HighTech and Innovation Journal
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Elbow-Hand Robotic Exoskeletons for Active and Passive Rehabilitation on Post-Stroke Patients: A Bioengineering Review
Full text linkThe clinical applications and benefits of the use of a Robotic Exoskeleton for Rehabilitation (RER) in the elbow and hand are described because a RER is a high-quality alternative capable of restoring compromised functions and neurorehabilitation at the same time in post-stroke patients. Passive rehabilitation (PR) is usually applied in the early stages of post-stroke recovery. The responsibility of assisting Physical Medicine and Rehabilitation (PM&R) doctors and the patient with passive exercises is a robotic system (RS); while active rehabilitation (AR) is applied regularly in late stages, it is like PR and its implications but requires the strength and support of the person to perform the exercises voluntarily. The objective of the present study is to collect, synthesize, and report relevant scientific studies related to the implementation of exoskeleton systems for elbow-hand rehabilitation. Various scientific literature on the topic was reviewed in the main biomedical databases using the population, intervention, comparison, results, and context (PICOC) criteria. This study presents the potential and describes a comprehensive and updated vision of the consequences and improvements obtained with the use of the RER and its advances. In conclusion, the usefulness and importance of a RER in various applied clinical practices have found numerous advantages, such as a better evaluation of spasticity, neuromotor recuperation, promoting neuroplasticity, and much more, which is of global relevance since this study gives us a greater understanding of the potential of these new perspectives to improve the rehabilitation of compromised functions in post-stroke cases with the use of RER. Doi: 10.28991/HIJ-2024-05-04-020 Full Text: PD
The Measurement of Blockchain Technology in Financial Reports in Commercial Banks
Full text linkThe objective of the present study is to measure the impact of blockchain technology on financial reports. The study utilizes a time series analysis covering eleven commercial banks listed on the Amman Stock Exchange from 2009 to 2019. Two key measures, namely other operating expenses and customer deposits are employed in the Return on Assets (ROA). The findings indicate that blockchain technology can be quantified by 0.038 of other operating expenses. However, there are no discernible indications of measuring blockchain technology through customer deposits. The study suggests that blockchain technology is a double-edged sword; when not utilized as required, it leads to increased expenses, and conversely, its effective exploitation can have cost-reducing effects. In other words, operational inefficiencies or heterogeneity are associated with elevated costs associated with implementing blockchain technology. Doi: 10.28991/HIJ-2024-05-02-014 Full Text: PD
Early Identification of Skin Cancer Using Region Growing Technique and a Deep Learning Algorithm
Full text linkSkin cancer, comprising both melanoma and non-melanoma forms, is a significant public health concern, constituting approximately 5.9% to 7.8% of annual cancer diagnoses. In Indonesia, the predominant form of the disease is basal cell carcinoma (65.5%), followed by squamous cell carcinoma (23%), and malignant melanoma (7.9%). Several studies have shown that early detection of its melanoma form is essential due to the heightened mortality risk. Therefore, this study aimed to assess the efficacy of the Region Growing + LSTM algorithm in improving detection accuracy compared to LSTM. The novelty of the study lay in addressing the inefficiencies of manual dermoscopy image examination and introducing a novel combination of Region Growing segmentation and Deep Learning LSTM for enhanced detection precision. The results showed that the proposed model could identify segmented areas before classification and achieved 96.62% accuracy, outperforming LSTM's 84%. However, LSTM exhibited shorter training and prediction times (39.3 seconds and 3.2 seconds, respectively) compared to Region Growing + LSTM (17 minutes and 2 seconds for training, 3 minutes and 49 seconds for prediction). Although Region Growing + LSTM offered superior accuracy, it required more time than LSTM, showing potential trade-offs between accuracy and efficiency in skin cancer image detection. Doi: 10.28991/HIJ-2024-05-03-07 Full Text: PD
Content Automation in Marketing Research: A Bibliometric Analysis using VOSviewer
Full text linkObjectives: This study conducted a comprehensive assessment of content automation in marketing research using bibliometric analysis spanning 20 years from 2004 to 2024. The primary influential factors, topics, and areas of research were analyzed to determine future research paths. Methods/Analysis: Sample selection and data collection were conducted using the Scopus database. The initial dataset was adjusted by applying certain inclusion and exclusion criteria. The final dataset consisting of 149 articles in the RIS format was loaded into the VOSviewer program to perform a bibliometric analysis. Findings: The results revealed key findings, including the highest citation-counting papers, the most common research format, the research field, the year of publication, collaboration countries, the journal, prominent authors, popular themes, areas for further investigation, and the intellectual framework of current research on the topic. Novelty/ Improvement:Four areas were identified for future research: marketing automation as digital commerce content marketing, artificial intelligence for digital marketing transformation, automation and optimization for personalized advertising content, and automatic knowledge discovery. The temporal development of these issues was also examined to offer valuable insights into the changing focus of academic interest over time and establish a foundation for future investigations. Doi: 10.28991/HIJ-2024-05-03-019 Full Text: PD
