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The inherent risk of climate change becoming a hindrance in a business supply chain
Purpose: A sophisticated network of interconnected supply chains serves as the central organising principle for most of the manufacturing serving the global economy. Right from computers and vehicles to life-saving pharmaceuticals and food is made possible by the supply web. The final goods part of a supply chain may include thousands of components from various global regions. These supply chains have been refined to achieve the highest speed and efficiency. Methodology: This study includes a sample of 127 firms that have been in business for at least 15 years and are familiar with business dynamics. The authors anticipate how climate risks, common in global supply networks, will evolve over the next several decades. This study examines the vulnerability of nine commercial value chains to climatic disasters. Also, it explores company and value chain vulnerabilities, financial losses, and adaptation or strategic methods to increase resilience. Findings: Companies must plan forward in terms of locations by retaining operational facilities while running new operations in less risky places. Without change, supply networks will become unstable and dangerous shortly. Using their climate objectives, businesses must decarbonise their supply chains. Businesses should connect with suppliers longer-term. The quality and dependability of a company's suppliers affect its success and safety. Future-focussed corporations are already engaging their suppliers on health, safety, and environmental issues. Significance: The typology may be helpful to executives as they make decisions about the strategic option(s) they wish to pick to address climate change. These decisions can also be influenced by the insights provided by the research about the present status of operations of other firms from different sectors all over the globe. 2023 by Chabi Gupta and Swati Bhatia. -
Cultivating efficiency-harnessing artificial intelligence (AI) for sustainable agriculture supply chains
This chapter intends to investigate and present the possibility of utilizing artificial intelligence to transform the agricultural supply chain. The primary objective is to enhance efficiency, sustainability, and adaptability in response to a shifting climate and expanding global population. The results of this investigation provide significant and valuable knowledge for both new businesses and well-established corporations who have a vested interest in embarking on an intelligent and sustainable digital revolution within the agricultural sector and food supply chains. These findings present insights that can guide these enterprises in their pursuit of transforming their operations using technology to enhance efficiency, productivity, and sustainability. By harnessing the benefits offered by digitization, organizations operating within the agriculture industry will be able to streamline processes, optimize resource allocation, reduce waste, and improve traceability throughout the supply chain network while ultimately securing long-term success. 2024, IGI Global. All rights reserved. -
Designing Artificial Intelligence-Enabled Training Approaches and Models for Physical Disabilities Individuals
The focus of this research is on investigating AI-based strategies and models that can be used to develop workforce training systems specifically for individuals with physical disabilities. The goal is to leverage the advancements in artificial intelligence (AI) and its potential impact on workplace learning and development. There is an increasing demand for utilizing AI capabilities to design comprehensive training programs that are both inclusive and effective for people who face physical challenges. The research will examine effective strategies, real-life examples, and current AI-based training platforms for people with physical disabilities. Additionally, it aims to tackle the obstacles and ethical matters linked to incorporating AI in workforce training. These concerns include mitigating biases, ensuring accessibility, and safeguarding privacy. The outcomes of this study will assist in creating progressive approaches and frameworks driven by AI that can empower individuals with physical disabilities by improving their employability prospects while simultaneously fostering inclusivity within workforce training. The chapter will also explore the integration of AI-powered solutions in training programs for physically challenged individuals. By utilizing AI technologies like personalized learning algorithms, predictive analytics, and adaptive content delivery systems, training can be customized to cater to the unique requirements and learning needs of everyone. The implementation of AI has the potential to automate processes, analyze data effectively, and generate personalized learning pathways for improved accessibility. 2024 selection and editorial matter, Alex Khang; individual chapters, the contributors. -
Malicious Traffic Classification in WSN using Deep Learning Approaches
