Template-Type: ReDIF-Article 1.0
Author-Name: Shobhit Srivastava
Author-X-Name-First: Shobhit
Author-X-Name-Last: Srivastava
Author-Name: Mrinal Kanti Sarkar
Author-X-Name-First: Mrinal Kanti
Author-X-Name-Last: Sarkar
Author-Name: Chinmay Chakraborty
Author-X-Name-First: Chinmay
Author-X-Name-Last: Chakraborty
Title: Sentiment analysis of Twitter data using machine learning: COVID-19 perspective
Abstract:
The 2019 COVID-19 pandemic has affected people worldwide. Social media has become a global platform for individuals to voice their diverse perspectives on the pandemic, which has significantly altered their lives during and beyond lockdown periods. Twitter, a leading social media platform, experienced a surge in coronavirus-related tweets encompassing a spectrum of positive, negative and neutral opinions. Coronavirus transmits between humans in numerous ways. It irritates the lungs. This makes Twitter a perfect platform for expressing opinions. Twitter data from across the world was collected and analysed for sentiment in order to better understand public opinion and prepare for COVID-19 (Tusar et al., 2022). In this article, our aim is to compare the neural network techniques and indicate the share of their performance measures. We use kNN and neural network algorithms for these and use the MSE factor as a key of comparison. However, we use other performance measures too for better analysis of the result. Our main focus in this study is to analyse the performance partition of the kNN algorithms, including the performance portion of the each algorithm.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 1-16
Issue: 1
Volume: 16
Year: 2024
Keywords: COVID-19; social media; sentiment analysis; Twitter; machine learning; neural networks; KNN; neural network.
File-URL: http://www.inderscience.com/link.php?id=137479
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Handle: RePEc:ids:injdan:v:16:y:2024:i:1:p:1-16

Template-Type: ReDIF-Article 1.0
Author-Name: Sarah Bukhari
Author-X-Name-First: Sarah
Author-X-Name-Last: Bukhari
Author-Name: Muhammad Ramzan
Author-X-Name-First: Muhammad
Author-X-Name-Last: Ramzan
Title: Text mining on social media data: a systematic literature review
Abstract:
Text mining is the process of getting meaningful information from unstructured data. In this paper, a precise writing overview was directed to research text mining via online media information. Thus, a comprehensive deliberate writing audit (SLR) was completed to explore online media as a hotspot for the perception of text mining. For this reason, 40 articles were chosen from different notable sources after a concentrated SLR cycle of looking, sifting, and implementing the incorporation and avoidance models. As a result, the text mining strategies via web-based media information were featured regarding online media as a wellspring of data. A detail SLR which features the need of message mining methods on most recent online media information, cover more kinds of web-based media which were not shrouded in past work and furthermore present qualities and shortcomings of text mining strategies utilised in web-based media.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 82-104
Issue: 1
Volume: 16
Year: 2024
Keywords: social media; text mining; text mining techniques; role of social media; social media types.
File-URL: http://www.inderscience.com/link.php?id=137480
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Handle: RePEc:ids:injdan:v:16:y:2024:i:1:p:82-104

Template-Type: ReDIF-Article 1.0
Author-Name: Fateh Bougamouza
Author-X-Name-First: Fateh
Author-X-Name-Last: Bougamouza
Author-Name: Samira Hazmoune
Author-X-Name-First: Samira
Author-X-Name-Last: Hazmoune
Title: Sentiment analysis of customer reviews for Algerian dialect using the DziriBERT model
Abstract:
The increasing volume of daily comments and tweets presents a valuable resource for improving various processes, from business strategies to service management. However, the Algerian Dialect (AlgD), despite its growing presence on social media, has been overlooked in sentiment analysis. This study addresses this gap by proposing an approach for sentiment analysis of Algerian Dialect feedback, specifically from customers of Algerian telephone operators (Djezzy, Mobilis, and Ooredoo). Leveraging transfer learning, the pre-trained DziriBERT model was fine-tuned, with experiments refining data preprocessing techniques and hyperparameters. The outcome is an impressive 82.01% accuracy rate, offering promising insights into sentiment analysis in the Algerian Dialect and highlighting its potential significance for companies and researchers in the field.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 341-362
Issue: 3
Volume: 16
Year: 2024
Keywords: sentiment analysis; Algerian Arabic dialect; DziriBERT; transfer learning; Algerian telephone operators; emoji categorisation.
File-URL: http://www.inderscience.com/link.php?id=140647
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Handle: RePEc:ids:injdan:v:16:y:2024:i:3:p:341-362

