bluuebunny/crossref_metadata_2025 huggingface dataset:
Comparison of Muscular Strength, Speed, Flexibility and Agility among Professional and Sub-Professional Players of Basketball
Authors: Shahzad Umar , Rashid Mehmood Muhammad , Mubashar Muhammad , Umer Ilyas Muhammad , Ul Mass Qusas
Published: August 2024

Physical fitness components are widely unspoken, such as the ability to carry out daily tasks with vigor and alertness, maintain energy reserves to enjoy leisure activities and respond effectively to unforeseen challenges. The primary purpose of this study was to compare muscular strength, speed, flexibility, and agility among sub-professional basketball players. A total of 60 (60) subjects participated in this study, which were basketball game players and citizens of Pakistan. The nature of the study was quantitative in approach; all the subjects were selected randomly when they were attending national training camp. The Statistical Package of Social Sciences (SPSS) version 26 was used to analyze the data for descriptive statistics (mean, standard deviation) and inferential statistics tools (independent t-test and ANOVA). The mean and SD of professional basketball players was 5.60±.01, and the mean and SD of sub-professional basketball players was 6.72±.03. Significant differences were observed in both groups of sub-professional players. Professional players reported higher mean scores in muscular strength, flexibility and agility than professional players. This showed that muscular strength, flexibility, and agility were reasonable compared to professional players. At the same time, the professional players of basketball reported higher mean scores in speed than professional players of basketball.
Socio-Economic Determinants of Crime: A Case Study of District Prison of Vehari
Authors: Sultan Sana , Ilyas Sidra , Saeed Kashif , Dilawar Sabira , Ibrar Nayla , Ramzan Sheikh Muhammad
Published: August 2024

This study is crucial for understanding the intricate relationship between socio-economic factors and criminal behavior. This research shows how various socioeconomic conditions, such as employment status, education attainment, and social inequality, influence crime rates in Vehari. Gaining insights into these determinants is essential for developing targeted and effective strategies to reduce crime and improve social stability in the district. This study investigates the determinants of crime in district Vehari by testing 15 hypotheses related to crime-related and socioeconomic factors using cross-tabulation analysis and chi-square tests. Data was collected from 169 prisoners at district jail Vehari through simple random sampling. The study reveals that property crimes are more prevalent than violent crimes, with offenders serving shorter sentences for property crimes. Social factors significantly influence both types of crime, with property crime offenders showing higher levels of regret and being less influenced by inmate interactions compared to their violent crime counterparts. In contrast, violent crime offenders tend to come from nuclear families, live in urban areas, and have somewhat better educational backgrounds. Chi-square tests confirm that crime type is significantly associated with offence frequency, sentence duration, crime motivations, education level, distrust, social deprivation, and family relationships. However, crime type is not significantly related to prisoner remorse or interactions with other inmates, family type, area of residence, or relationship with the household head.
Role of Information and Communication Technologies (ICTs) in Students’ Knowledge at University Level: An Analysis
Authors: Mazhar Ayesha , Muhammad Zafar Jam , Ullah Naeem , Meharunisa
Published: August 2024

The study analyzed the "role of Information and Communication Technologies (ICTs) in students' knowledge at university Level". The study was descriptive and survey in nature. The quantitative and qualitative (QUAN-qual) methods were used. The explanatory sequential technique was adopted. The cluster random sampling was used for data collection. The study sample consisted of 384 male and female students (192 male, 192 female) (undergraduate) at Khwaja Fareed University of Engineering and Information Technology (KFUEIT). The questionnaire was developed for data collection from students. The validity of the questionnaire was ensured through experts' opinions, and the reliability of the questionnaire was calculated through SPSS-24 using Cronbach Alpha. The study found that 80% of students used ICTs to increase their subject knowledge. The mean score of 3.95 and the standard deviation of 1.06 supported this. In qualitative data, the majority of students agreed to increase the availability of the Internet; most of them suggest to promote computer-based assignments, some of them said to arrange online classes, and few said to facilitate the use of LMS, while very few suggested encouraging the use of the projectors. Technology should be provided to all students for learning. The study recommended that by integrating ICTs into the teaching and learning process, teachers and students may be provided with adequate technical support, administrative support and appropriate access to technological resources.
Lydia’s Alienation and Societal Burdens: A Marxist Analysis
Authors: Hizbullah Muhammad , Ur Rahman Saeed , Ubaid Ullah Jan Talha
Published: August 2024

This study aims to analyze Lydia's alienation in Ng's everything I never told you through a Marxist perspective, focusing on societal factors contributing to her isolation. The significance of this study lies in uncovering the cultural determinants of alienation, offering insights into the broader implications of these themes in contemporary society. Using a Marxist framework, the study examines how class structures, racial discrimination, and capitalist ideologies shape Lydia's psychological state and her interactions with her family and society. Findings suggest that Lydia's alienation is deeply influenced by the oppressive societal expectations and racial prejudices that stem from capitalist and classist structures, leading to her sense of estrangement and eventual tragic fate. This research highlights the need to critically examine societal pressures and their impact on individual well-being, particularly within marginalized communities.
Impact of the reform to the general royalties system on employment and business development in the department of La Guajira
Authors: Morales Bustamante Jose Gregorio , Puche Pinto Luis Angel
Published: August 2024

