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This paper aims to classify the types of stress experienced by women undergoing infertility treatment in Japan and to clarify the causes of such stress. To achieve this, tweets posted by infertility patients were used to identify the causes of stress through a learning model, followed by dependency analysis. A system is proposed to extract causal relations using dependency analysis, where the dependents are the causes and the leaders are the effects. The study focuses on the stress experienced by infertility patients and limits the analysis to "family issues" and "work." Consequently, the incentives for the causes were clarified. [ABSTRACT FROM AUTHOR]

This paper explores the characteristics of a two-step procedure (dimension reduction and function approximation) in discrete choice modeling with high-dimension data. This study proposes the SS-GAM procedure which is an extension of the Super Sparse Principal Component Analysis (SSPCA) where the results are further processed with the Generalized Additive Model (GAM) in a classification problem. Moreover, the Orthogonal Sparse Principal Component Analysis with GAM (OS-GAM) is also proposed. For baseline comparison, the General Adaptive Sparse-PCA with GAM (GAS-GAM) is considered in this paper. The performance of these three sparse PCA methods are investigated with varied underlying correlation structure. In the simulation study, it is demonstrated that with varied degree of dimensionality, and levels of correlation structure, SS-GAM performed better compared to OS-GAM and GAS-GAM in terms of its predictive rate, on the average. It was observed that the OS-GAM performed best when data exhibits low correlation structure. However, with high correlation structure, OS-GAM and GAS-GAM obtained comparable result. Moreover, in terms of computational time, OS-GAM seems to be not affected by the increase of feature dimension. [ABSTRACT FROM AUTHOR]

The sustainability issue in the transportation sector brings the electric vehicle (EV) as a new promising solution for reducing carbon emissions. However, the EV adoption faces issues regarding range, recharging time, and high initial investment. To enhance the adoption, supporting infrastructures should be planned. Therefore, forecasting the growth of EV adoption becomes important to help industries and government in strategic decision making. This paper provides a literature review about forecasting methods in EV ownership, which includes studies from 2011 to March 2023. This will contribute to highlight the current methods and stimulating more developments of forecasting methods related to EV ownership. [ABSTRACT FROM AUTHOR]

As start-up companies may leverage information spreading through Instagram's hashtag-setting, algorithms in influence maximization can be encoded. The objective is to find a maximum impact which can activate users in spreading advertisement marketing campaign through social network. This paper contributes an influence analysis on Instagram and reproduces it into a hashtag network by transforming postings with hashtag interdependence. In finding optimal paths such network structure with influence probabilities, we employ Floyd-Warshall algorithm under uncertainty. The technique is simulated on Indonesian FnB unicorn company and demonstrated in numerical example. The result shows that the hashtag #promokopi may impact the most. [ABSTRACT FROM AUTHOR]

We introduce the combination of two techniques: Sparse Retrieval and Dense Retrieval, while experimenting with different training approaches to find the optimal method for the Vietnamese Legal Text Retrieval task. Moreover, the Question Answering task was only built on the open domain of UIT-ViQuAD but shown promising results on the in-domain legal dataset. Finally, we also mentioned the data augmentation of legal documents up to 3GB to train the Phobert language model, improve this backbone with Condenser, Cocondenser in this paper. Furthermore, these techniques can be utilized for other information retrieval assignments in languages with limited resources. [ABSTRACT FROM AUTHOR]

Hate Speech already been phenomenal expansion over the past decade. The paper proposed a new model that combines advanced CNN and Bidirectional Encoder Representations from Transformers (BERT) context embedding to predict hate speech in social media. This research trained contextual embedding on the datasets and used the learned information to identify objectionable language and hate speech in text. The paper evaluated supervised machine learning classifiers for bigoted and offensive content on Twitter using two datasets and found that advanced CNN context embeddings produced superior results. This research generated optimistic outcomes, which achieves 73% F1-score for Davidson dataset and 56% F1-score for TRAC-1 dataset [ABSTRACT FROM AUTHOR]

In this paper we analyze the problem of data-driven university management. We propose a concept of an intelligent strategic decision support system (ISDSS) to ensure that informed decisions are made in the management of higher education institutions. We show that the underlying task of various decision support tasks in university management is the analysis and forecasting of scientific and technological trends. We propose an approach to solving this task which includes determining promising emerging and existing technological areas, forecasting the further development of each of the selected areas, generating possible scenarios of their development and preparing suggestions for decision makers. [ABSTRACT FROM AUTHOR]

Big Data wave has led to a rapid increase in the amount of data being collected by organizations. While the accuracy and reliability of prediction models are often prioritized, the quality of the collected data is frequently overlooked. Poor data quality can result in the common problem of 'garbage in, garbage out'. Traditional measures of data quality, such as accuracy, consistency, completeness, and timeliness, are no longer adequate in the era of Big Data. Therefore, this paper proposes a taxonomy of data quality dimensions specifically for Big Data, addressing emerging challenges by formulating 20 dimensions and categorizing them into four distinct categories. [ABSTRACT FROM AUTHOR]