Classifying malicious traffic in Wireless Sensor Networks (WSNs) is crucial for maintaining the network's security and dependability. Traditional security techniques are challenging to deploy in WSNs because they comprise tiny, resourceconstrained components with limited processing and energy capabilities. On the other hand, machine learning-based techniques, such as Deep Learning (DL) models like LSTMs, may be used to detect and categorize fraudulent traffic accurately. The classification of malicious traffic in WSNs is crucial because of security. To protect the network's integrity, data, and performance and ensure the system functions properly and securely for its intended use, hostile traffic categorization in WSNs is essential. Classifying malicious communication in a WSN using a Long Short-Term Memory (LSTM) is efficient. WSNs are susceptible to several security risks, such as malicious nodes or traffic that can impair network performance or endanger data integrity. In sequential data processing, LSTM is a Recurrent Neural Network (RNN) appropriate for identifying patterns in network traffic data. 2023 IEEE. -
Metaheuristic Machine Learning Algorithms for Liver Disease Prediction
In machine learning, optimizing solutions is critical for improving performance. This study explores the use of metaheuristic algorithms to enhance key processes such as hyperparameter tuning, feature selection, and model optimization. Specifically, we integrate the Artificial Bee Colony (ABC) algorithm with Random Forest and Decision Tree models to improve the accuracy and efficiency of disease prediction. Machine learning has the potential to uncover complex patterns in medical data, offering transformative capabilities in disease diagnosis. However, selecting the optimal algorithm for model optimization presents a significant challenge. In this work, we employ Random Forest, Decision Tree models, and the ABC algorithmbased on the foraging behaviours of honeybeesto predict liver disease using a dataset from Indian medical records. Our experiments demonstrate that the Random Forest model achieves an accuracy of 85.12%, the Decision Tree model 76.89%, and the ABC algorithm 80.45%. These findings underscore the promise of metaheuristic approaches in machine learning, with the ABC algorithm proving to be a valuable tool in improving predictive accuracy. In conclusion, the integration of machine learning models with metaheuristic techniques, such as the ABC algorithm, represents a significant advancement in disease prediction, driving progress in data-driven healthcare. 2024, Iquz Galaxy Publisher. All rights reserved. -
Taste of your Hometown: Evoking Nostalgia through the Diner Space in Midnight Diner
Restaurant spaces are seen as a space that intersects between the personal and the cultural. This paper looks at a Japanese TV series, Midnight Diner, an adaptation of a Manga by Yaro Abe, where a tiny, not-so-popular restaurant in one of the back lanes in Tokyo serves food from midnight to 7 a.m. This show makes several meaningful connections between food, memory, and space as the customers come with specific food cravings, and the Master (the owner-chef of the Diner) is happy to customize. The diner space transcends the traditional meaning of a diner that not only serves food to satiate hunger but is an experience that evokes nostalgia for their home and their loved ones. The wistfulness in the lives of the customers for their home, people, and home-cooked food finds a release in the diner. The space of the diner acquires different meanings, as do the dishes the customer relishes. Thus, the paper explores the diner space as a symbolic space where each episode introduces a new character, a new story, and the past they deal with while the food is prepared and consumed on screen. The taste, smell, texture, and ingredients of the food in this diner stimulate the senses, and this space acquires emotional meaning for everyone stepping in. 2023, University of Malaya. All rights reserved. -
Volatility in Indian stock markets during COVID-19: An analysis of equity investment strategies
The aim of the paper is to evaluate the impact of novel COVID-19 on the returns and volatility of Indian stock markets with special reference to equity investment strategies of the Bombay Stock Exchange. For the purpose of evaluating the impact, the study has applied GARCH. The research has considered a time frame from March 2015 to January 2021. Prior to implementing GARCH model, pre-estimation tests (i.e., augmented Dickey-Fuller and ARCH-Lagrange multiplier) were conducted. Outcomes clearly indicate that the returns during the crisis for all the strategy indices have been negative, which means that the COVID-19 outbreak resulted in massive losses. Additionally, 'during crisis' period showed an increase in volatility for all the strategy indices depicting that the pandemic has a long-lasting effect and will take time to fade off. This research will help the investors in the investment decision process by giving them insights about the different strategies. 2021. -
Usability Evaluation and Classification of mHealth Applications for Type 2 Diabetes Mellitus Using MARS and ID3 Algorithm