Template-Type: ReDIF-Article 1.0
Author-Name: Hamza Benyezza
Author-X-Name-First: Hamza
Author-X-Name-Last: Benyezza
Author-Name: Reda Kara
Author-X-Name-First: Reda
Author-X-Name-Last: Kara
Author-Name: Mounir Bouhedda
Author-X-Name-First: Mounir
Author-X-Name-Last: Bouhedda
Author-Name: Zine Eddine Safar Zitoun
Author-X-Name-First: Zine Eddine Safar
Author-X-Name-Last: Zitoun
Author-Name: Samia Rebouh
Author-X-Name-First: Samia
Author-X-Name-Last: Rebouh
Title: Boosting CNN network performance for face recognition in an authentication system
Abstract:
Face recognition technology has made significant advancements through the utilisation of convolutional neural networks (CNN) in various applications. However, accurately identifying individuals from similar backgrounds remains a notable challenge due to inherent similarities in facial features among individuals with shared genetic ancestry or cultural heritage. This paper addresses the limitations of traditional CNN in accurately identifying individuals from the same origins and presents an approach to enhance the performance of CNN networks and improve the reliability of face recognition in authentication systems. The proposed approach incorporates an advanced face detection and identification algorithm based on the visual geometry group face (VGG-Face) CNN descriptor model, along with the cosine distance algorithm. Promising results were obtained through a prototype implementation on a Raspberry Pi 4. Comparative evaluations against alternative face recognition strategies showcased exceptional performance, achieving an accuracy rate of 96.33% for positive pairs and 95.38% for negative pairs at an optimal threshold of 20.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 282-310
Issue: 3
Volume: 16
Year: 2024
Keywords: smart authentication system; face detection and identification; VGG-face CNN descriptor; Internet of Things; cosine distance algorithm.
File-URL: http://www.inderscience.com/link.php?id=140648
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Handle: RePEc:ids:injdan:v:16:y:2024:i:3:p:282-310

Template-Type: ReDIF-Article 1.0
Author-Name: Pabitra Kumar Dey
Author-X-Name-First: Pabitra Kumar
Author-X-Name-Last: Dey
Author-Name: Abhijit Banerjee
Author-X-Name-First: Abhijit
Author-X-Name-Last: Banerjee
Author-Name: Dipendra Nath Ghosh
Author-X-Name-First: Dipendra Nath
Author-X-Name-Last: Ghosh
Title: Optimising IPL squad composition: a mathematical framework for efficient team selection on a limited budget in a multi-criteria, multi-objective environment
Abstract:
Selection of the finest cricket squads for Twenty-20 cricket while considering multiple criteria and a limited budget is indeed a challenging problem for team management. For the formation of the best team squads, the objectives could include maximising batting and bowling strength, considering player performances, experiences, age, and captaincy capabilities while spending the minimum amount. To tackle this problem, a multi-objective optimisation approach can be valuable to find the best possible team composition. A comprehensive approach for the selectors was proposed by combining the multi-objective genetic algorithm in a multi-criteria environment. Overall, the aims of this research work are to provide selectors with a mathematical framework that can assist them in choosing the best cricket squad with a lower budget. This approach can help automate the process of selecting teams in a multi-criteria environment, such as player auctions, and provide selectors with a range of optimal options to consider.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 311-340
Issue: 3
Volume: 16
Year: 2024
Keywords: optimum team selection; MGDA; modified group decision algorithm; MMOGA; modified multi-objective genetic algorithm; NSGA-II; Non-Dominated Sorting Genetic Algorithm-II; IPL T-20 cricket; strategy planning.
File-URL: http://www.inderscience.com/link.php?id=140649
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Handle: RePEc:ids:injdan:v:16:y:2024:i:3:p:311-340