The paper discusses the possibility of increasing the effectiveness of strategic management of research and development activities in higher education institutions. To achieve this goal, the authors proposed an algorithm for selecting competencies, which allows for the distribution of university employees and students among research and development teams in accordance with their individual competencies for carrying out research and development work. The paper covers the problem statement of competence selection, an overview of natural language processing methods for algorithm development, problematic areas of the algorithm, and prospects for its implementation in university management. [ABSTRACT FROM AUTHOR]

Determination of macroeconomic policies in real-time requires assessing the correct information regarding current economic conditions. This statement spurred researchers to develop methods involving high-frequency data for risk analysis. This paper extends the quarterly growth-at-risk (GaR) approach by involving a machine-learning approach based on the Mixed-Frequency Data Sampling Quantile Regression Neural Network (MIDAS-QRNN) model. This paper shows that the MIDAS-QRNN model has the best prediction accuracy and can show good PDB nowcasting. The monthly financial GaR can detect unusual economic growth movements during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The license plate recognition system currently in use is susceptible to interference from the external environment and performs poorly in low-light conditions. This paper presents a solution for license plate recognition under a low-light environment. We adopted URetinex-Net methods that unfold an optimization issue into a learnable network to decompose a low illumination image into reflectance and illumination layers. We also adopted TRBA, an end-to-end recognition method involving no character segmentation. The experimental results show that the accuracy of the night environment of the proposed method is 80.11% increased by 5.11% compared to without the low light image enhancement method. [ABSTRACT FROM AUTHOR]

Indonesian cuisine, especially Bakso, is one of the trendy foods that many travelers try to consume. Among the variations of Bakso, Bakso Malang is one of the most popular variants that is much talked about in social media. The present study analyzes the sentiment text mining of Bakso Malang business on post covid period from a natural language processing (NLP) social perspective. The data was taken from scraping to social media by "bakso malang" keywords. A total of 331 lexicon-filtering Tweets were gathered in the middle of March 2023. The corpus data were analyzed by valance aware dictionary and sentiment reasoner (Vader) sentiment analysis. K-means conducted the cluster of expressions. The result shows the variation of sentiments expressed in social media by anger, disgust, fear, joy, sadness, and surprise with 1.21%, 1.21%, 4.83%, 32.33%, 3.02%, and 57.40%, respectively. Many contents were discussed with keywords of "malang", "bakso", "enak", "makan", "foodfess2", "mau", and "pengen". Behavioural insight shows joy expression when discussing Bakso Malang. Further insights and recommendations are discussed in the paper. [ABSTRACT FROM AUTHOR]

Planning and resource management are important aspects of a company's operational sustainability. With good management, companies can achieve their targets while minimizing operational costs. The same goes for hospitals. Various challenges related to resources are experienced by hospitals, such as scheduling nurses, patient surgeries, and patient appointments. Therefore, this paper aim at optimizing patient admission scheduling in order to improve hospital resource efficiency. To be more specific, patient admission scheduling, also known as the Patient Admission Scheduling Problem (PASP), is a scheduling problem that considers patient preferences and needs, bed availability, resource efficiency, and utilization. This problem is highly relevant, especially for large hospitals. The number of rooms, specialists, and facilities makes manual scheduling extremely difficult. It is due to the varied preferences, needs, and lengths of stay for patients in hospital. Various methods, both heuristic and exact, have been proposed, including the use of integer programming methods. However, for a large search space, this method requires a very long computational time. To address the PASP, this study applies the Variable Neighborhood Search (VNS) algorithm and random selection as optimization algorithms. The method was chosen because it has been proven effective to solve some combinatorial optimization problems in prior studies. Seven types of neighborhoods are implemented to find the best combinations in optimizing the PASP. The results show that the VNS algorithm outperforms the random selection algorithm, as it is able to generate 5 out of 7 solutions that are better, reducing penalties by 27.84% to 55.29%. The expected impact of this study is to increase the hospital patient satisfaction whereas in the same time minimize the operational cost. [ABSTRACT FROM AUTHOR]

This paper studies lessons learned from Covid-19 disaster management in Malaysia using machine learning techniques. First, we crawl Twitter data related to 'covid' with geo-location bounding-box. Then we contribute to propose LDA topics generated on citizen perception containing negative sentiment towards government response; hence, we represent the data using VOSviewer and D3.js to emphasize topic modeling with respect to timestamp due to pattern analysis. As results, LDA-based topic visualization may recognize the accounts' pattern that are assumed as the pillars of disaster management in Malaysia. This study gains insights from political science field. Implications of the results are also discussed. [ABSTRACT FROM AUTHOR]

The rapid development of information technology has brought numerous benefits to society, but it has also led to increased security vulnerabilities in network systems. Intrusion detection systems (IDS) play a crucial role in identifying malicious activities, but they face challenges due to imbalanced datasets where the number of attack samples outweighs normal activities. This paper explores the performance of an IDS using SMOTE (Synthetic Minority Over-sampling Technique) and various classification algorithms to address imbalanced datasets and enhance detection of multi-class intrusions. Related works in the field of intrusion detection are reviewed, highlighting the effectiveness of different algorithms and techniques. The proposed work presents a model that combines SMOTE with log normalization and feature selection to improve IDS performance. Experiments are conducted on the NSL-KDD and CIC-IDS2017 datasets, evaluating different oversampling configurations and machine learning models. The results show that applying SMOTE improves overall performance, with high accuracy, precision, recall, and F1-score. Feature selection has minimal impact on model performance, suggesting the presence of redundant features. The study concludes that SMOTE effectively addresses class imbalance and enhances IDS performance, emphasizing the importance of incorporating oversampling techniques in intrusion detection systems. [ABSTRACT FROM AUTHOR]