The rapid growth of mHealth applications for Type 2 Diabetes Mellitus (T2DM) patients self-management has motivated the evaluation of these applications from both the usability and user point of view. The objective of this study was to identify mHealth applications that focus on T2DM from the Android store and rate them from the usability perspective using the MARS tool. Additionally, a classification of these mHealth applications was conducted using the ID3 algorithm to identify the most preferred application. The usability of the applications was assessed by two experts using MARS. A total of 11 mHealth applications were identified from the initial search, which fulfilled our inclusion criteria. The usability of the applications was rated using the MARS scale, from 1 (inadequate) to 5 (excellent). The Functionality (3.23) and Aesthetics (3.22) attributes had the highest score, whereas Information (3.1) had the lowest score. Among the 11 applications, mySugr had the highest average MARS score for both Application Quality (4.1/5) as well as Application Subjective Quality (4.5/5). Moreover, from the classification conducted using the ID3 algorithm, it was observed that 6 out of 11 mHealth applications were preferred for the self-management of T2DM. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Multi-Criteria Usability Evaluation of mHealth Applications on Type 2 Diabetes Mellitus Using Two Hybrid MCDM Models: CODAS-FAHP and MOORA-FAHP
People use mHealth applications to help manage and keep track of their health conditions more effectively. With the increase of mHealth applications, it has become more difficult to choose the best applications that are user-friendly and provide user satisfaction. The best techniques for any decision-making challenge are multi-criteria decision-making (MCDM) methodologies. However, traditional MCDM methods cannot provide accurate results in complex situations. Currently, researchers are focusing on the use of hybrid MCDM methods to provide accurate decisions for complex problems. Thus, the authors in this paper proposed two hybrid MCDM methods, CODAS-FAHP and MOORA-FAHP, to assess the usability of the five most familiar mHealth applications that focus on type 2 diabetes mellitus (T2DM), based on ten criteria. The fuzzy Analytic Hierarchy Process (FAHP) is applied for efficient weight estimation by removing the vagueness and ambiguity of expert judgment. The CODAS and MOORA MCDM methods are used to rank the mHealth applications, depending on the usability parameter, and to select the best application. The resulting analysis shows that the ranking from both hybrid models is sufficiently consistent. To assess the proposed frameworks stability and validity, a sensitivity analysis was performed. It showed that the result is consistent with the proposed hybrid model. 2022 by the authors. Licensee MDPI, Basel, Switzerland. -
Advances in suicide prevention: Critical overview of the gaps in suicide risk assessments, multimodal strategies, medicolegal risks, and the emerging evidence
The CDC reports that the United States has the highest suicide rates in over 80 years. Numerous public policies aimed at reducing the rising suicide rates, such as Aetna's partnership with the American Foundation for Suicide Prevention (AFSP) and the zero-suicide initiative, continue to challenge these attempts. It, therefore, remains imperative to explore the shortcomings of these efforts that hamper their efficiency in reducing suicide rates. Advancements in research over time have sparked scientific skepticism, encouraging re-evaluation of established concepts. The current paper tests prevalent assumptions and arguments to uncover a scientifically informed approach to addressing rising suicide rates in clinical settings. The Author(s), 2024. -
Antecedents and Trajectories of the Child and Adolescent Mental Health Crisis: Assimilating Empirically Guided Pathways for Stakeholders
Importance: Amid and following the COVID-19 pandemic, there has been a growing focus on understanding the underlying etiology of the mental health crisis in children and youth. However, there remains a dearth of empirically driven literature to comprehensively explore these issues. This narrative review delves into current mental health challenges among children and youth, examining perspectives from both pre-pandemic and pandemic periods. Observations: Research highlights reveal concerning statistics, such as 1 in 5 children experience mental health disorders. The pandemic exacerbated these issues, introducing stressors such as job losses and heightened anticipatory anxiety. Race relations have emerged as a significant public health concern, with biases impacting students, particularly affecting Asian, black, and multiracial individuals. Substance use trends indicate a rise in overdose deaths, particularly among adolescents, with cannabis use linked to adverse outcomes. Increased screen time and income disparities further compound mental health challenges. Conclusions and Relevance: Proposed public health mitigation strategies include improving access to evidence-based treatments, implementing legislative measures for early identification and treatment of developmental disorders, and enhancing suicide prevention efforts. School-based interventions and vocational-technical education are crucial, alongside initiatives targeting sleep hygiene, social media usage, nutrition, and physical activity. Educating health care professionals about both physical and mental health is essential to address workforce burnout and effectively manage clinical complexities. 2024 Physicians Postgraduate Press, Inc. -
Framework to analyze customer's feedback in smartphone industry using opinion mining
In the present age, cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact. 2018 Institute of Advanced Engineering and Science. All rights reserved. -
Framework to analyze customer's feedback in smartphone industry using opinion mining