Template-Type: ReDIF-Article 1.0
Author-Name: Patil Jayashree Madhukar
Author-X-Name-First: Patil Jayashree
Author-X-Name-Last: Madhukar
Author-Name: P.M. Ashok Kumar
Author-X-Name-First: P.M. Ashok
Author-X-Name-Last: Kumar
Author-Name: R. Anitha
Author-X-Name-First: R.
Author-X-Name-Last: Anitha
Title: CEVAB: NIR-VIS face recognition using convolutional encoder-based visual attention block
Abstract:
Recent research in night vision face recognition has spiked due to the rise of night-time surveillance in public areas, where cameras often use near infrared (NIR) images. This paper presents a new face recognition method, the convolutional encoder-based visual attention block (CEVAB), optimised for NIR and visible spectrum (VIS) images. CEVAB combines a convolutional encoder with an attention-based architecture, focusing on critical facial features to enhance accuracy against watchlists. Tested on the FaceSurv dataset with over 132,000 images, CEVAB outshines traditional methods in VIS, achieving 95.08% Rank 1 accuracy at close distances, and in NIR, with 74.00% Rank 1 accuracy, surpassing competitors like Verilook and ResNet-50. These results prove CEVAB's exceptional adaptability and performance in various imaging conditions, significantly advancing night vision face recognition technology.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 262-281
Issue: 3
Volume: 16
Year: 2024
Keywords: deep learning; face recognition; NIR images; visual attention; convolutional decoder; convolutional encoder; cross-spectral recognition; deep learning; face recognition; feature extraction; night vision; NIR-VIS; surveillance systems; visual attention.
File-URL: http://www.inderscience.com/link.php?id=140650
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Handle: RePEc:ids:injdan:v:16:y:2024:i:3:p:262-281

Template-Type: ReDIF-Article 1.0
Author-Name: Thomas (Yew Sing) Lee
Author-X-Name-First: Thomas (Yew Sing)
Author-X-Name-Last: Lee
Title: Integrated cyber security risk management-insurance and investment cost analysis
Abstract:
An insurer offers cyber insurance coverage to several firms with risk averse decision makers. The cyber insurance premium offered depends on the cyber security implemented at the firm. Each firm faces attacks by multiple types of hackers and decides on the level of investment for cyber security counter measures. We address the software monoculture issue by considering that there is common, popular software used by all firms, and it is a source of correlated risk. Two types of cyber security interdependence breaching processes due to the software monoculture risk were analysed. We derive the probability distribution for the number of breaches and develop the cyber insurance pricing model. We also introduce the concept of cyber security defence level. Furthermore, we proposed to determine the optimal cyber insurance price given a targeted defence level. Finally, we demonstrate the use of our model through several numerical examples.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 223-261
Issue: 3
Volume: 16
Year: 2024
Keywords: cyber insurance; hacker; breaching probability; cyber security; correlated risks; software monoculture risk; defence level; integrated risk management.
File-URL: http://www.inderscience.com/link.php?id=140651
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Handle: RePEc:ids:injdan:v:16:y:2024:i:3:p:223-261

Template-Type: ReDIF-Article 1.0
Author-Name: Renato de Matta
Author-X-Name-First: Renato de
Author-X-Name-Last: Matta
Author-Name: Tan Miller
Author-X-Name-First: Tan
Author-X-Name-Last: Miller
Author-Name: Emmanuel Peters
Author-X-Name-First: Emmanuel
Author-X-Name-Last: Peters
Title: A medical device distribution network design strategic planning process and optimisation model
Abstract:
In this research study, we propose a strategic network design planning approach for the medical device industry that utilises a profit maximising optimisation model, innovative data pre-processing methods, and product pricing sensitivity analyses. This process provides decision support for planning the design of flexible, responsive physical networks, and for improving inventory deployment practices. This strategic planning approach poses a series of distribution strategy scenarios, each evaluated by our mathematical optimisation model. The modeling methodology includes data pre-processing steps that facilitate the incorporation of critical customer service order cycle delivery requirements and transportation mode choice selections, as well as product returns and product refurbishment processes specific to the medical device industry. We also demonstrate how the incorporation of product pricing sensitivity analysis into our planning process facilitates the coordination of distribution network design strategy with sales and marketing strategic pricing decisions. We present a case study with numerical results.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 126-161
Issue: 2
Volume: 16
Year: 2024
Keywords: manufacturing and distribution planning; strategic network planning; medical device network design; medical device industry; optimisation modelling; data pre-processing procedures for optimisation.
File-URL: http://www.inderscience.com/link.php?id=137858
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Handle: RePEc:ids:injdan:v:16:y:2024:i:2:p:126-161