In a construction environment, project scheduling is an essential tool to measure the success of all projects. This research paper will use project crashing of Activity to decrease the project completion schedule through the CPM method of time-cost trade-off and to minimize project cost. It includes linear programming with the aid of Microsoft Excel Solver to determine the result. And at the end of the research paper, it will show the impact of crashing the activities for the HVAC mechanical installation in a project for both normal and crash time, which displays that the selected Activity that had been crashed generated an increase of 9.8 % total cost from the total project cost. [ABSTRACT FROM AUTHOR]

Temporary trade barriers, such as safeguards, is a measure to rescue the economy in such a way as to protect domestic industries from foreign competitors. Safeguards provide opportunities for domestic industries to make structural adjustments and reshape their business process to increase their competitiveness during the validity period. Indonesia's government has imposed safeguards for most textile sub-industries, from the upstream industry (yarn and fibres) to the downstream garment industry and other related textile industries. This paper aims to evaluate whether the application of safeguards meets its objectives in improving the competitiveness of domestic industry against its foreign competitors with a long observation period. It is found that the effectiveness of the application of safeguards was limited to rescuing the trade balance. In terms of export competitiveness, safeguards have not been effective in increasing the export competitiveness of the textile industry in the world market. [ABSTRACT FROM AUTHOR]

To generate a research overview of digital banking, we did a bibliometric analysis. In our bibliometric analysis, we identified 2475 papers about digital banking that were published in the previous 10 years (2013–2023) from 1492 different sources under the direction of 6777 academics. The bibliographic information was gathered using the Scopus database. The dataset was examined using the bibliometric R program Biblioshiny. The number of digital banking research publications published each year, the most relevant author, the most cited articles, thematic maps, and trending topics are all listed. A keyword analysis showed that "blockchain", "security", "digital banking", and "digital storage" appeared most often. The research results will be significant to scholars, researchers, and policymakers in banking, as the study showed the roadmap and pathways to scientifically understanding current and future research trends. [ABSTRACT FROM AUTHOR]

The traditional pen and paper-based administrative processes in Pondok Pesantren have been time-consuming and error-prone. To address these challenges, the E-Santren app system was developed to modernize and streamline administrative tasks. This research paper examines the acceptance and impact of the E-Santren app system as an alternative to the traditional approach. The study aimed to assess educators' perceptions and experiences regarding the implementation of the app system. Gorunded in the Technology Acceptance Model (TAM), a mixed-methods approach was employed, including a questionnaire survey and a focused group discussion. The results showed a positive acceptance of the E-Santren app system, with participants recognizing its benefits in terms of efficiency, accuracy, monitoring capabilities, and student compliance. This highlights a willingness among educators to adopt the new method, shifting away from traditional practices. The implications of these findings emphasize the potential for improved administrative processes in Pondok Pesantren through the adoption of the E-Santren app system. [ABSTRACT FROM AUTHOR]

This paper presents a Systematic Literature Review (SLR) to investigate the relationship of cost with Business Process Management (BPM). Cost is part of finance, so cost is often addressed in a broader sense related to finance. Previous studies show contradictory views on cost and BPM. Several studies state that finance did not have a direct relationship with business processes and other studies state the lack of theory relating cost to BPM. However, other studies saw a positive relationship and consider cost aspects to perform efficiency and optimization by using BPM. Further and in-depth research needs to investigate, the relationship between cost aspects and BPM particularly related to Information Technology applications. [ABSTRACT FROM AUTHOR]

The increased internet usage after the pandemic led the UN Forum to improve cybersecurity measures, with zero-knowledge proofs (ZKP) being a viable solution for securing confidential information. ZKP protocols can be demonstrated through the binary puzzle, an NP-complete logic puzzle with four specific constraints. The key contribution of this paper is its successful implementation of the genetic algorithm as a new method to solve the binary puzzle. The optimized fitness function determined the solution at an average of 1.33-2.33 generations for populations ranging from 100 to 500. Its quadratic property calculated the solution faster than the ordinary linear fitness function. [ABSTRACT FROM AUTHOR]

Numerous smart city frameworks have arisen in recent years as a response to the global urbanization problem. The emergence of frameworks with their distinct dimensions causes confusion in the selection of a framework and the implementation of a smart city. Therefore, more thorough research on the smart city dimension is necessary to build a more comprehensive smart city framework. This study conducted a systematic literature review to find the most prevalent dimension in the subject by examining a collection of 30 papers from five reputable sources that discuss smart city frameworks. A total of 38 smart city dimensions were retrieved from 12 smart city frameworks and 9 smart city maturity models. The 38 dimensions are then categorized according to their similarity. This study results in six main categories of smart city dimensions, including economy, people, environment, government, living, and branding. [ABSTRACT FROM AUTHOR]

