Towards sustainable textile and apparel industry: Exploring the role of business intelligence systems in the era of industry 4.0

Industry 4.0 is a new era of industrial revolution in which textile and apparel (T&A) companies are adopting and integrating advanced technologies to achieve sustainability and a competitive edge. Previous studies have just focused on the perspective of big data utilization in Industry 4.0 and neglected the role of business intelligence systems (BIS), especially in the T&A industry. The current study is one of the first to investigate the determinants of BIS adoption with an eye towards understanding how BIS can resolve sustainability issues in T&A companies with Industry 4.0 technologies. Methodology: A qualitative research approach is applied with 14 semi-structured in-depth interviews from 12 of the world's high-end T&A companies. The snowball and purposeful sampling strategy is used to select the participants. The qualitative content analysis technique is used to analyze the interview data. Results: The findings revealed various themes, such as sustainability issues in T&A companies, improved value creation processes with leading BI solutions, and perceived difficulties in the adoption of BIS. Major improvements are perceived in the apparel retail business because apparel companies are more prone to adopt the Industry 4.0 technologies with advanced business intelligence (BI) solutions. The results prove the pivotal role of economic sustainability in the adoption of BIS and Industry 4.0 technologies in T&A companies

Statistical assessment of business intelligence system adoption model for sustainable textile and apparel industry

The textile and apparel industry is one of the biggest competitive industries in the world. Nowadays, industry 4.0 concepts put pressures on textile and apparel companies to integrate advanced technologies. Consequently, Business Intelligence (BI) systems are diffusing rapidly to process large data sets to harness the true value of smart technologies. Regardless of its potentials, most textile and apparel companies are lagging and hesitating to adopt this credible innovation in the presence of a high failure rate (70%-80%) especially in developing countries. To achieve the successful adoption of BI systems, statistical assessment is required to better understand this complex phenomenon. Therefore, a BI system model based on Technology-Organization-Environment (TOE) is developed to evaluate the role of potential determinants pertaining to the users, technology, organization, and environment. Data were collected using a survey with self-administered questionnaires from decision-makers with authoritative designations in the textile and apparel industry, academia, and software companies. Influential relationships among critical determinants were assessed and validated by using Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach. The results of this study would contribute to the success of costly BI system projects and will motivate the industry experts to potentially assign investments for the BI projects in the developing countries to sustain in the competitive markets

Recent advances in passive UHF-RFID tag antenna design for improved read range in product packaging applications: a comprehensive review

Radio frequency identification (RFID) is a rapidly developing technology, and RFID sensors have become important components in many common technology applications. The passive ultra-high frequency (UHF) tags used in RFID sensors have a higher data transfer rate and longer read range and usually come in unique small and portable application designs. However, these tags suffer from significant frequency interference when mounted on metallic materials or placed near liquid surfaces. This paper presents the recent advancements made in passive UHF-RFID tag designs proposed to resolve the interference problems. We focus on those designs that are intended to improve antenna read range as well as scalability designs for miniaturized application

A generalized laser simulator algorithm for mobile robot path planning with obstacle avoidance

This paper aims to develop a new mobile robot path planning algorithm, called generalized laser simulator (GLS), for navigating autonomously mobile robots in the presence of static and dynamic obstacles. This algorithm enables a mobile robot to identify a feasible path while finding the target and avoiding obstacles while moving in complex regions. An optimal path between the start and target point is found by forming a wave of points in all directions towards the target position considering target minimum and border maximum distance principles. The algorithm will select the minimum path from the candidate points to target while avoiding obstacles. The obstacle borders are regarded as the environment’s borders for static obstacle avoidance. However, once dynamic obstacles appear in front of the GLS waves, the system detects them as new dynamic obstacle borders. Several experiments were carried out to validate the effectiveness and practicality of the GLS algorithm, including path-planning experiments in the presence of obstacles in a complex dynamic environment. The findings indicate that the robot could successfully find the correct path while avoiding obstacles. The proposed method is compared to other popular methods in terms of speed and path length in both real and simulated environments. According to the results, the GLS algorithm outperformed the original laser simulator (LS) method in path and success rate. With application of the all-direction border scan, it outperforms the A-star (A*) and PRM algorithms and provides safer and shorter paths. Furthermore, the path planning approach was validated for local planning in simulation and real-world tests, in which the proposed method produced the best path compared to the original LS algorithm

Phishing Attacks Survey: Types, Vectors, and Technical Approaches

Phishing attacks, which have existed for several decades and continue to be a major problem today, constitute a severe threat in the cyber world. Attackers are adopting multiple new and creative methods through which to conduct phishing attacks, which are growing rapidly. Therefore, there is a need to conduct a comprehensive review of past and current phishing approaches. In this paper, a review of the approaches used during phishing attacks is presented. This paper comprises a literature review, followed by a comprehensive examination of the characteristics of the existing classic, modern, and cutting-edge phishing attack techniques. The aims of this paper are to build awareness of phishing techniques, educate individuals about these attacks, and encourage the use of phishing prevention techniques, in addition to encouraging discourse among the professional community about this topic

Computation of the Values for the Riemann-Liouville Fractional Derivative of the Generalized Poly-logarithm Functions