Template-Type: ReDIF-Article 1.0
Author-Name: Md. Zanea Alam
Author-X-Name-First: Md. Zanea
Author-X-Name-Last: Alam
Author-Name: Mahfuzulhoq Chowdhury
Author-X-Name-First: Mahfuzulhoq
Author-X-Name-Last: Chowdhury
Title: An intelligent social protection service beneficiary selection scheme using machine learning and a mobile application for social safety net program
Abstract:
Social safety net (SSN) initiatives serve an important role in assisting disadvantaged communities and resolving socio-economic inequities across the world. However, Appropriate candidate selection for the SSN program is very difficult due to the lack of a proper automated system. Traditional systems frequently rely on manual evaluation, which can result in mistakes, delays, and unworthy recipients. To conquer this, this paper develops a prediction model to choose beneficiaries and determine eligibility by using five machine learning algorithms. The proposed prediction model can categorise individuals as eligible or ineligible for SSN help using labelled data, such as past program beneficiary records and socioeconomic statistics. The results show that the random forest-based ML algorithm outperforms others in terms of higher accuracy. This paper developed a mobile application through which people can easily apply for SSN programs and see the results. The user rating results provide information on the suitability of the proposed scheme.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 181-206
Issue: 2
Volume: 16
Year: 2024
Keywords: social protection program; machine learning; eligibility prediction; beneficiary selection; old age allowance; poor people support; Android application.
File-URL: http://www.inderscience.com/link.php?id=137864
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Handle: RePEc:ids:injdan:v:16:y:2024:i:2:p:181-206

Template-Type: ReDIF-Article 1.0
Author-Name: Mehbooba P. Shareef
Author-X-Name-First: Mehbooba P.
Author-X-Name-Last: Shareef
Author-Name: Babita Roslind
Author-X-Name-First: Babita
Author-X-Name-Last: Roslind
Author-Name: Jimson Mathew
Author-X-Name-First: Jimson
Author-X-Name-Last: Mathew
Title: FESSARec: explaining course recommendations using fuzzy expert system and self-attention
Abstract:
Recommendations generated by a model become more convincing when the system is capable of explaining the rationale behind the recommendations with respect to various decision parameters involved. A recommendation system which uses fuzzy expert system and self attention (FESSARec) to explain the recommendations is proposed here. The self-attention module extracts features of learners and courses and generates attention weights which will be used to explain the recommendations. The fuzzy expert system extracts relevant rules from the additional domain knowledge available in the datasets. As a result of this hybrid approach, FESSARec outperforms the recent architectures with which it is compared and obtains a very small root mean square error (RMSE) score of 0.65. FESSARec is also capable of producing top-N recommendations with a very high NDCG of 0.89 and HR of 0.72. It outperforms the best e-commerce baseline by 8% and the educational baseline by 16% of lower error rates.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 207-221
Issue: 2
Volume: 16
Year: 2024
Keywords: E-learning; MOOC; massive open online course; educational recommendation system; explainable recommendation system; self-attention-based recommendation; fuzzy expert systems.
File-URL: http://www.inderscience.com/link.php?id=137870
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Handle: RePEc:ids:injdan:v:16:y:2024:i:2:p:207-221