In the present age cellular phones are the largest selling products in the world. Big Data Analytics is a method used for examining large and varied data, which we know as big data. Big data analytics is very useful for understanding the world of cellphone business. It is important to understand the requirements, demands, and opinions of the customer. Opinion Mining is getting more important than ever before, for performing analysis and forecasting customer behavior and preferences. This study proposes a framework about the key features of cellphones based on which, customers buy them and rate them accordingly. This research work also provides balanced and well researched reasons as to why few companies enjoy dominance in the market, while others do not make as much of an impact 2018 Institute of Advanced Engineering and Science. All rights resented. -
Assessment of ML techniques and suitability to predict the compressive strength of high-performance concrete (HPC)
Using industrial soil waste or secondary materials for making cement and concrete has encouraged the construction industry because it uses fewer natural resources. High-performance concrete (HPC) is recognized for its exceptional strength and sturdiness compared to conventional concrete. Accurate prediction of the compressive concentration of HPC is vital for optimizing the concrete mix design and ensuring structural integrity. Machine learning (ML) techniques have shown promise in predicting concrete properties, including compressive strength. This research focuses on various ML techniques for their suitability in predicting the compressive dilution of HPC. In this research, the Extended Deep Neural Network (EDNN) technique is used to analyze the strengths, limitations, and performance of different ML algorithms and identify the most effective methods for this specific prediction task. However, there is a problem with accuracy. Therefore, our research approach is the EDNN-centred strength characteristics prediction of HPC. In the suggested approach, data is initially acquired. Afterward, the data is pre-processed through normalization and removing missing data. Thus, the data are fed into the EDNN algorithm, which forecasts the strength characteristics of the particular mixed input designs. With the Multi-Objective Jellyfish Optimization (MOJO) technique, the value of weight is initialized in the EDNN. The activation function is the Gaussian radial function. In the experimental analysis, the implementation of the suggested EDNN is evaluated to the performance of the prevailing algorithms. When compared to current research methodologies, the proposed method performs better in this regard. The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024. -
Detection and localization for watermarking technique using LSB encryption for DICOM Image
Watermarking is an effective way of transferring hidden data from one place to another, or proving ownership of digital content. The hidden data can be text, audio, images GIF etc., the data is embedded in a cover object usually an image or a video sequence. Usually the watermarking system(s) rely on their hidden aspect, as their primary security measure, once this is established that the cover object is counting some hidden data, then it is generally possible to recover the hidden information. The author proposed an in-genuine technique for DICOM color image water marking by combining Multi Quadrant LSB with truly random mixed key cryptography. This system provides a high level of security by just the water marking technique, as it breaks the cover image into up to four quadrants, & does LSB replacement of two bytes each quadrant. The bit sequence as the quadrant sequence can be randomized to increase the randomness, use of truly random mixed key cryptography, by using a pre shared, variable length, truly random, private key, turns hidden data into noise, which can only be recovered by having the private key. Thus, the proposed technique truly diminishes the probability of recovering hidden data, even if it is detected that something is hidden in cover object. 2022 Taru Publications. -
Design of Machine Learning Model for Health Care Index during COVID- 19
Predicting stock prices and index movement in the field of finance is always challenging. The events in the macro-economic framework affect the trends of the market and the COVID-19 pandemic was a major reason for the slowdown of the global economies in the short run. It was assumed that the healthcare industry has completely been transformed due to changing behavioral habits of individuals. The study presents the time series approach with the help of historical prices on the Bombay Stock Exchanges (BSE) Health Care Index, both in the long and short run, using the ARIMA model. The period of the study is from February 1999 to August 2020. The ARIMA equations are used to forecast the future price movement of the Health Care Index till December 2020. The findings reveal that the market will continue with the same volatility, and investors should give due attention to analysis and logical reasoning rather than following their feeling of overconfidence. 2024 Taylor & Francis Group, LLC. -
Sustainability of Indian tourism in backdrop of COVID-19