The development of technology creates a new concept in trading by combining real-time video with commercial activities known as live-streaming commerce. Livestreaming commerce or better known as shoppertaiment is a combination of electronic commerce and entertainment. The trend of shopping through livestreaming commerce is increasing, thus changing consumer habits and everyday lifestyles. Based on this situation, it is necessary to conduct research on livestreaming commerce. The aim of the research conducted is to consider the results of pre-existing studies and systematically review literature reviews on livestreaming commerce research by summarizing all academic scientific publication papers from 2017 to 2022. The research questions from this study were: (1) What are the current live-streaming commerce studies? (2) What research methods have been used in live-streaming commerce studies? (3) What are the potentials for future research on live-streaming commerce research? To answer the first question, various aspects of live streaming commerce are described, such as definitions, types, technologies, challenges, benefits, and models. Answer the second question by applying various methods and techniques. Finally, it offers guidelines for future research and makes this work a road map for understanding the live-streaming commerce research literature. [ABSTRACT FROM AUTHOR]

This paper aims to prove that different types of users influence the degree of user satisfaction with the Surabaya Population Administration & Civil Registration Systems (KNG) and confirm that there are differences in satisfaction between internal and external users. D&M IS Success Model is used to evaluate user satisfaction. The data were collected via a survey and analyzed by the PLS-SEM method. The results revealed that service quality has the most significant influence on user satisfaction. The results also revealed that there are differences in variables that influence user satisfaction with the KNG system between different types of users. [ABSTRACT FROM AUTHOR]

Digital transformation is about transforming processes, business models, domains, and culture. Studies show that the failure rate of digital transformation is quite high up to 90%. Studies show that the transformational leadership model has a significant impact on digital transformation adoption. This paper identifies the positive and negative attributes of transformational leadership including the components that support and are affected for successful adoption of digital transformation. Furthermore, the paper combines several findings related to the attributes and components in the form of a conceptual framework. The conceptual framework can serve as a guide for organizations for their digital transformation journey. [ABSTRACT FROM AUTHOR]

This study aims to predict the determinants of mobile fintech apps and digital financial inclusion from the perspective of the Bottom of the Pyramid (BOP) segment in Indonesia. To address the complexities of BOP users, this paper theoretically grounded on Mobile Technology Acceptance Model (MTAM) (i.e. mobile usefulness (MU), mobile ease of use (MEU)) along with its relevant extension including personal factor (digital financial literacy (DFL)), financial risk factor (mobile perceived financial cost (MPFC)), and consequent factors (i.e. use behavior (UB), and digital financial inclusion (DFI)). The moderating impact of gender was also tested. A survey instrument that was self-administered and comprised measurement items adapted from prior research was utilized to gather data from 200 mobile fintech BOP users residing in Makassar, South Sulawesi. According to the results of the PLS-SEM test, seven hypotheses were accepted, while two were rejected. In addition, it was confirmed that men play a more prominent role than women in strengthening mobile fintech use and digital financial inclusion. [ABSTRACT FROM AUTHOR]

This conceptual paper aims to investigate the interrelationship between intellectual capital (IC) and frugal innovation (FI) in higher education institutions (HEI), considering the mediating role of information technology (IT) capabilities. With many private HEI facing financial challenges, it is crucial to explore how IC can be effectively managed to achieve frugal innovation. By drawing on the knowledge-based view (KBV), this study examines key factors that influence intellectual capital and its impact on frugal innovation in HEI. The findings emphasize the importance of leveraging intangible assets in HEI to enhance their capabilities and address the dynamic business environment. Specifically, the study highlights the need to identify the extent to which HEI can exploit IT capabilities to develop intellectual capital aligned with the criteria of frugal innovation for value creation. This research contributes to the development of evolving approaches in IC knowledge for effective IC management and highlights the benefits for HEI, including a focus on core functions, stronger integration with industry and local/international communities, and enhanced operational efficiency. By shedding light on the formalization, capture, and leveraging of intangible assets, rather than solely focusing on tangible assets, HEI can achieve frugal innovation. Overall, this paper provides a novel perspective by bridging the gap between frugal innovation and IC research in the context of HEI. It highlights the mediating role of IT capabilities in the relationship between IC and frugal innovation, adding valuable insights to the existing literature and contributing to the ongoing scholarly discourse in this field. [ABSTRACT FROM AUTHOR]

Higher education institutions in Indonesia are required to report their academic data to the national higher institution database (namely PDDikti) through a synchronization mechanism. Because the PDDikti database has been used as the reference for nationwide programs such as university ranking, diploma verification, faculty promotion, and tenure, academic data in their reports have to be valid, accurate, and up to date. Yet, data quality is an issue among higher institutions, while data governance is the heart of data quality. This paper investigates challenges encountered by a public university in Indonesia in implementing data governance and how to resolve them. The findings outline six main challenges in data governance implementation. [ABSTRACT FROM AUTHOR]

Although there have been reviews of research in cybersecurity, there has yet to be an identification of the information systems approach used. This study aims to identify the trends in the information systems approach used in cybersecurity. The research is designed as a systematic literature review (SLR) with grouping based on cybersecurity functions and information systems aspects. SLR process gained 23 papers from 2017 until 2023. As a result of extraction, an information system approach can be used to detect, deter, protect, and identify cybersecurity threats. This approach addresses various aspects, including people, processes, and technology. [ABSTRACT FROM AUTHOR]