Template-Type: ReDIF-Article 1.0
Author-Name: Sushovon Jana
Author-X-Name-First: Sushovon
Author-X-Name-Last: Jana
Author-Name: Chandranath Pal
Author-X-Name-First: Chandranath
Author-X-Name-Last: Pal
Title: A study of stellar evolution based on structural equation modelling of stellar parameters
Abstract:
A study on the sequence of changes in a star with time is an important aspect for understanding the universe. One can not observe the whole lifetime of any single star. In this paper we have considered stellar parameters at different metallicity levels of stars to accomplish stellar evolutionary studies. We have studied the changes in the relationship structures between different stellar parameters at different metallicity levels. Before doing this, we have compressed the stellar parameter space on the basis of nonlinear principal component analysis (PCA) using Neural Networks and have classified stellar parameters into two groups based on agglomerative hierarchical clustering technique around latent variables. Then we have taken into account the exploratory structural equation modelling (SEM) technique to evaluate the relationship structures between stellar parameters. Our analysis clearly highlights the changes in factorability of different stellar parameters and their associative structures over different metallicity levels.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 105-125
Issue: 2
Volume: 16
Year: 2024
Keywords: stellar parameter; dimension reduction; variable clustering; exploratory factors; structure equation modelling.
File-URL: http://www.inderscience.com/link.php?id=137872
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Handle: RePEc:ids:injdan:v:16:y:2024:i:2:p:105-125

Template-Type: ReDIF-Article 1.0
Author-Name: Sushant Kr. Vishnoi
Author-X-Name-First: Sushant Kr.
Author-X-Name-Last: Vishnoi
Author-Name: Smriti Mathur
Author-X-Name-First: Smriti
Author-X-Name-Last: Mathur
Author-Name: Teena Bagga
Author-X-Name-First: Teena
Author-X-Name-Last: Bagga
Author-Name: Abhishek Singhal
Author-X-Name-First: Abhishek
Author-X-Name-Last: Singhal
Author-Name: Pankaj Rawal
Author-X-Name-First: Pankaj
Author-X-Name-Last: Rawal
Author-Name: Sanjeev Sharma
Author-X-Name-First: Sanjeev
Author-X-Name-Last: Sharma
Author-Name: Rajesh Yadav
Author-X-Name-First: Rajesh
Author-X-Name-Last: Yadav
Title: Construct modelling, statistical analysis and empirical validation using PLS-SEM: a step-by-step guide of the analysis procedure
Abstract:
Partial least square-structured equation modelling (PLS-SEM) is a widely accepted tool for statistical analysis in social science research. The complex architecture of PLS-SEM sometimes makes it difficult for users to understand the taxonomy, nomenclature, or process of statistical analysis. This research study proposes summarising the procedure adopted in PLS-SEM for data analysis. Measurement evaluation and structural model was the subject of discussion, with a focus on the statistical techniques employed. Furthermore, the threshold values corresponding to statistical tools under measurement and structural model were also provided. The inference of these threshold values were also discussed with an eye on improving researchers' awareness and understanding. The discussion about the methodology adopted in statistical analysis with the help of PLS-SEM is also reported. Finally, the limitations of the research work were presented, and further study directions were streamlined.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 162-180
Issue: 2
Volume: 16
Year: 2024
Keywords: PLS-SEM; partial least square-structured equation modelling; smart-PLS; structural model; measurement model.
File-URL: http://www.inderscience.com/link.php?id=137877
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Handle: RePEc:ids:injdan:v:16:y:2024:i:2:p:162-180

Template-Type: ReDIF-Article 1.0
Author-Name: Anupama Nandeppanavar
Author-X-Name-First: Anupama
Author-X-Name-Last: Nandeppanavar
Author-Name: Medha Kudari
Author-X-Name-First: Medha
Author-X-Name-Last: Kudari
Author-Name: Prasanna Bammigatti
Author-X-Name-First: Prasanna
Author-X-Name-Last: Bammigatti
Author-Name: Kaveri Vakkund
Author-X-Name-First: Kaveri
Author-X-Name-Last: Vakkund
Title: A machine learning-based food recommendation system with nutrition estimation
Abstract:
The human body needs energy to perform various activities, which are provided by calories. The proposed work is an efficient, user-friendly tool to assist calorie calculation. The system takes inputs such as height, weight, age, gender, and daily exercise level to estimate the recommended daily caloric intake. To achieve this, three machine learning models, K-nearest neighbours (KNN), decision tree and random forest algorithms, are employed to enhance the accuracy of predictions. Model accuracy achieved is 96.4% for KNN, 97.1% for decision tree and 96.8% using random forest algorithms. In addition to providing personalised caloric intake recommendations, the proposed system also offers diet recipes for breakfast, lunch and dinner tailored to the individuals's specific needs and preferences. Through the integration of machine learning algorithms, a user-friendly GUI, and personalised diet recommendations, the project aims to promote healthier eating habits and overall well-being for users.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 487-507
Issue: 4
Volume: 16
Year: 2024
Keywords: accuracy; BMI; body mass index; calorie; data processing; dietary; recipes; user interface visualisation.
File-URL: http://www.inderscience.com/link.php?id=142485
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Handle: RePEc:ids:injdan:v:16:y:2024:i:4:p:487-507