Permissible Extrapolation Justification of the Multiplicative Multifactorial Model and Its Application to the White Soot Production Technology
Full text linkThe solution to a specific technological problem is combined with the methodological development of a nonlinear multifactorial relationship to justify the boundaries of its extrapolation beyond the experimental range used. White soot was produced through two-stage carbonization of a silicate solution (composition, g/l: Na2O = 126.5, SiO2 = 107.7, Al2O3 = 3.1), obtained after processing waste tailings with carbon dioxide in a recirculation system. The influence of the deposition duration, temperature, and final pH value of the pulp on the formation of the specific surface area (Ssp, m2/g) of white soot was studied. The specific surface area was calculated from the average diameter of the white soot particles measured using an electron microscope. A multifactorial experiment was designed, and the experimental results were processed using a probabilistic deterministic method for experiment design (PDED) to obtain a nonlinear multiplicative combined model. A new interpretation of the subordination of the nonlinear multiple correlation coefficient R and the R2 value was given as relating to the structural and adaptive components of complex self-organizing systems. This determines their use for assessing the ratio of the basic R and extrapolated R2ranges of variation for each factor and any combinations thereof and multifactorial dependence in general. The results are presented in the form of multifactor tabular nomograms measured by the number of multifactor cells in localized areas of optimal sets that allow isolation by one or another combination of factors. The technological object of extrapolation of the ‘white soot' production is linked to the solution of emerging methodological problems and illustrates the accessibility of the engineering application of the proposed method for combining nonlinear and linear approaches to mathematical experimental design. Doi: 10.28991/HIJ-2024-05-03-08 Full Text: PD
A UAV Based Concrete Crack Detection and Segmentation Using 2-Stage Convolutional Network with Transfer Learning
Full text linkThis study explores a non-destructive testing (NDT) method for crack detection using a two-stage convolutional neural network (CNN) model, incorporating a combination of AlexNet and YOLO models through transfer learning. Crack detection is pivotal for assessing structural integrity and ensuring timely maintenance interventions. The developed model was rigorously tested in simulated environments and through physical experimentations with the use of a UAV to evaluate its effectiveness. A 2-stage model, based on AlexNet and YOLO, was developed for crack classification and segmentation. The developed model leveraged transfer learning to address limitations from traditional CNN models. A known dataset was used to evaluate the developed model, benchmarking it against other models. The classification network achieved an accuracy rate exceeding 90%, while the segmentation network successfully identified and delineated cracks in 85.71% of the images. Finally, the developed model was deployed using a UAV to perform crack detection and segmentation in a controlled environment. These results underscore the model's proficiency in both detecting and segmenting structural cracks, highlighting its potential as a reliable tool for enhancing the maintenance and safety of architectural structures. Doi: 10.28991/HIJ-2024-05-03-010 Full Text: PD
A Method for Assessing Urban Industrial Ecological Efficiency Using SBM-GML Model with Tax Reduction
Full text linkThe industrial development of cities promotes social and economic development, but it also affects cities' ecological environments. To balance the relationship between the two, the country introduces corresponding tax reduction policies as an effective means of regulation. Therefore, to explore tax and fee reduction policies' specific impact on urban industrial ecological efficiency, the proposed text clustering model was first used in this experiment to cluster the tax and fee reduction policies issued by the government. Subsequently, the Slack-Based Measure-Global Malmquist Lunberger was constructed to measure urban industrial development's ecological efficiency. These experiments confirmed that policy text clustering models had different clustering accuracy on different datasets, with clustering accuracy reaching up to 80.95%, 87.13%, and 94.08% at iterations of 200, 500, and 1000. The regression coefficients for the main variables obtained from the clustering policy, including overall tax reduction and fee reduction, circulation tax reduction, income tax reduction, social expense reduction, and technological innovation tax reduction, were 0.117, 0.105, 0.269, 0.112, and 0.115, respectively. This indicated that these tax and fee reduction measures affected industrial ecological efficiency positively. Therefore, the proposed method can effectively cluster policy texts and measure the industrial ecological efficiency of cities, which has practical feasibility. This provides an effective path for promoting industry and the ecological environment's balanced development. Doi: 10.28991/HIJ-2024-05-04-02 Full Text: PD