The main purpose of this research is to analyze the impact of the reform of the general system of royalties on employment and business development in the department of La Guajira, in order to fulfill the mission and vision of the University of La Guajira, regarding the study of the problems that arise in the department, thus allowing us to explore the current context in the economic and social aspect, establishing the real impact of the aforementioned reform, to achieve the proposed objectives we will use information obtained from various sources such as: the departmental government, municipal mayors' offices, the Guajira Chamber of Commerce, the Bank of the Republic, the Regional SENA, and the Regional ICBF. Thus contributing to the study and analysis of the department's problems. The objective of this study is to analyze the impact of the reform of the general system of royalties on employment and business development in the department of La Guajira, theoretically supported by the postulates of authors such as: Gómez and Garcia (2012), Barón (2012), Martínez & Collinao, (2010), Biltrán (2009), Restrepo (2006), Buendía (2002), Marinez (2004), all of whom made important contributions regarding the variable royalties, business development and employment
Tools for AI-driven Development of Research Competencies
Authors: Aristizábal Valbuena Chris Nathalie
Published: August 2024

Artificial intelligence (AI) tools are transforming scientific research by enabling the analysis of large volumes of data and the generation of new hypotheses and theoretical models. In 2024, there is an expected proliferation of smaller and more efficient AI models that can run on accessible hardware, facilitating the democratization of access to this technology. This will allow academic institutions and small businesses to implement and optimize AI models without the need for expensive infrastructures. The ability of AI to handle and analyze large datasets has been particularly useful in fields such as biomedicine, where it has accelerated the discovery of new treatments and therapies. Furthermore, the integration of AI models into local devices addresses critical concerns regarding data privacy and security, enabling the secure processing of sensitive information. These tools not only enhance the efficiency and accuracy of research but also foster innovation by expanding the frontiers of knowledge in diverse disciplines.
The principle of transparency and Electronic Government in Cuba
Authors: Bell Águila Osmel
Published: August 2024

The Electronic Government includes better ways of interaction between Government, companies and society through Information Technology and Communications. Access to visible, available and quality public information quickly and expeditiously improves the perception of the functioning of the institutions in pursuit of Cuba's efforts to advance in the digital transformation. The effectiveness of the principle of transparency from the constitutional mandate obliges the Public Administration to generate updated, reliable and accessible information according to the activity carried out. It is a prerequisite for good administration that involves citizens in decision-making processes as well as balancing their position vis-à-vis the State. It promotes good governance derived from the establishment and management of effective, efficient and accountable institutions.
Social responsibility of small and medium enterprises in Vietnam through digital transformation and application of artificial intelligence
Authors: Anh Duong Ngoc , Duc Phan Minh
Published: August 2024

The study on the social responsibility of small and medium enterprises (SMEs) in Vietnam through digital transformation and the application of artificial intelligence explored key aspects such as challenges faced during digital transformation, the importance of SMEs in the Vietnamese economy, and the significance of corporate social responsibility (CSR). It emphasized the need for SMEs to adapt to remain competitive and contribute more significantly to the state budget. The research highlighted the landscape of SMEs in Vietnam from 2017 to 2021, focusing on their classification, numbers, and characteristics, noting a steady increase in the number of SMEs each year. The document discussed the limited adoption of advanced technologies like artificial intelligence among Vietnamese SMEs and the need for increased support and resources for effective digital transformation, especially adopting AI technology. Additionally, it touched upon the social responsibility aspects of SMEs in the context of digital transformation, addressing opportunities and challenges related to environmental impact, labor productivity, financial transparency, and animal welfare. Through a qualitative analysis approach, the study aimed to provide insights into the evolving landscape of SMEs in Vietnam and their integration of digital technologies to enhance social responsibility practices
Correlation between Productivity and Relationship with Co-Workers and Supervisors among Selected Nurses in Quezon City
Authors:   University of Santo Tomas, Yap Lauren Abigail , Dupaya Joaquin Matteo ,   University of Santo Tomas, Incognito Marc Jio ,   University of Santo Tomas, Yañez Jazmine Yrah ,   University of Santo Tomas, Pura Rosa Bernadette ,   University of Santo Tomas, Candelaria Chloe ,   University of Santo Tomas, Briones Vince Angelo ,   University of Santo Tomas, Ferrolino Arthur ,   University of Santo Tomas, Tumbali Mary Vida ,   University of Santo Tomas
Published: August 2024