Template-Type: ReDIF-Article 1.0
Author-Name: Tushtee Varshney
Author-X-Name-First: Tushtee
Author-X-Name-Last: Varshney
Author-Name: Sonam Gupta
Author-X-Name-First: Sonam
Author-X-Name-Last: Gupta
Author-Name: Lipika Goel
Author-X-Name-First: Lipika
Author-X-Name-Last: Goel
Author-Name: Ishaan Saxena
Author-X-Name-First: Ishaan
Author-X-Name-Last: Saxena
Author-Name: Arjun Singh
Author-X-Name-First: Arjun
Author-X-Name-Last: Singh
Author-Name: Arun Kumar Yadav
Author-X-Name-First: Arun Kumar
Author-X-Name-Last: Yadav
Author-Name: Pradeep Gupta
Author-X-Name-First: Pradeep
Author-X-Name-Last: Gupta
Title: Novel approach for depression detection on Reddit post
Abstract:
Psychotic disorder is one of the major health problems found in humans. Mostly every age group of the population is affected by a psychotic disorder called depression. Depression causes a person with low mood and loss of interest, ideal in working time, and irregularities in sleep and eating habits. The analysis of emotional feelings behind the text is detected by machine learning technology called sentimental analysis or psychological analysis. In this study, we took Reddit as the social platform to collect datasets and studied to know the hidden behaviour of the individual using machine learning algorithm logistic regression, naive Bayes Decision Tree, XGBoost, and deep learning classifier CNN, maximum entropy. The classifiers are first studied individually on the dataset, then the proposed model is created using the classifier logistic regression, multilayer perceptron, and XGBoost with an accuracy of approximately 93% and precision of 95%.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 367-385
Issue: 4
Volume: 16
Year: 2024
Keywords: machine learning; depression; XGBoost; Reddit; multilayer perceptron; logistic regression; psychotic disorder; deep learning.
File-URL: http://www.inderscience.com/link.php?id=142486
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Handle: RePEc:ids:injdan:v:16:y:2024:i:4:p:367-385

Template-Type: ReDIF-Article 1.0
Author-Name: Kara L. Combs
Author-X-Name-First: Kara L.
Author-X-Name-Last: Combs
Author-Name: Trevor J. Bihl
Author-X-Name-First: Trevor J.
Author-X-Name-Last: Bihl
Title: Question optimisation: building quiz bowl tournament sets
Abstract:
Quiz bowl is an activity in which players test their knowledge against others in tournaments. Quiz bowl set organisation is a lengthy and involved process involving many expectations related to the set's content and quality. Current techniques to address question placement rely on lengthy, manually-edited databases, if any. Ensuring a set meets all expectations is vital to producing a high-quality set that is suitable for competition. We propose a repeatable methodology for optimising question placement implemented in both Python and Excel to be compared to the traditional manual method. On the initial data, the baseline manually-produced set was matched qualitatively by the other methods, which also had repeatability, traceability, and reduction of time spent. These results were furthermore supported by a three-way comparison of a portion of the real-world 2022 state competition questions by the Head Editor, who recommended the Python version for future use.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 386-409
Issue: 4
Volume: 16
Year: 2024
Keywords: quiz bowl; quizbowl; optimisation; simplex linear programming; placement.
File-URL: http://www.inderscience.com/link.php?id=142487
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Handle: RePEc:ids:injdan:v:16:y:2024:i:4:p:386-409