Learner Assessment System in e-Learning with OBE Approach: Activity Performance, Ability Level and Recommendation
Full text linkE-learning can lead learners to achieve learning outcomes if it is designed based on several principles. One is applying assessments that motivate and inform ability levels. In Outcome-based Education (OBE), assessment is integral to the system. However, e-learning has limitations in providing assessment instruments according to needs, such as assessing complex and detailed aspects and accommodating a variety of numerical and linguistic assessment data. Moreover, the presence and involvement of learners affect their performance and learning outcomes. This study proposes a learner assessment system in e-learning with the OBE approach, including learning design, activity performance analysis, ability level determination, and recommendations. This system adds the e-rubric to e-learning to overcome instrument limitations and accommodate comprehensive assessments. Various numerical and linguistic assessment data are unified using 2-tuple fuzzy linguistics, producing ability levels as two tuples. Performance analysis was based on event log data using descriptive statistical technique and alignment-based conformance checking, from frequency, time, and sequence of activity objects, resulting in five activity performance variables. The performance value of each variable is converted into High, Medium, or Low levels. The ability and performance levels are processed using rule-based methods to produce recommendations for learning stages and activity performance directions. The results of this research can be used as input for academic stakeholders and online learning providers and potentially be applied to the advancement of e-learning in higher education. Doi: 10.28991/HIJ-2024-05-03-03 Full Text: PD
Effect of Artificial Intelligence (AI) on Financial Decision-Making: Mediating Role of Financial Technologies (Fin-Tech)
Full text linkThe main objective of the current study is to shed light on the mediating effect of financial technology (Fin-Tech) on the relationship between artificial intelligence (encompassing natural language processing (NLP), machine learning algorithms, computer vision, predictive analytics, robotic process automation (RPA), blockchain technology, and deep learning) and financial decision-making from the perspective of financial managers within Jordan's commercial banking sector. Realizing this objective required the use of quantitative methodology. A questionnaire was self-administered by 86 financial managers in the Jordanian banking sector. Primary data was analyzed using AMOS. Results of analysis confirmed that FinTech plays a significant mediating role between AI applications and financial decision-making. Machine learning was identified as the most impactful AI technique, facilitating more informed decisions through advanced data analysis and pattern recognition beyond the scope of traditional analysis methods. The novelty of current research is in the fact that it offers valuable insights into the intersection of AI and Fin-Tech within the Jordanian financial sector. It contributes to the understanding of how advanced AI techniques can enhance financial decision-making, emphasizing the importance of multidisciplinary expertise in the development of AI-driven financial systems. The findings have significant implications for both theoretical understanding and practical application in the finance industry. Doi: 10.28991/HIJ-2024-05-03-015 Full Text: PD
Exploring Managers' Skills Affecting Dynamic-Innovative Capabilities and Performance in New Normal Era
Full text linkThe human resources department, as a dynamic mechanism in the hotel business, is a supporter and a manager who manages the corporation to grow by planning, supervising, and assuring the expected performance leads to desirable outcomes. The situation of spread of the COVID-19 virus has resulted in businesses and labor departments having to adapt to survive by upgrading existing knowledge and adding new skills. Therefore, this research aims to describe components and models of necessary skills development for performance affecting dynamic capabilities and performance in a new normal era for human resources managers of five-star hotels in Phuket Province, which are crucial components in an increased corporation's sustainability and performance in terms of personnel efficiency, assets, funds, and information. This research is quantitative, and research data was collected from a total of 384 human resource managers of five-star hotels. There was a mutual discussion of factor analysis and structural equation results with three human resources managers who have been successful for not less than seven years in their work. The components consisted of systematic consideration through the following causes: necessary skills; professional skills, work skills, and emotional skills, mediator variables; dynamic capabilities, and organizational performance. This research also discussed five guidelines for developing the necessary skills for performance. As various factors have affected the performance in the new normal era, the human resources executives of five-star hotels in Phuket province should apply them and consider them together with their business plans for setting the strategic plan of organizational management, management, administrative, and human resources development. Doi: 10.28991/HIJ-2023-04-01-03 Full Text: PD