In the recent Covid-19 pandemic, there has been an increase in the rate of healthcare workers who experience burnout, thus decreasing their work productivity. However, few studies tackle how co-worker support among healthcare workers in the Philippines affects their productivity. This research aimed to identify the effect of co- workers’ support on the job productivity of the employees in a government-owned hospital specializing in cardiology. This study identified the relationship between co-workers’ support, co-workers’ relationship, and relationships with the board of directors or bosses and its effect on the job productivity of the nurses using a convenience sampling technique. The study used descriptive-correlational analysis to examine 100 respondents from the survey and utilized the Social Capital Theory to discuss further and analyze the effects of the variables. It was found that there is a direct low relationship between the productivity of nurses and co-workers and supervisor support. Therefore, the researchers recommend that the hospital shall ensure an open communication line between supervisors and the employees. There is also the need for future research to delve deeper into the nurses’ experiences, using a qualitative method to reveal additional data and knowledge on the above field.
DIGITAL TRANSFORMATION OF MSMES: STRATEGY AND CHALLENGES
Authors:   MJPRU, Bareilly, U.P., India., Khandelwal Rakhi , Priya Ashutosh ,   MJPRU, Bareilly, U.P., India.
Published: August 2024

Micro, Small and Medium Enterprise (MSME) sector is a very prominent sector of the Indian economy, contributing significantly, to Gross Domestic Product (GDP), merchandise exports and inclusive growth of the country. It is second largest source of providing employment after agriculture. It is also a major socio-economic contributor of our country. However, this sector has faced few challenges over the past few years like lack of technological knowhow, insufficient supply chain, capital crunch that have worsen the condition. It is considered that technology is playing wider role in economic growth. Digital technology has brought a drastic change in way of doing business, it automates the process and product, so it improves the production and quality of product. Throughout the period, the government also has undertaken several policy measures related to finance, infrastructure, risk mitigation etc. to trigger growth of MSMEs. With the government initiatives, such as, digital India that led the boom in digital payment and the overall technological adoption, in doing business, has improved in the country. This paper outlines the potential of digital transformation which could manacle its growth prospects and the possible challenges to transform the MSME. This paper provides a comprehensive view of the factors affecting digital transformation process.
MODEL ORDER REDUCTION USING EIGEN SPECTRUM ANALYSIS AND ROUTH ARRAY METHOD
Authors:   Shobhit University, Meerut, U.P., India., Ahamad Mohd , Rana Jasvir Singh ,   Shobhit University, Meerut, U.P., India.
Published: August 2024

The author suggests a hybrid technique to reduce the high order dynamic systems. The suggested approach uses the Routh array method to obtain the coefficients of the numerator and the Eigen Spectrum Analysis of the original system to generate the denominator polynomial of the lower order system, which is preserved in the reduced model. This technique is easy and simple and generates stable reduced models if the original high- order system is stable. The suggested technique is illustrated with the help of the numerical example taken from the literature
Predicting Credit Card Fraud on a Imbalanced Data
Authors: Soh Wei Wen , Yusuf Rika Mohd
Published: August 2024

Credit card fraud is increasing considerably with the development of modern technology and the global superhighways of communication. Credit card fraudsters continuously try to come out with a new tactic challenged the present technology and system. It cost both, providers and consumers a lot of money. Thus, quick and accurate model become essential for companies and credit card providers, to decrease their financial and customer trust losses. However, there is a lack of published literature on credit card fraud detection techniques, due to the unlabeled credit card transactions dataset for researchers. High dimensional data refer to data that have multiple variables. The dataset consist of the credit card details, amount transaction, location, time, and personal details of the cardholders that are anonymized. Thus, in this study, a real-world dataset (European Credit card), with PCA transformation applied is being used. The common problem happened in this kind of research is the data tend to be imbalanced. Imbalanced data will often introduce bias which the accuracy of the prediction is not accurate. In this study, the dataset has been train with an oversampling pre-processing technique called SAS Sample and various data mining technique such as Random Forest, KNN, Decision Tree, and Logistic Regression. After several trials, we found out that the regression technique has the best performance among the others.
Instinctive Traffic and Toll Plaza Management with Computer Vision and Image Processing using MATLAB
Authors: K Rajalashmi
Published: August 2024

In the current world, people don’t have time to ride or drive carefully by following appropriate traffic rules and discipline to avoid any casualty. Everyone is in a hurry to reach their destination on time; this leads to many traffic problems and create cumbersome among the public. On the other hand, inspite of utilizing advanced innovation to save time, it gets spoiled due to the toll plaza, where it takes any minutes or sometimes it even takes a whole day to pass through. These regular issues are happening due to the manual process that being held. In this project, an image processing and compute vision is introduced, so that if any individual violate the traffic rules (rough driving, over speed,one way, tripling on bike, no helmet, fastening the seat belt etc…) a PDF is generated and either mail to the respective individual or sent post, so that he/she should aid the penalty rely upon the negotiation within one month. Equivalently, in Toll Plaza a smart card encrypted with image processing will be given to each individual which is accessible in a certain particular locality. It decodes the registration number, it has to be linked with the Aadhaar card, so that by using this smart card the individual can either pay with the payment card (credit, debit card) or can recharge the smart card and paid accordingly. A bill is sent to the respective individual by means of either one way, two way billing method or manually; these are processed automatically.
Drone Autonomous Landing on a Moving Maritime Platform Using Machine Learning Classifiers
Authors: Shriki Udi , Gal Oren , Doytsher Yerach
Published: August 2024