Template-Type: ReDIF-Article 1.0
Author-Name: Rohit Rastogi
Author-X-Name-First: Rohit
Author-X-Name-Last: Rastogi
Author-Name: Mamta Saxena
Author-X-Name-First: Mamta
Author-X-Name-Last: Saxena
Author-Name: Richa Singh
Author-X-Name-First: Richa
Author-X-Name-Last: Singh
Author-Name: Yati Varshney
Author-X-Name-First: Yati
Author-X-Name-Last: Varshney
Author-Name: Pranav Sharma
Author-X-Name-First: Pranav
Author-X-Name-Last: Sharma
Author-Name: Vaibhav Aggarwal
Author-X-Name-First: Vaibhav
Author-X-Name-Last: Aggarwal
Title: Establishing the significance of spiritual environment with the effects of herbals: an empirical approach on students' QoL in the South Asian continent
Abstract:
In the past few years, humanity has faced too many challenges due to the outrage of the Corona virus pandemic. But even in such difficulty, the world cannot stop growing, so we humans have adopted various lifestyle changes like work from home, online education, etc. in order to make our jobs done. But these changes had affected us physically and mentally in many ways, and the major impact of these changes is seen on adolescents (age group of 10-18 years), resulting in being diagnosed with many physical and mental health ailments like obesity, lack of mobility, stress, mental trauma, etc. due to less exposure to the outside environment and sudden increase of social media. These all aspects are hindering the overall growth needed in that particular age group. So, this study is based on the impact of some techniques based on Indian Vedic science to rejuvenate the physical and mental health of adolescents or to counter the post-Covid effects.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 410-461
Issue: 4
Volume: 16
Year: 2024
Keywords: adolescents; obesity; mental trauma; SaraswatiPanchak; yoga; Yajna; control group; experimental group.
File-URL: http://www.inderscience.com/link.php?id=142488
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Handle: RePEc:ids:injdan:v:16:y:2024:i:4:p:410-461

Template-Type: ReDIF-Article 1.0
Author-Name: Hamza Benyezza
Author-X-Name-First: Hamza
Author-X-Name-Last: Benyezza
Author-Name: Reda Kara
Author-X-Name-First: Reda
Author-X-Name-Last: Kara
Author-Name: Mounir Bouhedda
Author-X-Name-First: Mounir
Author-X-Name-Last: Bouhedda
Author-Name: Mosaab Benhadjer
Author-X-Name-First: Mosaab
Author-X-Name-Last: Benhadjer
Author-Name: Patrice Wira
Author-X-Name-First: Patrice
Author-X-Name-Last: Wira
Author-Name: Samia Rebouh
Author-X-Name-First: Samia
Author-X-Name-Last: Rebouh
Title: Streamlining check processing: advancing Arabic handwriting verification with a CNN-based system
Abstract:
Arabic handwriting analysis and verification pose challenges due to their unique characteristics. Deep learning techniques have gained prominence in computer vision for their ability to learn from data. This study proposes a high-speed and precise solution using a convolutional neural network (CNN) to automate the verification process of Algerian postal checks written in Arabic handwriting. The solution consists of hardware and software components. The software includes four CNN models to identify the check's ID number (CID), user's signature (US), handwriting courtesy (HCA), and legal amount (HLA). The hardware setup involves a camera connected to a Raspberry PI 3. Test results demonstrate the proposed approach's effectiveness, achieving accuracy of 100% for CID, 98.61% for US, 99.28% for HCA, and 96.35% for HLA. This comprehensive system offers a promising solution for efficient verification of Arabic handwritten postal checks.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 462-486
Issue: 4
Volume: 16
Year: 2024
Keywords: deep learning; CNN; convolutional neural network; computer vision; Arabic handwriting; check verification.
File-URL: http://www.inderscience.com/link.php?id=142490
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Handle: RePEc:ids:injdan:v:16:y:2024:i:4:p:462-486