Disaster Recovery (DR) providers define the main services as backup and restore, both with critical data and applications. Most clients of data and application backup, fully rely on DR's services to restore them in the disaster events. Furthermore, in the normal conditions, DR provider backs up data, sometimes with application in routine or regular basis. Thus, providers should deliver the services in sophisticated way through several Key Performance Indicators (KPIs). In this paper, development of KPIs is focused on performance and assurance, which a laboratory testing simulates backup and restore services. On the other hand, experimental data such as network and system parameters are analysed to build performance and assurance framework. Meanwhile, interdependence among each performance and assurance of DRCs was used to build a relationship model. It is acted as a part of service level agreement, that a DR providers can present services dynamically. They also should adapt the computing resources to deliver services to meet SLA (Service Level Agreement) in flexible strategy. In turn, they can negotiate SLA with the customers based on this new approach. [ABSTRACT FROM AUTHOR]

The decision made by machine learning is mostly based on historical data that is used to train them. It raises the awareness that discrimination in machine learning should be eliminated because it may contain societal bias. The financial industry uses credit scoring as a reference to reflect the customer risk profile. To achieve fairness in the model, this paper tries to: (1) assess bias and (2) improve fairness in machine learning models with three bias mitigation methodologies. This study depicts that there is a trade-off between improving fairness and preserving performance. Implementing post-processing methods, for example, Grid Search performs best. [ABSTRACT FROM AUTHOR]

Online Food Delivery (OFD) is a service innovation that significantly impacts current technological developments in the food and beverage sector. This paper aims to review the academic literature on OFD services systematically. This study reviews all literature published from 2017 until 2022. Literature is classified based on the context, the theory, and the factors influencing the adoption of OFD. The results of this study indicate that twenty-one factors have a dominant influence on OFD adoption. These factors are classified into three categories, namely individual factors, technological factors, and external factors. This study highlights that individual factors are the most commonly used factors for OFD adoption. This study outlines directions for future research and allows a detailed view of consumer behavior in OFD businesses. [ABSTRACT FROM AUTHOR]

This paper aimed to explore the application of SERVQUAL in assessing the performance of in-house detailed engineering design in an engineering management firm. The goal is to evaluate service quality and identify gaps between customer expectations and perceptions across the five service quality dimensions. Results show that customers are dissatisfied with the department's service quality, and negative gaps exist across all 22 questions. Improvements in reliability, assurance, and responsiveness are recommended to enhance customer satisfaction. The study demonstrates a strong correlation between service quality dimensions and contract retention, highlighting the importance of improving service quality for long-term business success. [ABSTRACT FROM AUTHOR]

Many cities around the world have experienced an increase in population and pose challenges in planning and managing the freight transportation within the city. Off-peak hour delivery (OPHD) is a strategy of delivering goods in the afternoon and evening to reduce congestion during peak periods, increase the efficiency of delivery companies, and reduce emissions. This research proposed a model of city logistics in the retail supply chain by implementing OPHD in the logistics system. The aim of this study is to explore the extent of potential implementation of OPHD in urban logistics. An agent-based modeling and simulation (ABMS) approach is applied to model the autonomous and dynamic interactions of stakeholders involved in city logistics systems. Preliminary results are presented in this paper to provide illustrations of the model. It shows that OPHD can significantly reduce the transportation cost and emissions cost, and ABMS is able to provide results that are relatively close to pilot test drive research which is carried out in real world. [ABSTRACT FROM AUTHOR]

This paper presents the development of a web-based course timetabling system based on an enhanced genetic algorithm. The enhanced method utilizes a heuristic mutation which concentrates on mutating the infeasible genes to improve the algorithms' exploration and exploitation capability. The method was implemented using a free and open-source application and can be accessed online. Based on the actual datasets from Caraga State University, the enhanced method optimized the use of classroom resources by using a smaller number of rooms. The generated timetable is more efficient as it satisfies not just hard constraints, which are conflicting schedules, but also soft constraints. [ABSTRACT FROM AUTHOR]

Intermittent data, characterized by sporadic and irregular occurrences, have successive zero values in time series, had present unique challenges for modeling and analysis. Forecasting using intermittent data is not easy to do. This paper presents an application of the Group Method of Data Handling (GMDH) in modeling and forecasting intermittent data. GMDH models excel in capturing non-linear patterns, handling missing or sparse data, and adapting to changing dynamics. By iteratively selecting the most informative variables and estimating their coefficients, GMDH constructs a hierarchical network of interconnected models that can effectively handle intermittent data. The forecasting results show that GMDH is able to follow the pattern of actual data. The mean deviation accuracy analysis shows a value of 72.22%. The findings showcase the potential of GMDH as a valuable tool in addressing the challenges associated with intermittent data and offer insights into its application across various domains. [ABSTRACT FROM AUTHOR]