Quadcopters are four rotor Vertical Take-Off and Landing (VTOL) Unmanned Aerial Vehicle (UAV) with agile manoeuvring ability, small form factor and light weight – which makes it possible to carry on small platforms.  Quadcopters are also used in marine environment for similar reasons – especially the ability to carry on small boats, instead of using helicopters on larger boats. Pilots of both manned and unmanned aerial vehicles over sea, face the challenge of landing their aircraft on a boat that is continuously rocking in all three axes (pitch, roll, yaw) and with movement of the boat, usually at steady course and speed. In this paper, we present a new approach for autonomous landing a quadcopter on a moving marine platform. Our approach is based on computer-vision algorithms using markers identification as input for the decision by Stochastic Gradient Descent (SGD) classifier with Neural Network decision making module. We use OpenCV with its built-in ArUco module to analyze the camera images and recognize platform/markers, then we use Sci-Kit Learn implementation of SGD classifier to predict landing optimum angle and compare results to manually decide by simple calculations. Our research includes real-time experiments using Parrot Bebop2 quadcopter and the Parrot Sphinx Simulator.
Cancer Type Driver Classification Accuracy Using Spark ML Technology
Authors: Vistro Daniel Mago , Farooq Muhammad Shoaib , Rehman Attique Ur , Tanveer Hafiz Abdullah
Published: August 2024

In this paper, analysis of genes extracted from the body has been performed that can be a driver of tumor, resulting in a cancer of different types like breast cancer etc. motived by the BIGBIOCL. Classifier with Alternative and Multiple Rule Based (CAMUR) is a core algorithm that is applied here to dissect large datasets. For the purpose to acquire the desire goal, Apache Spark as well as MLlib is used, on stack of Hadoop in local mode. The practice has been performed using the decision tree as well as random forest separately. As far as the deployed data is concerned, in terms of measurement of F and efficiency, random forest has shown the better results. For the objective of extraction of genes and other pertinent models, deletion of features has been performed with the deployment of iterative algorithm as proposed earlier CAMUR with modified version. Finally, the extracted results are facilitated to biologist, so they can analyzed the extraction is related or either can be a driver of cancer.
Preventing Malicious Keyloggers using Anti-keylogger Integrated Keyboard in Mobile Banking Applications
Authors: Xuan Chow Wen , Kasmin Intan Farahana , Amin Salmiah , Zainal Nur Khairunnisha
Published: August 2024

In current mobile banking applications, users will need to have an account to access the banking services. Every time accessing mobile banking applications, users are required to login using their credentials. This will lead to a chance that cyber-attackers perform malicious keylogging activities. Although the implementation of Multi-Factor Authentication can strengthen the authentication process, there are still restrictions in certain cases. In this research paper, a new keyboard system is suggested to overcome this issue. The proposed system is trusted and verified by the bank companies and prevents users from being threatened by malicious keyloggers. This research will be using quantitative study via survey to review to gather information from respondents. The target respondents will be 300 respondents who are using mobile-banking applications regularly around Malaysia. The sampling method applied is proportionate stratified sampling. In conclusion, this study will cover the limitations in the current keyboard system and propose a new system to fulfill the gap. The recommendation for future researchers is to develop a system that could identify the nature of a keylogger, so that the keylogger used for supervising will not be removed.
Browser-Based Password Manager Using Tokenization
Authors: Pei Teoh Xin , Kasmin Intan Farahana , Zainal Abidin Zety Marlia , Vasudavan Hemalata
Published: August 2024

The browser built-in password manager makes it simply for user to manage their password credential. The purpose of this research is to propose a cloud-based browser password manager with using the browser extension and aim to achieve a high level of security. This study will employ data tokenization function to save password credential data. The methodology of the study will be carried out using survey and stratified sampling. Moreover, the target user will be those who using browser. In conclude. The research paper would be discussing the security of the browser password manager including the build-in and third-party extension and the reason of implementing the tokenization technique into the browser password manager. The future research for this proposed system is where implementing the 2FA or OTP feature to replace the manually entering if master password.
Smartphone Security Issue and Cybercrime During Covid-19
Authors: You Lee Zhao , Kasmin Intan Farahana , Zainal Abidin Zety Marlia , Vasudavan Hemalata
Published: August 2024

