The rapid digitalization in recent years has opened up many technological possibilities, gradually transforming various sectors and society as a whole. This digital shift has enabled advancements in a number of fields, leading to improved resource efficiency, systems and processes. The Internet of Things (IoT) refers to a system of interconnected devices that share information that exchange information with one another via the internet. IoT devices are now everywhere, found in applications ranging from unmanned aerial vehicles to smart home environments, from the Industrial Internet of Things to the Internet of Medical Things. The core concept of IoT revolves around establishing a seamless and intelligent communication ecosystem, facilitating interactions between devices over the internet. This is anticipated to create new opportunities for enhancing services in various societal sectors, such as transportation, farming and smart cities. However, IoT-based networks face limitations and challenges that hinder the realization of their full potential. This paper outlines these challenges and proposes solutions, emphasizing the importance of collaboration and innovation. The paper also anticipates future trends in IoT, particularly the integration of 5G connectivity, cloud computing and AI, and identifies areas for future research to address current challenges and explore new applications.

Eco-friendly business practices range from energy efficiency to sustainable management; therefore, the implementation of these practices not only has the potential to mitigate environmental impacts, but also to improve the long-term competitiveness and sustainability of companies. In this study we analyze eco-friendly business practices and their impact on environmental sustainability in companies in a region of the Peruvian Amazon. A quantitative, non-experimental approach was considered, with an explanatory design; for which, a sample size of 200 companies was considered, which were randomly selected, to which two instruments were applied, the first with 7 indicators and the second with 3, valid, with an alpha of 0.720 and 0.670 respectively, assessing structural equation modeling. The results show that eco-friendly business practices are oriented towards sustainability (C.R. = 19.280), as well as preventive and eco-efficient practices (C.R. = 5.023, C.R.= 14.185); these findings can serve as a basis for the creation or improvement of public policies that promote eco-friendly business practices, encouraging companies to adopt measures that favor environmental sustainability. Therefore, eco-friendly business practices have a positive impact on environmental sustainability in companies in a region of the Peruvian Amazon; in addition, preventive practices and eco-efficient practices have a positive impact on environmental sustainability in companies in a region of the Peruvian Amazon.

The primary objective of this study was to examine the antecedents of digital transformation (DT) within multinational corporations (MNCs) in Malaysia, from the perspectives of corporate managers. Amidst limited research on DT within the MNC context, this paper examines the key drivers of DT in Malaysian MNCs. A quantitative method using non-probability sampling was used to gather the required data via the distribution of questionnaires among the MNCs’ managers. To evaluate the underlying theoretical model based on the collected data, we chose SmartPLS as the preferred method. Findings revealed that business value, digital leadership, inter-functional coordination and decision-making quality were significant drivers of DT, while DT exerted a positive influence on business performance. However, collaborative innovation did not have a significant relationship with digital transformation adoption in MNCs. The findings offer novel insights for both academics and international corporate managers, enhancing their understanding of the drivers behind DT adoption from the perspective of managers within MNCs in Malaysia.

This paper presents RIFD-DLT, an advanced near-lossless image compression algorithm that combines Delta Encoding with the original rounding the intensity followed by division (RIFD) method. The RIFD method first minimizes the image intensities, which makes the next compression stages more efficient. Subsequently, Delta Encoding subtracts neighboring rows in each of the image's three-color matrices, using the proximity of pixel values in adjacent rows to further reduce the image intensity. Extensive investigations show that RIFD-DLT outperforms the state-of-the-art algorithms and benchmarks with respect to compression ratio and processing time. More specifically, RIFD-DLT compresses data from 11520 KBs to 2131 KBs with an 81.93% reduction and a 43.7% improvement over original RIFD-Huffman when compressing Kodak Image set. When comparing the RIFD-DLT with LICA algorithm, the total file size is reduced by 71.2%, representing a 10.73% improvement for RIFD-DLT. Also, RIFD-DLT shows notable speed gains over RIFD-Huffman, requiring only 34.62 seconds to compress and decompress all images from three datasets (Waterloo, Kodak and EPFL), as compared to 50.99 seconds for RIFD-Huffman. As for the image quality, the proposed algorithm achieved an average PSNR values of 58.51 dB, 51.3 dB, and 52.22 dB for the EPFL, Kodak, and Waterloo image sets, respectively, demonstrate the excellent image quality that persists after decompression, with a minimal distortion that is imperceptible to the human visual system and identically to the RIFD-Huffman PSNR. These findings show that, while preserving excellent image quality, RIFD-DLT provides an incredibly efficient and fast method of image compression.

This research aims to examine how data privacy concerns influence DOI in Digital Marketing and investigates how artificial intelligence trust mechanism integration modulates that effect. Primary data was collected by accessing the structured questionnaire targeting ZAIN Telecom employees as the chosen study site. The PLS-SEM technique was used in this research to analyze data privacy, digital marketing effectiveness, and AI-driven trust constructs. According to this study, digital marketing processes significantly focus on data privacy and, ultimately, AI-driven trust. Further, powerful AI trust systems will be required to reinforce data privacy and systematically configure and drive digital marketing enterprises. Thus, such systems could allow firms a sustainable competitive advantage in this new Battle for data security age. The findings discovered that data privacy concerns significantly impede AI-driven trust, diminishing digital marketing effectiveness. Therefore, they contribute to the literature by offering empirical support for AI-powered trust mediating between factors within a model and offering practical implications and extensions of the theoretical models. This literature helps industry practitioners and policymakers build trust in AI interventions to alleviate data privacy risks and improve support for digital marketing strategies.