In this article, we compute tables of values for the Riemann-Liouville fractional derivative of the generalized polylogarithm functions considering parameter values µ = 3; 4; 5 and s = 1 2 ; 3 2 ; −1 2 ; −3 2 . Several authors investigated such functions and their analytic properties, but no work can be found in the literature for the computation of their values. We perform numerical computations to evaluate Riemann-Liouville fractional derivative of the generalized polylogarithm functions for different values of the involved parameters. We validate the data obtained by using our new mathematical model (given in the form of a difference equation) and the known classical integral representations for µ = 3; 4; 5 and s = 1 2 ; 3 2 . It is worth mentioning that for the positive values of parameter s = 1 2 ; 3 2 , our calculations are consistent with the directly computed results by using their integral representation and 100% accuracy is achieved. Furthermore, it is obvious that the involved integrals R ∞ 0 t s−1e−3t (1−ze−t) 3 ; R ∞ 0 t s−1e−4t (1−ze−t) 4 ; R ∞ 0 t s−1e−5t (1−ze−t) 5 ; are not convergent for the negative values of parameter s and in this investigation we evaluate these integrals for the negative values of s

A Saudi Female Perspective on the Adoption of Online Banking with Saudi Arabian Banks

The adoption of online banking in Saudi Arabia is still emerging. The purpose of this study was to identify the factors that influenced Saudi females to adopt online banking with Saudi banks. This study answered the following research question: What are the factors that influence Saudi female users in Saudi Arabia to adopt online banking through Saudi Arabian banks? This study contributes to a gap in the literature regarding the limited studies of online banking from a Saudi female perspective. A qualitative method was used to conduct the study. A semi-structured interview was conducted to collect data from the participants. The sample consisted of 13 Saudi females who live in Riyadh, Saudi Arabia. The themes of this study developed by coding the transcripts via NVivo, then categorizing the responses into themes. These themes were identified according to the multiple responses from the participants repeatedly on each theme. The results provided seven main themes which influenced females to adopt online banking. Easiness and convenience were the preeminent influential themes according to the females followed by security, trust, user-friendly comfortable, and availability. The sub-themes were: (1) save time, (2) effortless, (3) easy to navigate, (4) easy to use, (5) clear options, and (6) clear to navigate. In addition, this study found that education, professional background, computer competency, and age had a significant impact on online banking adoption from Saudi females

Exploration of influential determinants for the adoption of business intelligence system in the textile and apparel industry

The textile and apparel industry is prone to digitization with business intelligence systems (BIS) and big data concepts to contribute the global sustainability. BIS, an impactful and leading technology, is being implemented in many industrial sectors but almost 80% of BIS fail to give expected results due to unknown reasons. Although many scholars put effort into finding the influential determinants for the BIS implementation, they neglect the BIS adoption context, especially in the textile and apparel industry. A purposive and proportionate choice of potential determinants in the context of adoption would contribute significantly to the success of BIS. Multi-stage research is employed to identify and prioritize the significant determinants. In the first stage, twenty-two semi-structured in-depth interviews are conducted with seventeen textile and apparel companies. Ten significant determinants emerged after thematic analysis of interview data. The determinants are sustainability, competitive pressure, market trends, compatibility, technology maturity, leadership commitment and support, satisfaction with existing systems, sustainable data quality and integrity, users' traits, and interpersonal communications that influence the adoption of BIS. In the second stage, the Best Worst Method (BWM) is used to calculate the weights for prioritizing the determinants based on experts' opinion. These weights are then used to evaluate and rank the determinants. The findings of this research show that the leadership commitment and support, sustainability, users' traits, and technology maturity, are the top-ranked determinants that influence the practitioners' choice to adopt the BIS in the textile and apparel industry. The results of this study enable the BIS stakeholders to holistically comprehend the significant determinants that would drive or impede the success of BIS projects in the sustainable textile and apparel industry

Artificial-Intelligence-Based Decision Making for Oral Potentially Malignant Disorder Diagnosis in Internet of Medical Things Environment

Oral cancer is considered one of the most common cancer types in several counties. Earlier-stage identification is essential for better prognosis, treatment, and survival. To enhance precision medicine, Internet of Medical Things (IoMT) and deep learning (DL) models can be developed for automated oral cancer classification to improve detection rate and decrease cancer-specific mortality. This article focuses on the design of an optimal Inception-Deep Convolution Neural Network for Oral Potentially Malignant Disorder Detection (OIDCNN-OPMDD) technique in the IoMT environment. The presented OIDCNN-OPMDD technique mainly concentrates on identifying and classifying oral cancer by using an IoMT device-based data collection process. In this study, the feature extraction and classification process are performed using the IDCNN model, which integrates the Inception module with DCNN. To enhance the classification performance of the IDCNN model, the moth flame optimization (MFO) technique can be employed. The experimental results of the OIDCNN-OPMDD technique are investigated, and the results are inspected under specific measures. The experimental outcome pointed out the enhanced performance of the OIDCNN-OPMDD model over other DL models

Dominating Topological Analysis and Comparison of the Cellular Neural Network

Graph theory is a discrete branch of mathematics for designing and predicting a network. Some topological invariants are mathematical tools for the analysis of connection properties of a particular network. The Cellular Neural Network (CNN) is a computer paradigm in the field of machine learning and computer science. In this article we have given a close expression to dominating invariants computed by the dominating degree for a cellular neural network. Moreover, we have also presented a 3D comparison between dominating invariants and classical degree-based indices to show that, in some cases, dominating invariants give a better correlation on the cellular neural network as compared to classical indices