This research is to provide the solution on protect smartphone users from cybercrime. The issue to overcome is during covid-19 pandemic, the cases of cybercrime to smartphone users has increase with significant percentage compared to before covid-19. Stratified sampling will be used in this research and it categorized with the characteristic of the operating system like android and iOS, the data will be obtained through online survey. Human behavior has changed from physical working, physical classes, purchasing groceries, physical meeting and etc. to online based during pandemic. Where this behavior increases the chances of being attacks. Hence, the proposed system will help to enhance the security of smartphone, raise the awareness of protecting self-property and reduce the chances of being a victim of cybercrime.
Social Distance Monitoring and Face Mask Detection System
Authors: Yee Chin Xin , Arshad Amad , Abdul Halim Sithiq Hairul Aysa
Published: August 2024

The Covid-19 pandemic has caused a devastating effect toward society, economy and even education system. To prevent the Covid-19 outspread, lock-down seems to be one of the most popular solutions being carried out by governments including Malaysia. In the long run, schools will be reopening anytime when the pandemic is under control. However, the attention towards control measures in school should be paid to prevent the pandemic resurgence. Manual monitoring of the SOP is impractical towards a large population with only limited resources and task forces. To mitigate the issues, the social distancing monitoring and face mask detection system – EyeSpy has been proposed. The proposed system aims to assist teachers in monitoring SOP compliances performed by students in school, thus lowering the risk for Covid- 19 outspread. This paper also provides a comparative study of different face detections, CNN architecture on facemask classifiers. Apart from that, this research employed Rapid Application Development as the system development methodology for the development of EyeSpy. Other than that, interview and questionnaire are used as requirement gathering methods to study the feasibility of the project. A system has been developed to act as a proof of concept to underline the implementation of EyeSpy mobile application in restraining the spreading of Covid-19 in schools.
Addressing user perception and implementing Hedera Hashgraph and voice recognition into Multi-Factor Authentication (MFA) system
Authors: Sheng Wong Jie , Kasmin Intan Farahana , Amin Salmiah , Zainal Nur Khairunnisha
Published: August 2024

Multi-Factor Authentication (MFA) has been gaining popularity in recent years for offering extra layers of protection to secure user accounts. Most of the integration of MFA today includes a OTP to be sent through a Short Message Service (SMS) identified by a user’s phone number or through an MFA application. However, MFA executions like these still possess underlying vulnerabilities like phishing attacks. The aim of this research is to propose an effective Software Engineering solution to decrease the number of successful attacks on user accounts during the MFA process. The proposed system suggests a first-level authentication in the form of textbox for users to input their current location of access. This can prompt the user naturally to always pay attention to the user information provided before they continue on with the MFA process. This can help users to identify a threat and realise an attempted phishing attack before giving the full access to the attackers if proceeded with the MFA process. This paper proposes a solution that takes in voice recognition biometric alongside the traditional OTP. An improved distributed ledger technology in the form of Hedera Hashgraph is also proposed which addresses efficiency and security shortcomings of Blockchains like the Ethereum Blockchain. For this research, self-selection sampling was carried out and 5 people were chosen to attend a one-to-one online interview session.
Mobile Application for Cybersecurity Education and Awareness since COVID-19 Pandemic
Authors: Yin Tang Shi , Kasmin Intan Farahana , Zainal Abidin Zety Marlia , Vasudavan Hemalata
Published: August 2024

Many schools, businesses and even governments have shifted to digital platforms and digital solutions since the COVID-19 pandemic. However, public awareness of cybersecurity and cyberattacks remains low. As the public rapidly instals remote systems and networks to enable online learning and remote working, cybercriminals are utilising a rising number of security breaches to steal data and make profits. In this paper, an effective method is proposed to educate users in the field of cybersecurity through mobile applications for their benefits. This study will adopt the stratified sampling method, and quantitative research of questionnaire will be the data collection method. The public will be the target user, with 120 respondents involve. In conclusion, this research paper would examine how to benefits users with knowledges about cybersecurity through mobile applications. The future recommendation for this proposed system is to provide an interface for users to ask questions and discuss their troubles with cybersecurity experts.
How Big Data is Shaping Healthcare Dynamics Across USA
Authors: Kellerton Thomas Oliver , Smith Meredith Claire
Published: August 2024