The accurate and efficient classification of leukemia images is crucial for early diagnosis and effective treatment planning. Traditional methods often face challenges in handling the complexity and variability of medical images. To address these challenges, we propose a novel approach that leverages the Gray Level Co-occurrence Matrix (GLCM) and statistical feature-based segmentation techniques. In this paper, we present a comprehensive framework for the automated classification of leukemia images using advanced image processing techniques. The methodology involves six key stages: input of leukemia images, preprocessing to enhance image quality, segmentation to isolate relevant features, feature extraction using texture analysis, classification using multiple distance metrics Euclidean, Manhattan, Canberra, and Chebyshev, and performance evaluation. Our results demonstrate significant improvements in classification accuracy, sensitivity, specificity, and error rates across various metrics and feature sets. For instance, using the Chebyshev distance, we achieved an average accuracy of 82.69%, sensitivity of 85.95%, and specificity of 82.77%. The Canberra distance provided optimal performance with 65 features, yielding an accuracy of 85.18%, sensitivity of 86.39%, and specificity of 86.31%. These findings underscore the efficacy of our approach in distinguishing between healthy and leukemic cells, thereby contributing to early diagnosis and effective treatment planning for leukemia.

The Kingdom of Saudi Arabia has witnessed a significant surge in online shopping in recent years, fueled by factors like growing internet penetration, smartphone adoption, and government initiatives supporting e-commerce growth. This rise in online activity has led to a corresponding increase in the utilization of online courier services, playing a crucial role in ensuring timely and efficient delivery of goods In this context, understanding public perception of online courier services becomes crucial for businesses to improve their offerings, address customer concerns, and maintain a competitive edge. Social media platforms have emerged as a valuable source of customer feedback and user-generated content, offering insights into customer experiences and opinions. This paper presents a sentiment analysis on online couriers in Saudi Arabia using natural language processing techniques combined with Decision Tree and Support Vector Machine (SVM) classifiers of machine learning. A dataset on customers’ sentiments was created by a crawling process from X social media. Both classifiers perform well, with Decision Tree classifier performs slightly better on accuracy, i.e. 95.01% compared to 93.60% of the Support Vector Machine. Other metrics support the robustness of the classification.

Augmented reality (AR) has garnered considerable interest for its potential to motivate engagement and advance customer-brand interactions. This study explored the impact of reality congruence (RC) of AR-menus on brand information sharing (BIS) in fast-food restaurants, particularly among Generation Z (Gen Z), as well as the mediating effects of usefulness of AR menu (UAR) and brand positivity (BP) on this relationship. Media Richness Theory (MRT) was employed as the theoretical umbrella for developing the study model. To validate the research model, we employed structural equation modeling (PLS-SEM) with a sample of 209 respondents. The results demonstrate that reality congruence of AR menus is a relevant predictor of Gen Z members’ behavior in sharing information about the brand. Furthermore, this relationship was mediated by the UAR and BP. The findings also demonstrated that UAR and BP had a sequential mediating effect on the relationship between the RC of AR-menu and BIS. This revolutionary study revealed that RC of AR-menu in restaurants fosters positive behaviors in fast food settings. By highlighting AR's potential to create engaging dining experiences for Gen Z members, this study offers valuable insights for service businesses, marketing managers, and the hospitality industry. Addressing this gap in existing research emphasizes the importance of adopting innovative technologies to enhance Gen Z's customer experience and engagement in the restaurant industry.

This study aimed to determine the impact of digital transformation (DT) and digital accounting (DA) on the financial reports quality (FRQ) in companies listed in the Saudi capital market. Data were collected using a questionnaire distributed via email. The research sample consisted of 116 individuals, including accountants and executive directors in companies listed. The study adopted the smart PLS method to test hypotheses. The study found that there is an incomplete positive relationship between digital transformation and the quality of financial reports through the positive impact on the characteristics of relevance and understandability, as well as the existence of a complete positive relationship between digital accounting and the FRQ through the impact on all characteristics of accounting information.

Sentiment analysis of students’ feedback using machine learning algorithms has emerged as a valuable tool for understanding students’ sentiments and improving educational outcomes. Currently, existing systems use frequency-based methods for feature selection (e.g., Term Frequency-Inverse Document Frequency (TF-IDF) and Bag of Words (BoW)) not to capture the subtleties of emotions expressed in student feedback and do not provide insights into the specific concerns of students via topics or themes. In this study, we propose the Student Sentiment from Feedback (SSF) framework, which includes four main procedures: pre-processing, feature selection, classification, and theme finding. The SSF framework classifies student sentiments and subsequently groups feedback into themes using semantic networks based on word co-occurrence. Our innovative feature selection approach combines TF-IDF with sentiment-based features derived from SentiWordNet and intensifiers, creating a robust feature vector that enhances the dataset’s richness and improves classification accuracy and robustness. In the experiments, we utilize a public dataset from Kaggle, applying our proposed method and various machine learning models (e.g., k-nearest neighbor, decision tree, random forest, multilayer perceptron, support vector machine, gradient boosting, and extreme gradient boosting). The experimental results show that our concatenated features achieve the highest accuracy across all machine learning models (greater than 0.82). Our study demonstrates the efficacy of this hybrid feature selection method, contributing to better understanding and decision-making in educational settings.