vector machine: A survey." International Journal of Computer Applications 77.14, 2013.
[29] Saha, Sanjib. "Non-rigid Registration of De-noised Ultrasound Breast Tumors in Image Guided Breast-Conserving Surgery." Intelligent Systems and Human Machine Collaboration. Springer, Singapore, pp.191-206, 2023.
[30] Saha, Sanjib, et al. "ADU-Net: An Attention Dense U-Net based deep supervised DNN for automated lesion segmentation of COVID-19 from chest CT images." Biomedical Signal Processing and Control 85: 104974, 2023.

Sanjib Saha, "An Empirical Comparison and Effect of Clustering Massive Data on Association Rule Mining," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.141-148, 2023.

The research paper outlines the creation of vehicle accident detection and notification system that is based on an Android application. When a vehicle collision occurs, the system uses sensors and mobile technologies to notify the necessary parties, including emergency services, insurance providers, and worried family members. A predestined contact list will get an emergency alert message from the system when it has detected and appraise the relentlessness of a vehicle accident. The technology is proposed to be extremely accurate and effectual at detecting and responding to auto accidents. When a vehicle`s velocity suddenly changes, the system employs a mix of accelerometer, gyroscope, and Global Positioning System (GPS) sensors to look for impending accident signs. After determining the accident`s relentlessness using an algorithm, the system sends an emergency alert message to a predetermined contact list. The system has undergone testing and been shown to be reliable and successful in identifying vehicle collisions and transmitting emergency signals. The design and development of the system, its possible applications, and the benefits and difficulties of its adoption are all covered in the research paper. The paper also explores the possibility for more study to enhance the system.

Android Application, Global Positioning System (GPS), Accident Detection System.

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[5]. H. M. Sherif, M. A. Shedid and S. A. Senbel, "Real time traffic accident detection system using wireless sensor network," 6th International Conference of Soft Computing and Pattern Recognition (SoCPaR), Tunis, Tunisia, pp.59-64, 2014. doi: 10.1109/SOCPAR.2014.7007982.
[6]. C. Devi and S. Gowri, "An automatic Smart Phone with IoT based Accident detection and alerting System," 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA), Coimbatore, India, pp.426-432, 2021. doi: 10.1109/ICECA52323.2021.9676093.
[7]. R. Rishi, S. Yede, K. Kunal and N. V. Bansode, "Automatic Messaging System for Vehicle Tracking and Accident Detection," International Conference on Electronics and Sustainable Communication Systems (ICESC), Coimbatore, India, pp.831-834, 2020. doi: 10.1109/ICESC48915.2020.9155836.
[8]. G. Rajesh, A. R. Benny, A. Harikrishnan, J. Jacob Abraham and N. P. John, "A Deep Learning based Accident Detection System," 2020 International Conference on Communication and Signal Processing (ICCSP), Chennai, India, pp.1322-1325, 2020. doi: 10.1109/ICCSP48568.2020.9182224.
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[10]. Baballe M.A., Yau A, Farouk S., Ibrahim, Muhammad A., Mustapha N. K. Accident Detection System with GPS, GSM, and Buzzer, Journal of Mathematical Techniques and Computational Mathematics, Vol.2, Issue.3, 2023.
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Shadma Bakhtawar, Shahid Imam, Shadab Ali, Sunil, "SafeWay: An Android Application based Automatic Vehicle Accident Detection and Messaging System," International Journal of Computer Sciences and Engineering, Vol.11, Issue.01, pp.149-154, 2023.