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We summarize the potential impact that the European Union's new General Data Protection Regulation will have on the routine use of machine‐learning algorithms. Slated to take effect as law across the European Union in 2018, it will place restrictions on automated individual decision making (that is, algorithms that make decisions based on user‐level predictors) that “significantly affect” users. When put into practice, the law may also effectively create a right to explanation, whereby a user can ask for an explanation of an algorithmic decision that significantly affects them. We argue that while this law may pose large challenges for industry, it highlights opportunities for computer scientists to take the lead in designing algorithms and evaluation frameworks that avoid discrimination and enable explanation.

Shikako et al analysis highlights that needs of persons with disability (PwD) were often overlooked, with policies primarily focused on general population health measures rather than specific accommodations for PwD. This commentary suggests adopting universal design principles in policy development to ensure inclusivity and advocate for maintaining services essential for PwD even during crises. It emphasizes the importance of involving PwD in policy-making processes and enhancing data collection for better policy analysis and concludes with recommendations for creating more inclusive policies, stressing the need for international collaboration and the integration of PwD needs into all policy levels. Keywords: Universal Design, Inclusion, Text Mining

This article presents a study using ResNet-50, GRU, and transfer learning to construct a marketing decision-making model and predict consumer behavior. Deep learning algorithms address the scale and complexity of consumer data in the information age. Traditional methods may not capture patterns effectively, while deep learning excels at extracting features from large datasets. The research aims to leverage deep learning to build a marketing decision-making model and predict consumer behavior. ResNet-50 analyzes consumer data, extracting visual features for marketing decisions. GRU model temporal dynamics, capturing elements like purchase sequences. Transfer learning improves performance with limited data by using pre-trained models. By comparing the model predictions with ground truth data, the performance of the models can be assessed and their effectiveness in capturing consumer behavior and making accurate predictions can be measured. This research contributes to marketing decision-making. Deep learning helps understand consumer behavior, formulate personalized strategies, and improve promotion and sales. It introduces new approaches to academic marketing research, fostering collaboration between academia and industry.

In order to better promote the healthy and long-term development of enterprise procurement management process, under the background of e-commerce environment, Suppliers-Inputs-Process-Outputs-Customers (SIPOC) model, deep learning and related theories of enterprise procurement management are expounded and proposed. Then, D electric power enterprise is studied as samples. After understanding the current situation of procurement management of the enterprise, there are a series of problems in the enterprise, such as complex process, and no correlation between procurement management process and overall strategic planning. Finally, through the analysis of the early warning indicators of the enterprise by the deep learning algorithm, the procurement management process has caused certain risks to the financial management level of the enterprise, and the procurement management process of the enterprise needs to be adjusted. The material record and consumption scheme of the enterprise is optimized by using the SIPOC organizational system model.

A mobile application (App) is an application designed to run on a smartphone, tablet, or other mobile device. With the continuous change of mobile payment applications in smart phones and the support of the banking system, the global mobile payment population is increasing. This study examines the behaviors of Taiwan mobile payment and apps users, a total of 1,176 valid questionnaire data is divided into six sections with 29 items for a database design. This study develops a data mining approach, including clustering analysis and association rules, based on a relational database. Thus, this study shows that mobile payment not only can provide payment service but is also a critical mobile application platform for online business. Finally, we show that as users of mobile payment and apps gain additional demand and consumption ability, online operators can gradually put together mobile payment business models to enable future electronic commerce online recommendations.

The stock market is an aggregation of investor sentiment that affects daily changes in stock prices. Investor sentiment remained a mystery and challenge over time, inviting researchers to comprehend the market trends. The entry of behavioral scientists in and around the 1980s brought in the market trading's human dimensions. Shortly after that, due to the digitization of exchanges, the mix of traders changed as institutional traders started using algorithmic trading (AT) on computers. Nevertheless, the effects of investor sentiment did not disappear and continued to intrigue market researchers. Though market sentiment plays a significant role in timing investment decisions, classical finance models largely ignored the role of investor sentiment in asset pricing. For knowing if the market price is value-driven, the investor would isolate components of irrationality from the price, as reflected in the sentiment. Investor sentiment is an expression of irrational expectations of a stock's risk-return profile that is not justified by available information. In this context, the paper aims to predict the next-day trend in the index prices for the centralized Indian National Stock Exchange (NSE) deploying machine learning algorithms like support vector machine, random forest, gradient boosting, and deep neural networks. The training set is historical NSE closing price data from June 1st, 2013-June 30th, 2020. Additionally, the authors factor technical indicators like moving average (MA), moving average convergence-divergence (MACD), K (%) oscillator and corresponding three days moving average D (%), relative strength indicator (RSI) value, and the LW (R%) indicator for the same period. The predictive power of deep neural networks over other machine learning techniques is established in the paper, demonstrating the future scope of deep learning in multi-parameter time series prediction.