At present, technological systems lack a secure and transparent method for tracking goods and preventing theft in e-commerce, leading to trust issues and data vulnerabilities. There is a pressing need for a comprehensive solution that integrates Ethereum blockchain, IPFS, and advanced cryptographic techniques to address these challenges and enhance the security and transparency of transactions. This research paper presents a robust system that harnesses the Ethereum blockchain, IPFS (Interplanetary File System), and advanced cryptographic algorithms to create a secure, decentralized approach for tracking goods and preventing theft incidents. Unique identifiers and related information will be sent to the mail of the customer and same should entered by the customer for successful transaction. By assigning unique identifiers to purchased products and employing cryptographic techniques to encrypt sensitive data, our system ensures both user privacy and the creation of an immutable transaction ledger. Users can efficiently manage their purchased goods, block stolen items, and communicate with sellers through an intuitive interface. Additionally, the system provides sellers with a comprehensive transaction history, enhancing accountability and transparency within the supply chain. Through this research, we demonstrate the effectiveness of our blockchain based anti-theft measures, underpinned by Ethereum, IPFS, and cutting-edge cryptographic algorithms, in fostering secure, trustless transactions. This work highlights the transformative potential of blockchain technology and decentralized protocols in revolutionizing security and transparency across diverse sectors. [ABSTRACT FROM AUTHOR]

Renewable energy sources, driven by the global concern for climate change and the urgent need to reduce greenhouse gas emissions, are gaining increasing importance. Among them, solar energy stands out as a crucial renewable source and accurate solar irradiance forecasting is vital for its effective utilisation. Deep learning (DL) techniques have emerged as state-of-the-art solar irradiance forecasting methods in past years. However, traditional models that are widely used have inferior accuracy in forecasting. To bridge this gap, this paper presents an improved state-of-the-art model that leverages the convolutional property to forecast the time series data. We got a reduction of 8.32% in MAE compared to the base model. The paper's findings highlight the potential of DL-based approaches to significantly improve the accuracy of solar irradiance forecasts. [ABSTRACT FROM AUTHOR]

Graphs are the most complex data structure that involves heavy mathematical computations. Various real-time applications use the graph at storage, processing, object characterization, and behavior specification stages. The connectivity analysis, coverage building, and routing are the critical applications of graph evaluation. Various associated applications need the exploration of a graph with a practical and real-time outcome. The processing of larger graphs is a big challenge for the researcher. In this paper, a hybrid graph-based computation optimization method is designed to optimize the performance of graph processing in multi-core processors. The BFS algorithm is applied over the multiple processors to optimize the functional response. The multithreaded-BFS is implemented to reduce the time of graph processing and computation. The proposed algorithm is implemented in the OpenMP environment. The experimentation is done on two-core and four-core processors. The experiment is conducted on a 100 to 1000-node network. The traversing and graph coloring algorithms are implemented for the analysis. The comparative analysis is conducted on single-core and multi-core processors with DFS and BFS-based algorithms. The analysis results identified that the proposed algorithm reduced the processing time effectively. [ABSTRACT FROM AUTHOR]

Blockchain is an emerging technology penetrating various real-time and sensitive domains including financial products. Block chain can deal with complex system of financial transactions with focus to provide security and integrity to both the parties with reliable flow of transactions and asset securitization. Block chain is a distributed technology providing a lot of benefits such as cryptic transactions, smart contracts, distributed ledger system along with decentralization, authentication, authorization, and immutability to enhance the efficiency of the financial system. In this paper, the existing solutions for asset-backed securitization in blockchain are analysed and reviewed to highlight the requirements for change in design and ideas for enterprise applicability of blockchain for asset securitization. Also, the benefits of blockchain to reduce the risk. [ABSTRACT FROM AUTHOR]

Mutual funds, a cornerstone of modern investment portfolios, offer investors a diversified and professionally managed approach to financial growth. These investment vehicles collect small amount from multiple investors and invests the collective investment further in diversified portfolio of stocks, bonds, or other securities. Mutual funds are characterized by their net asset value (NAV), which represents the per-share value of the fund's assets minus its liabilities. Investors benefit from the expertise of seasoned fund managers who make investment decisions based on their analysis of market trends and company performance. By providing an accessible and diversified investment option, mutual funds have become a popular choice for both novice and seasoned investors seeking to achieve their financial goals. Apart from that, the role of AI is increasing exponentially in various areas. Such AI based tools and algorithms are utilized by various MFs expert to identify the suitable investment portfolios. This paper proposes a technique to analyse the performance of the different stock's portfolios based on the past data. Combining the isolated analysis of different stocks portfolios may be utilized to predict the net asset value prediction (NAV) of MFs. The experiments carried out in this research work uses linear regression analysis to analyse the stock's performance at sub level of the MFs. A case study for the Axis Bluechip fund's is also carried out by using the proposed ML approach. The Yahoo Finance dataset is used for the case study. Previously, machine learning algorithms like linear regression (LR), decision tree regression (DTR), and multivariate regression (MR) are taken to predict and analyse the dataset. These algorithms calculate the NAV of the mutual fund with good accuracy and analyse the best result for the test dataset. The various models are being created from different portfolios to analyse the NAV of the mutual funds. This work proposed hierarchical method to perform the linear regression analysis for the NAV predicting. This work uses R-Square (R2) statistical measures to determine the goodness of models. The obtained R2 is 0.86, implying that the created models have good accuracy and performance on the dataset. [ABSTRACT FROM AUTHOR]