The Indian tourism and travel industry is one of the fastest growing industry. According to WTTC (2019), India ranked 10th among 185 countries in terms of travel & tourism's having a total contribution to GDP of 6.8% of the total economy, Rs. 13,68,100 crores (US$ 194.30 billion) (www.ibef.org). In the year 2017, The United Nations World Tourism Organization (UNWTO) has declared 2017 as the 'International Year of Sustainable Tourism for Development', which underscores tourism's critical role in fostering inclusive growth. Hence, the efforts to achieve sustainability got an impetus and gained much wanted attention. However, everything came to standstill with the onset of Corona Virus Pandemic in November 2019, questioning the survival of the industry itself. The present crisis caused tremendous losses which have resulted in large scale job losses bringing the sustainability in question. This study aims to investigate the state of sustainability of Indian tourism through infrastructure development, environmental degradation, social, economic and cultural impacts on destinations due to this growth in the backdrop of the present COVID pandemic. It is an empirical study of perceptions of tourists to Indian destinations. The data was collected through self-administered questionnaires and interviews. A total of 520 valid responses were analyzed and results revealed a different scenario. The study concludes with a discussion of the findings and providing a few recommendations to rectify the situation for a sustainable industry and future. 2021 Ecological Society of India. All rights reserved. -
Feature extraction and diagnosis of dementia using magnetic resonance imaging
Dementia is a state of mind in which the sufferer tends to forget important data like memories, language, etc.. This is caused due to the brain cells that are damaged. The damaged brain cells and the intensity of the damage can be detected by using Magnetic Resonance Imaging. In this process, two extraction techniques, Gray Level Co-Occurrence Matrix (GLCM) and the Gray Level Run-Length matrix (GLRM), are used for the clear extraction of data from the image of the brain. Then the data obtained from the extraction techniques are further analyzed using four machine learning classifiers named Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Random Forest (RF), and the combination of two classifiers (SVM+KNN). The results are further analyzed using a confusion matrix to find accuracy, precision, TPR/FPR - True and False Positive Rate, and TNR/FNR - True and False Negative Rate. The maximum accuracy of 93.53% is obtained using the GLRM Feature Extraction (FE) technique with the combination of the SVM and KNN algorithm. 2023, Bentham Books imprint. All rights reserved. -
Exploring the Impact of Latent and Obscure Factors on Left-Censored Data: Bayesian Approaches and Case Study
In the realm of scientific investigation, traditional survival studies have historically focused on mitigating failures over time. However, when both observed and unobserved variables remain enigmatic, adverse consequences can arise. Frailty models offer a promising approach to understanding the effects of these latent factors. In this scholarly work, we hypothesize that frailty has a lasting impact on the reversed hazard rate. Notably, our research highlights the reliability of generalized Lindley frailty models, rooted in the generalized log-logistic type II distribution, as a robust framework for capturing the widespread influence of inherent variability. To estimate the associated parameters, we employ diverse loss functions such as SELF, MQSELF, and PLF within a Bayesian framework, forming the foundation for Markov Chain Monte Carlo methodology. We subsequently utilize Bayesian assessment strategies to assess the effectiveness of our proposed models. To illustrate their superiority, we employ data from renowned Australian twins as a demonstrative case study, establishing the innovative models advantages over those relying on inverse Gaussian and gamma frailty distributions. This study delves into the impact of hidden and obscure factors on left-censored data, utilizing Bayesian methodologies, with a specific emphasis on the application of generalized Lindley frailty models. Our findings contribute to a deeper understanding of survival analysis, particularly when dealing with complex and unobservable covariates. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Pure and suggestive impulse buying in mobile shopping app: shopping pattern of young consumers
Purpose: This study differentiates pure impulse buying behaviour from suggestive impulse buying behaviour in using mobile shopping applications (apps). This study aims to assess the moderating effects of instant discount and cashback promotional offers along with the mediating effects of impulse buying intention (IBI) and user satisfaction (US), using the app stimuli (performance expectancy, effort expectancy, layout, atmosphere, privacy and security). Design/methodology/approach: The study was done in three stages: analysis of variance, followed by structural equation modelling (SEM) and paired t-tests. Findings: The results showed that instant discounts and cashback offers are different from each other for the mediating variable IBI. The SEM results for pure impulse buying showed that, except for layout, the remaining variables have a positive relationship with IBI. For suggestive impulse buying, effort expectancy and layout were significantly related to both the mediating variables. Finally, pure and suggestive impulse buying behaviour showed significant differences. Originality/value: Previous studies have looked into impulse buying in its generic sense and not through the types of impulse buying they were measuring. As impulse buying behaviour is a predominant theme for discussion today, marketing professionals and researchers must comprehend the impact of app stimuli in the context of select types of impulse buying behaviour. 2024, Emerald Publishing Limited.