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammed Bouasabah
Author-X-Name-First: Mohammed
Author-X-Name-Last: Bouasabah
Title: Analysis of machine learning's performance in stock market prediction, compared to traditional technical analysis indicators
Abstract:
This study compares the performance of machine learning (ML) algorithms with traditional technical indicators in real estate, technology, and healthcare sectors. Unveiling the limitations of classical indicators, particularly their struggle to surpass the 50% threshold, the research explores the predictive capabilities of ML algorithms, focusing on AdaBoost and support vector machine (SVM). The relative strength index (RSI) emerges as a reliable performer for buy decisions but with potential oversight. Results affirm the superiority of ML algorithms in precision, recall, and F1 score, transcending traditional indicators. Sector-specific variations showcase exceptional ML efficacy, particularly in healthcare. Algorithmic evaluation spotlights AdaBoost and SVM, underscoring the importance of strategic selection. The study advocates for a nuanced approach, blending RSI with ML for refined strategies. In conclusion, this research contributes significantly to financial decision-making, exposing limitations and positioning ML algorithms as powerful tools for improved investment strategies.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 32-46
Issue: 1
Volume: 16
Year: 2024
Keywords: machine learning; ML; technical analysis indicators; prediction; financial market; data analysis; SMA; MACD; RSI; trading; support vector machine; SVM.
File-URL: http://www.inderscience.com/link.php?id=137465
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Handle: RePEc:ids:injdan:v:16:y:2024:i:1:p:32-46

Template-Type: ReDIF-Article 1.0
Author-Name: Mohammad Salah Uddin
Author-X-Name-First: Mohammad Salah
Author-X-Name-Last: Uddin
Author-Name: Mahfuzulhoq Chowdhury
Author-X-Name-First: Mahfuzulhoq
Author-X-Name-Last: Chowdhury
Title: A user friendly anger and anxiety disorder prediction scheme using machine learning and a mobile application for mental healthcare
Abstract:
The growing prevalence of mental health disorders concerns has motivated the development of innovative technologies to support mental well-being. The previous literary works on mental healthcare did not investigate anger and anxiety disorder prediction by considering 23 features. There is a lack of mental healthcare assistance mobile applications in the literary works by considering anger and anxiety assessment and necessary emergency assistance features. To solve these issues, this paper initiates a machine learning model-based anger and anxiety prediction scheme by examining different machine learning algorithms. Our analytical results show that the logistic regression model shows better prediction results among all machine learning algorithms in terms of higher accuracy, precision, recall, and error rate. This paper presents a mental healthcare mobile application with anger and anxiety assessment, physical exercise suggestions, hospital search, doctor appointment booking, and emergency contact. The evaluation result shows the efficiency of the proposed scheme.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 47-81
Issue: 1
Volume: 16
Year: 2024
Keywords: anger and anxiety prediction; self-assessment tools; mental healthcare; mobile application; machine learning; logistic regression; support vector machine; SVM; K-nearest neighbours; KNN; decision tree; multi-class classification.
File-URL: http://www.inderscience.com/link.php?id=137466
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Handle: RePEc:ids:injdan:v:16:y:2024:i:1:p:47-81

Template-Type: ReDIF-Article 1.0
Author-Name: Dharyll Prince Mariscal Abellana
Author-X-Name-First: Dharyll Prince Mariscal
Author-X-Name-Last: Abellana
Title: Modelling the critical success factors of software development freelancing using an intuitionistic fuzzy DEMATEL approach
Abstract:
This paper investigates the critical success factors of software development freelancing in the Philippines during the COVID-19 pandemic. The paper adopts the intuitionistic fuzzy decision making trial and evaluation laboratory (IF-DEMATEL), which is a causal modelling approach based on multiple criteria decision making and intuitionistic fuzzy set theory. As a result, the study was able to derive 26 critical success factors for software development freelancing. The study finds significant cause groups and effect groups among the critical success factors. In particular, results suggest that the cause groups are highly associated with the management-side of the project than any other factor. The results of this study would be significant to the literature in many ways. For one, it is the first to investigate the critical success factors of software development freelancing. For another, it is the first to model the dynamics of these critical success factors under a causal modelling framework.
Journal: Int. J. of Data Analysis Techniques and Strategies
Pages: 17-31
Issue: 1
Volume: 16
Year: 2024
Keywords: intuitionistic fuzzy sets; freelancing during the COVID-19 pandemic; critical success factors; software development; soft computing.
File-URL: http://www.inderscience.com/link.php?id=137467
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Handle: RePEc:ids:injdan:v:16:y:2024:i:1:p:17-31