In the realm of online learning and distance education, the issue of inadequate supervision looms large, posing a significant obstacle. This paper delves into the challenges posed by the lack of supervision in online learning environments and proposes an innovative solution to understand and recognize students' behaviors. This study's primary objective is to detect and recognize students' actions in images captured through webcam. This task distinguishes itself from the well-established video-based student action recognition domain, which relies on temporal cues. Recognizing student actions from images intensifies the complexity of the problem. To meet this challenge, a novel deep learning model named AdaptSepCX Attention, specifically designed for student action recognition in online learning environments, is introduced. The proposed method exhibits exceptional performance with 92.73% validation accuracy on the Student Online Action Image dataset (SOAId), a carefully curated collection comprising 2029 student-centric images. The proposed model outperforms well-established models such as DenseNet121, NASNet Mobile, Con-vXNet, DELVS1 and MobileNetV2 in student action recognition. Action recognition for students has broader implications beyond the online classroom. It has the potential to revolutionize educational technology, making online learning more interactive and engaging. Enabling machines to understand and respond to student actions enhances education, personalizes learning, and supports students' academic success and well-being. This research enhances the understanding of student involvement in online learning and offers an effective solution for recognizing actions from images. [ABSTRACT FROM AUTHOR]

Next generation firewall is taking major part to secure network environment in the industry. This device will monitor all the traffic which is coming inside the network or going outside of the network. With all these security devices attackers can still perform various kind of attacks on the network. DDoS attack is one of the hardest attack to identify which will send packets to the network and which will look like normal traffic but it will act as a DDoS traffic. In this paper we used binary decision tree, XGBoost and support vector machine to identify DDoS attack traffic pattern from the different features in the packet header. Data will be fetched from the packet header and among them standard deviation of the packet bytes and packet flows are the features considered. We have applied this data on the trained dataset. Algorithm will predict whether the traffic coming inside the network is trusted or not. Out of the three algorithms Binary Decision tree algorithm is giving 99 percentage of accuracy and will predict the data as fast as possible. Here priority is to filter DDos attacks of any security level in the line speed of the NIDS or any other appliances. This method of DDoS attack detection will add extra layer of security in the next generation firewall which will make firewall more robust. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has presented numerous challenges around the world, including lockdowns, remote working and studying, and travel restrictions imposed by governments to curb the spread of the virus. As vaccines have become more widely available, restrictions have begun to ease for those vaccinated. Many countries use paper-based COVID-19 vaccination certificates to prove vaccination status. However, traditional certificates are vulnerable to forgery and counterfeiting. We developed a blockchain-based system where vaccination certificates are stored and accessed via smart contracts on an Ethereum blockchain. The certificates are stored in a tamper-proof, decentralized manner, ensuring secure verification of vaccination status. This research project successfully designed and implemented a system that securely stores and verifies vaccination certificates using blockchain, demonstrating the benefits of this approach. According to our knowledge, this is the first kind of research initiative in Brunei to develop a blockchain-based immutable COVID-19 Vaccination certificate. Despite its advantages, blockchain still has its flaws, particularly in scalability and adoption, which should be considered for further optimization of the proposed system. [ABSTRACT FROM AUTHOR]

Diabetic Retinopathy (DR) is an eye ailment in diabetics, often causing vision loss. Timely retinal screenings can help prevent blindness. While DR cannot be cured, early detection can curb vision decline. Ophthalmologists diagnose DR using Optical Coherence Tomography (OCT) or Fundus Images. Evaluating via fundus images can be complex and potentially imprecise. The study offers a computer-supported diagnosis to aid ophthalmologists. It uses Multi-level Otsu thresholding to segment Fundus Images, with segmented outputs processed further. Advanced feature extraction techniques like the Gray-Level Co-occurrence Matrix (GLCM) and dynamic Flemingo optimization enhance DR feature identification. Additionally, a novel cascaded voting ensemble deep neural network model is introduced, merging multiple algorithmic insights to improve classification. The paper concludes by aligning classifications with a standard grading system, giving clinicians a clear DR severity gauge for better treatment planning. [ABSTRACT FROM AUTHOR]

Pulmonary Embolism (PE) is a blood clot in the pulmonary arteries of the lungs. Currently, Computerized Tomography Pulmonary Angiography (CTPA) scans are used to diagnose this condition. However, to manually locate the presence of a PE in the scan is laborious. In this paper, the detection of PE and prediction of its features like location, Right Ventricle to Left Ventricle (RV/LV) ratio, and chronicity are automated. First, for the detection of the embolisms, image features from the RSNA Pulmonary Embolism CT (RSPECT) dataset - containing over 12,000 CTPA studies, are extracted by several pre-trained Convolutional Neural Networks (CNN) - AlexNet, Inception V3, ResNet-18, and ResNet-50. Then, various Machine Learning (ML) classifiers are trained on these features and their performances are compared. Second, for the prediction of labels, multi-label classification is performed by training these networks with Binary Cross Entropy Logits (BCE Logits) loss. The results showed that for the detection task, k-NN classifier trained on ResNet-50 features achieved the highest sensitivity of 0.98 with 1.7 false positives per scan and an AUROC score of 97.4%. This system also improved on the state-of-the-art sensitivity score by 0.0975 and AUROC score by 3.4%. For multi-label classification, ResNet-18 performed comparatively better with the best validation loss of 0.07 and weighted macro-average ROC AUC score of 0.61. [ABSTRACT FROM AUTHOR]