Background: The implementation of Big Data analytics in healthcare has astounding opportunity for enhancing the quality of care, minimizing error and waste, and lowering the price of care.
Purpose: This systematic comment of literature goals to figure out the range of Big Data analytics in healthcare such as its challenges and applications in the adoption of its in healthcare. Additionally, it intends to determine the strategies to conquer the challenges.
Data sources: An organized search of the posts was performed on ﬁve main scientiﬁc databases: ScienceDirect, Emerald, and Francis and Taylor. The content articles on Big Data analytics in healthcare posted from January 2017 to January 2022 have been considered.
Data extraction: 2 reviewers independently extracted info on deﬁnitions of Big Data analytics; applications and sources of Big Data analytics in medical field; strategies and challenges to conquer the difficulties in healthcare.
Results: A absolute of fifty-eight articles have been selected as per the addition requirements and examined. The analyses of these posts located that: researchers don't have consensus concerning the functional deﬁnition of Big Data in deep medical. Big Data analytics ﬁnds the application of its for medical choice support; optimization of medical operations and reduction of price of care chief struggle in deep adoption of Big Data analytics is non availability of proof of its sensible beneﬁts in healthcare.
Conclusion: This review analysis unveils that there's a paucity of info on proof of real world utilization of Big Data analytics in healthcare.
Using Machine Learning and Analytics for Efficient Manufacturing Process
Authors: Chiara Damaris Edel , Gonsalez Alberto Valentino , Maria Antonellia Celeste
Published: August 2024

Nowadays, we're unquestionably in the era of information. Big Data Analytics is not a point of view for those level of the business. This's of particular interest in the manufacturing activity with the high capital intensity of theirs, time constraints and provided the massive amount of information now captured. Nevertheless, there's a paucity in previous literature on BDA developing much better understanding of the abilities of the strategic ramifications to acquire value from BDA. In that vein, the main goal of this particular paper is usually to create a novel design which summarizes the primary abilities of BDA in the context of production procedure. This's completed by relying on the results of an evaluation of the continuing exploration in addition to a many case study in a prominent phosphates derivatives manufacturer to mention the abilities of BDA in manufacturing process as well as outline recommendations to advance exploration in the industry. The results are going to help businesses to recognize the major data analytics abilities and the potential implications of its for the manufacturing process of theirs and support them seeking to develop better BDA enabler infrastructure.
Large Scale Supply Chain Innovation in Canadian Wineries
Authors: Pasiely Michelle Harper , Pierce Kylo Elizah
Published: August 2024

With big data analytics rising in popularity, academics practitioners have been thinking about the ways through which they are able to get the shifts these solutions bring into the competitive strategies. Drawing on the resource-based point of view, capabilities, and on the latest literature on big data analytics, this particular analysis examines the indirect connection in between a big data analytics capability and two kinds of development abilities, radical and incremental. The study extends existing investigation by proposing BDACs enable firms to produce insight that may help strengthen the dynamic capabilities, which positively influence radical and incremental innovation capabilities. In order to test our proposed hypothesis, we used survey information from 185 chief officers and the managers operating in Italian firms. By way of partial least squares structural equation modeling, outcomes verify our assumptions about the indirect impact which BDACs have on development abilities. Particularly, we discover that dynamic abilities fully mediate the result on both radical and incremental innovation capabilities. Additionally, under conditions of higher environmentally friendly heterogeneity, the effect of BDAC's on powerful features, and in sequence, incremental innovation ability is improved, while under conditions of high environmentally friendly dynamism the impact of powerful abilities on incremental innovation abilities is amplified.
How Machine Learning Can Drive High Frequency Algorithmic Trading for Technology Stocks
Authors: Sazu Mesbaul Haque
Published: August 2024

The objective of this paper is to present an innovative method, based on deep machine learning (DRL), to resolve the algorithmic trading issue of figuring out the perfect trading place at any time during a trading activity on the stock market. It presents a new DRL trading policy to maximize the Sharpe ratio performance indicator over a wide range of stock markets. Named the Trading Deep Q-Network algorithm (TDN), the famous DQN algorithm influences this new DRL approach and considerably adapts to the particular algorithmic trading issue in front of us. Training of the ensuing machine learning (RL) agent is completely based on the development of artificial trajectories from a small set of historical data on the stock market. The paper additionally proposes a new, much more rigorous performance assessment method to objectively evaluate the performance of trading methods. Promising results for the TDN algorithm are reported adhering to this new approach to performance evaluation.
Prediction of Customer Sentiment Based on Online Reviews Using Machine Learning Algorithms
Authors: Guru Chinmayee , Bajnaid Walaa
Published: August 2024

Customer opinions and feedback play a pivotal role in enhancing business operations and decision-making processes. Sentiment analysis is a crucial technique used to decipher customer opinions from their feedback and thus provide valuable insights for businesses. However, analysing and understanding reviews is an intricate process and prone to be misleading if not conducted meticulously. This study aims to extract and classify customer emotions from e-commerce reviews of women’s clothing in terms of polarity of sentiment, enhancing sentiment analysis accuracy by means of machine learning (ML) classifiers. In addition, the study addresses the challenge of imbalanced data samples. Several supervised ML models, including Naïve Bayes, Random Forest, Support Vector Classifier (SVC) and Extreme Gradient Boosting (XGB), were employed for sentiment analysis. The study also attempts to deal with negations, and pre-processing steps were implemented to reduce the dimensionality and noise of the raw text. In SVC and XGB models, negation handling significantly improved the precision and recall values of minority classes. Moreover, a hybrid class balancing approach and the synthetic minority oversampling technique (SMOTE) were adopted. The findings indicate that the XGB classifier, combined with SMOTE sampling, produced the most accurate results, yielding better F1 and ROC AUC scores.
Air Quality Transformation in Twelve Major Cities during Covid-19 Lockdowns: A Global Assessment
Authors: Abbas Ismail I , Khan Iftikhar , Ginige Tilak A. , Abdelkader Amor
Published: August 2024