The National Nuclear Energy Agency of Indonesia (BATAN) taxonomy is a nuclear competence field organized into six categories. The Polytechnic Institute of Nuclear Technology, as an institution of nuclear education, faces a challenge in organizing student publications according to the fields in the BATAN taxonomy, especially in the library. The goal of this research is to determine the most efficient automatic document classification model using text mining to categorize student final project documents in Indonesian and monitor the development of the nuclear field in each category. The kNN algorithm is used to classify documents and identify the best model by comparing Cosine Similarity, Correlation Similarity, and Dice Similarity, along with vector creation binary term occurrence and TF-IDF. A total of 99 documents labeled as reference data were obtained from the BATAN repository, and 536 unlabeled final project documents were prepared for prediction. In this study, several text mining approaches such as stem, stop words filter, n-grams, and filter by length were utilized. The number of k is 4, with Cosine-binary being the best model with an accuracy value of 97 percent, and kNN works optimally when working with binary term occurrence in Indonesian language documents when compared to TF-IDF. Engineering of Nuclear Devices and Facilities is the most popular field among students, while Management is the least preferred. However, Isotopes and Radiation are the most prominent fields in Nuclear Technochemistry. Text mining can assist librarians in grouping documents based on specific criteria. There is also the possibility of observing the evolution of each existing category based on the increase of documents and the application of similar methods in various circumstances. Because of the curriculum and courses given, the growth of each discipline of nuclear science in the study program is different and varied.

Machine learning techniques are used extensively for automating various cybersecurity tasks. Most of these techniques use supervised learning algorithms that rely on training the algorithm to classify incoming data into categories, using data encountered in the relevant domain. A critical vulnerability of these algorithms is that they are susceptible to adversarial attacks by which a malicious entity called an adversary deliberately alters the training data to misguide the learning algorithm into making classification errors. Adversarial attacks could render the learning algorithm unsuitable for use and leave critical systems vulnerable to cybersecurity attacks. This article provides a detailed survey of the state‐of‐the‐art techniques that are used to make a machine learning algorithm robust against adversarial attacks by using the computational framework of game theory. We also discuss open problems and challenges and possible directions for further research that would make deep machine learning–based systems more robust and reliable for cybersecurity tasks.

This study examines how the Donggyeong Daejeon (東經大全), the principal scripture of early Donghak, receives and theologically reconfigures the conceptual lexicon of Confucian classics through text mining-based analysis. Drawing on the classical Chinese texts of the Four Books and the Donggyeong Daejeon, and employing computational techniques such as keyword frequency, keyword-in-context (KWIC), and co-occurrence mapping, the study identifies structural parallels and semantic shifts across the two corpora. These patterns are then interpreted hermeneutically to assess how early Donghak appropriates, repurposes, and theologically transforms inherited Confucian categories. Findings suggest that while the Donggyeong Daejeon retains key Confucian terms, it situates them within a distinct theological framework. The Confucian triad of human being, the Way, and Heaven (人–道–天), for example, is recast in Donghak as “Heaven’s heart is the human-heart” (天心卽人心), a theological affirmation of the human as the locus of Heaven’s immanence. Similarly, the Confucian virtue of sincerity (誠) is reinterpreted through the lens of faith (信), transforming it from a metaphysical ideal into a performative mode of spiritual judgment. Most notably, the Confucian dualism of li (理) and qi (氣) is overcome through the theology of “ultimate energy” (至氣), a divine substance that animates and unifies all beings. By combining quantitative text analysis with interpretive discussion, this study presents Donghak not as a rhetorical appropriation of Confucian discourse, but as a conceptual innovation rooted in the resemanticization of its inherited language. This methodology offers a new model for tracking doctrinal transformation in East Asian religious texts and contributes to broader discussions on intertextual borrowing, and the semantic evolution of classical traditions.