The research presented in this paper addresses the critical challenge of ranking Indian villages based on their potential for socio-economic growth, inspired by the objectives of the Shyam Prasad Mukerji Rurban Mission (SPMRM). Leveraging a novel algorithm known as ClusterRank, an extension of the PageRank algorithm, we endeavor to provide a timely, efficient, and accurate solution for village ranking. The ClusterRank algorithm, an extension of the well-known PageRank algorithm, serves as the cornerstone of our approach. It excels in its ability to efficiently and accurately rank villages, outperforming alternative ranking methods. Notably, our findings reveal the superior convergence speed of the ClusterRank algorithm, affirming its capacity to produce rapid results. To assess the efficacy of ClusterRank, we employ a range of statistical ranking coefficients, including the Spearman, Kendall, and Point Biserial correlation coefficients. Our analysis demonstrates a strong positive correlation, with a Spearman correlation coefficient of 0.89, between the rankings generated by ClusterRank and the ground truth rankings provided by SPMRM. This correlation underscores the effectiveness of ClusterRank in identifying villages with high growth potential and highlights its alignment with the objectives of the SPMRM. This research presents a compelling case for the adoption of the ClusterRank algorithm by SPMRM as a valuable tool in the identification of rural areas poised for rurban development. Such adoption not only promises substantial time and resource savings but also directs the focus of SPMRM towards the implementation of targeted interventions and initiatives to uplift rural communities. Moreover, our supplementary analyses using Kendall and Point Biserial correlation coefficients further validate the robustness of the ClusterRank algorithm. [ABSTRACT FROM AUTHOR]

This paper introduces a novel application of neural style transfer to video sequences, leveraging pre-trained Convolutional Neural Network (CNN) models. By combining content and style losses in an optimization process, the approach transforms video frames into visually captivating compositions, opening new avenues for artistic expression in visual storytelling and filmmaking. The procedure begins with meticulous preprocessing of video frames to align them with the specifications of the powerful VGG16 model. A content model is then constructed to extract activations from the 14th layer of the VGG network, forming the groundwork for nuanced content depiction. Style information is computed by aggregating data from a combination of convolutional layers, resulting in a multifaceted, artistically complex tapestry. The paper introduces a well-balanced loss function that combines style and content losses, serving as the foundation for the ensuing optimization procedure. This careful balancing between style adherence and content authenticity yields visually stunning synthesis, enhancing video sequence quality and artistic resonance. The paper also delves into a detailed analysis of hyper parameters, particularly style weights, and introduces a novel pixel value scaling method for enhanced visual coherence. Experimental findings demonstrate the efficiency and adaptability of the proposed approach, showcasing the seamless integration of content and style for an immersive visual experience. This accomplishment marks a significant stride toward unlocking the creative potential of video content, ushering in a period of enhanced visual storytelling. [ABSTRACT FROM AUTHOR]

In the current pandemic scenarios, a non-invasive method for determining a neonate's respiratory rate and categorizing them using a deep learning technique is highly pertinent. Acquiring an infrared neonatal dataset for detecting and classifying respiratory syndromes is challenging. The limited number of infrared videos and images representing different types of syndromes is a tremendous challenge to the accuracy of the deep learning model. This paper uses the Deep Convolutional Generative Adversarial Networks(DCGAN) with gradient penalty for the data augmentation. The Discriminator in a standard DCGAN architecture is a convolutional neural network (CNN) that receives an image as input and outputs a single scalar value that indicates the likelihood that the input image is real or fake. Adding a gradient penalty adds a regularisation term to the loss function. This modification helps to stabilize training by preventing mode collapse and generating higher-quality images. The augmented dataset helped to make the original imbalanced dataset more balanced and increased the size of the original dataset. When the accuracies of the deep learning models trained on the original and balanced augmented neonatal datasets were compared in this work, the model based on the balanced augmented dataset performed better. [ABSTRACT FROM AUTHOR]

Blockchain is a decentralized digital ledger technology that offers transparency, security, and immutability by recording and verifying transactions across multiple computers. However, it faces limitations such as scalability issues, high energy consumption, slow transaction validation, expanding storage requirements. Understanding these limitations is crucial for evaluating blockchain's suitability for specific use cases and finding solutions to overcome these challenges. Recognizing these limitations is imperative for assessing blockchain's applicability and devising solutions. This paper introduces a hybrid mechanism, ShardedScale, designed to enhance scalability, throughput, and system performance while mitigating time, gas, and ether consumption. The proposed ShardedScale approach combines various techniques including Proof of Work (PoW), Dynamic On-demand Proof of Stake (DPOS), Inter Planetary File System (IPFS), and sharding. ShardedScale achieved a remarkable 40% reduction in transaction confirmation times, a substantial 60% improvement in throughput, and demonstrated cost-effectiveness with a 30% reduction in gas price consumption and a 25% decrease in ethers consumption. These findings signify a significant step towards addressing challenges faced by traditional blockchain systems, providing valuable insights for future designs in blockchain technology. [ABSTRACT FROM AUTHOR]