The implementation of lockdown measures worldwide, aimed at preventing the spread of Covid-19, has temporarily improved air quality. This research paper aims to examine the impact of the lockdown period from March to May 2020 on the levels of four common air pollutants in 12 major cities, namely Delhi (India), Newcastle (UK), California (USA), Brescia (Italy), São Paulo (Brazil), Langfang (China), Madrid (Spain), Khon Kaen (Thailand), Santiago (Chile), Bogota (Colombia), Wellington (New Zealand), and Silivri (Turkey). The study analyzed the changes in average monthly concentrations of nitrogen dioxide (NO2), ozone (O3), and particulate matter (PM10 and PM2.5) during two phases: the pre-lockdown and lockdown phases. During the lockdown, all air pollutants except ozone exhibited a significant decrease. These results highlight the positive impact of reducing anthropogenic emissions on air quality during the Covid-19 lockdown. The researchers also used principal components analysis to examine the concentrations of NO2, O3, PM2.5, and PM10 from January 2018 to June 2020. The findings revealed that the 11 monitoring sites in the cities could be grouped into six clusters based on similar air pollution patterns. Overall, this study provides valuable insights that can inspire policymakers and stakeholders involved in air quality management to implement changes in environmental policies. By targeting pollution sources, it is possible to mitigate the harmful effects of air pollutants.
Detection of Hate Speech in Marathi Using Language Specific Pre-Processing
Authors: Sarode Ankur , Sultanova Nailya
Published: August 2024

The increasing accessibility of social media platforms has exponentially increased the amount of textual content on the internet. Among this textual content, there is also a rapid growth of hate speech on these social media platforms. This offensive and hateful speech are increasingly becoming a cause for self-harm, depression and even suicidal tendencies, motivating social media platforms to invest in strategies that can make social communities safer. Most of the research done on hate speech detection is done in languages like English, Spanish, French and more. There is also a good amount of work done on the Hindi language, but there is not much significant work done on the Marathi language apart from a few notable exceptions. Marathi has 83 million speakers many of whom consume Marathi social media content. This makes hate speech detection in the Marathi language a desirable opportunity to explore. How various pre-processing methods affect hate speech detection in Marathi were explored in this research. Furthermore, transfer learning was used to analyse the efficiency of multilingual hate speech detection models for the Marathi language. Finally, how hate speech detection in large texts can vary from that in shot text data was explored.
Music Generation through Transformers
Authors: Agarwal Shubhham , Sultanova Nailya
Published: August 2024

The content creator industry has been booming. After COVID-19 hit, the way content was observed changed drastically within the people using the social media platforms like Instagram, Facebook, Twitter, Tik Tok etc. There is an entire profession of “Influencers” who have become public figures due to their videos which are educational, or influencing the day to day decisions through reviews/unboxing or are just blogs/vlogs about passionate topics like traveling, food etc. This rise in video content has pushed the need for background music as well as the content creators are constantly adding in the videos with fresh, engaging music which sets the mood of the videos. True message is conveyed only when the music supporting the video matches with the mood of the viewers. The challenge is that music composition, editing etc. is a professional job and the influencers may not necessarily have those skills. Content creators can either try to create their own music which may have some transitional gaps or the quality of music may suffer or they can now buy the music through a licensing platform. In this research, transformers will be used to generate the transitional music for mixing two different sound clips.
Detecting Facial Emotion Expression to Mentor and Manage Crowds Using a Deep Convolutional Neural Network
Authors: Bajnaid Walaa , Bandaru Ratnu
Published: August 2024

Human interactions are heavily reliant on facial expressions. An individual’s emotional state can affect their safety and security in crowd places. Thus, monitoring and detecting emotions in facial expression in real time contributes to preventing potentially harmful situations. However, performing such tasks is difficult and complex and requires advanced computational methods. This study aimed to use a deep convolution neural network to detect and monitor facial emotion expressions in crowds. In addition, the effectiveness of transfer learning using VGG16, ResNet50 and Xception with DCNN models to improve accuracy was investigated. To achieve this, an FER2013 dataset with more than 35,000 images classified in terms of anger, fear, disgust, happiness, surprise, sadness and neutral was used. The results showed that transfer learning improved the accuracy and performance of an ensemble of the three models.
Balancing Accuracy and Efficiency: A Comparative Analysis of Collaborative Filtering Algorithms for Job Recommendation Systems
Authors: Khosravi Arash , Azarnik Ahmad
Published: August 2024

Recommender systems are commonly used to suggest relevant items to users, like movies or products. The digital transformation of the business sector has led to a surge in online job opportunities. This shift necessitates effective job recommendation systems to connect qualified candidates with relevant positions. This study evaluates the performance of four collaborative filtering algorithms for a job recommender system: Singular Value Decomposition (SVD),  SVD++ (SVDPP), co-clustering, and Non-Negative Matrix Factorization (NMF). We employ error rate, training time, and cross-validation performance as key evaluation metrics. Our findings reveal a trade-off between accuracy and efficiency. The co-clustering approach achieves the lowest error rates, indicating its effectiveness in recommending relevant jobs. However, this benefit potentially comes at the cost of increased training time compared to other methods. Conversely, the NMF-based model demonstrates significantly faster training times, making it computationally efficient.
Fire analysis using Sentinel-2 and Sentinel-5P data: Oil pipeline explosion near Strymba Village
Authors: Chernysh Ruslan , Stakh Mariana
Published: August 2024

Oil pipeline explosions pose a serious threat to environmental safety. The relevance of this study lies in examining the consequences of such incidents and their impact on the environment. The aim of the research was to assess the scale of the fire and the degree of air pollution by nitrogen dioxide and carbon monoxide following the pipeline explosion. The research methods included the analysis of satellite images using the normalized difference vegetation index, the normalized burn ratio, and the differenced normalized burn ratio, followed by the detection of burned areas using the thresholding method. The application of advanced Earth remote sensing methods, such as data from the Sentinel-2 and Sentinel-5P satellites, allowed for the analysis of the consequences of the oil pipeline explosion and the subsequent fire that occurred on 30.09.2023, near the Strymba Village in the Nadvirna District of Ivano-Frankivsk Region. Additionally, an analysis of harmful substance emissions into the air, obtained from the Sentinel-5P satellite, was conducted, followed by visualisation using the Python programming language and statistical analysis. The results obtained include the calculation of the fire area, which is approximately 2.5 ha, and the detection of elevated levels of nitrogen dioxide and carbon monoxide above the norm following the fire. Methods for converting concentration units obtained from satellite observations to ground-level concentrations were used. The validation of the obtained results with surface measurements confirms the study’s findings regarding nitrogen dioxide and carbon monoxide pollution. After the fire, concentrations ranged from 0.46 to 0.58 ml/m³ for nitrogen dioxide and 9.86 ml/m³ for carbon monoxide. These research results are important for identifying small fires resulting from pipeline explosions and for the practical understanding of the specifics of harmful substance emissions during such fires
Pollution of the atmosphere, soil and water resources as a result of the Russian-Ukrainian war
Authors: Matkivskyi Mykola , Taras Tetyana
Published: August 2024

Pollution of the atmosphere, soil, and water resources in Ukraine resulting from the Russian-Ukrainian war is a problem that requires immediate resolution, as the ecocide’s consequences on the environment will be long-lasting and have a global impact. The aim of the study was to analyse the impact of active military actions on the atmospheric air, water resources of Kharkiv and Kharkiv Region, and the soils of the Ivory Coast of Sviatoslav National Nature Park in Mykolaiv Region by assessing their eco-geochemical state. The study of the impact of fires on the soil properties of the Ivory Coast of Sviatoslav National Nature Park using potentiometric, luminescence-bituminological, and spectrofluorometric methods revealed that the content of polycyclic aromatic hydrocarbons (PAHs) is the highest in pyrogenic areas, where low molecular weight PAHs are predominant; high molecular weight PAHs are also present, indicating recent burning of the territory. It was found that the PAH content is lower at microelevations. The aspiration method was used to investigate the atmosphere. It was shown that the level of air pollution in Kharkiv is low at all observation points, with a level of less than 5 on the scale of the air pollution index. It was found that the largest number of exceedances in the water bodies of the Kharkiv Region was recorded for such pollutants as sulphates (recorded at 17 points), ammonium nitrogen (at 16 points), biochemical oxygen demand (at 14 points), and dissolved oxygen (at 11 points). In all the most polluted water bodies, the maximum permissible concentration exceedances were recorded for 4, 3, and 2 hydrochemical indicators. The study results can be used in practice by ecologists to develop and implement measures to improve the ecological condition of the eastern and southern regions of Ukraine
SOLUTION OF IRRATIONAL INEQUALITIES BY DIFFERENT METHODS
Authors: Ağayarov Mətləb , Sadiqov Mehman ,   Sumqayıt Dövlət Universiteti, Əliyev Fəxrəddin ,   Sumqayıt Dövlət Universiteti
Published: August 2024

The article presents the solution of different types of irrational equations using different methods. The author showed the solution of some mathematical problems using the trigonometric method. Explores ways to apply the concept of trigonometry to other sciences, including the measurement of geometric and mathematical quantities and calculations in many mathematically complex issues.