bluuebunny/crossref_metadata_2025 huggingface dataset:
Motives of water and sanitation entrepreneurs in South Africa
Authors: Mokgadi Julia Ngoepe-Ntsoane
Published: April 2024

The study aims to understand what motivates water and sanitation social entrepreneurs(SEs) in South Africa as they navigate the complex business environment of collaborating with municipalities in undertaking public service delivery. In an attempt to achieve higher sustainability in their SE business models, SE processes become risky and inevitably involve higher complexity as SE business model designers seek to also understand their business impact on the whole ecosystem. Any impact on any of the water and sanitation subsystems can stress or compromise the stability of the whole system.As such, in-depth face-to-face semi-structured interview was conducted with purposively selected social entrepreneurs in South Africa‘s Gauteng and Western Cape provinces. The study finds that SEs are motivated by compassion, religious, problem solving motives as well as the desire for self-sufficiency for sustainable business operation so as to make impact at scale. The overall mission is to change human living environments and provide sustainable alternative public services
Palladian Architecture in Thailand : Its meanings and Evolution
Authors: Wiroj Sheewasukthaworn
Published: April 2024

This research aims to study Palladian architecture in Thailand focusing on its meaning and evolution Palladian architecture is originated from an Italian architect, Andrea Palladio, in the Renaissance period. The distinctive identity is that it has beautiful proportions that are harmoniously consistent, with the style which unraveled from ancient Greco-Roman architecture. Around the 17th century, Palladian architectural style was popular in Europe. Therefore, It can be clearly reflects as a symbol of Western architecture. In 19th century, Western powers began to gain more influence in Southeast Asia as well as in Thailand. The arrival of Westerners led to the development of western architecture in many cities. The Palladian architectural style was used as a model for the design of many buildings. This study finds that Western architects worked in Thailand had applied their knowledge of classical architecture to design the buildings according to Thai’s cultures. Palladian architecture in Thailand are mostly applied for large buildings. Its layout is outstanding. The main circulation uses the central hall as a link to different functions. Palladian architecture reflected luxury, simply majestic, easy to be applied to suit the terrain and modernity equivalent to the developed western nations.
Sport and Physical Activity of Slovakia Inhabitants
Authors: Maria Luskova
Published: April 2024

The paper points out the importance of sport in a person's life in terms of positive effects on his physical and mental health, socialization aspects. The aim of the article is to analyze and identify the current level of involvement of Slovak inhabitants in physical and sports activities compared to the European average. Subsequently, to identify key problem areas that are the main obstacle to developing a healthy lifestyle together with the subsequent involvement of the population in sports and to propose measures that will support the creation of a positive relationship to the physical activities of inhabitants of all age categories.
The Misuse of Technology for Business (Organizational) Gains: A Case Study Approach of Implementing SAP-ISU Technology to Unethically Maximize Organizational Profits.
Authors: Badr-un-nisa (Nisa) Chand
Published: April 2024

Given the increasing use of Information Systems (IS) technology in organizational settings, business authors have attempted to explore the impact of IS technology on organizational efficiency and productivity. While IS technology offers various benefits to organizations, we know little about the misuse of technology by corporations to maximize their profits unethically. This paper engages with the question of how organizations ignore ethical considerations and exploit certain functions of IS technology to pursue their interests. The authors argue that exploring this area is crucial to outline the ethical use of technology within organizations. To support this research empirically, the authors conducted a qualitative exploratory case study of the implementation of SAP-ISU (Systems, Applications, and products – Industrial Solution for Utilities) technology by a large utility-providing firm in a Southeast Asian Country that misused the ‘billing function’ of the technology to overcharge its customers for personal gains. Methodologically, the authors have borrowed conceptual clarity from the abduction approach to infer the case study findings. In line with the abduction approach, the main findings of the case study data have been used to extend the ‘affordances of the technology’ framework to illustrate how the SAP-ISU technology provided ‘action possibilities’ for the organization to penalize its customers. The case study is compiled after in-depth interviews with 25 managers and 15 customers and an analysis of the secondary data available on the said topic. The qualitative data is analyzed using NVIVO software. This paper concludes that organizations must adhere to ethical compliance while using the technologies to ensure transparency and improved customer experience.
“The Teaching and Learning Experiences of Faculty and Students During the Implementation of Hyflex Modality”
Authors: Ma. Claire M. Guevara
Published: April 2024

The pandemic brought by COVID-19 has led to intense changes in the way teachers deliver their lessons and how learners participate in the discussion of the lessons. Jose Rizal University goes on full online teaching and learning modality during the first two years of the pandemic using the Canvas as its Learning Management System. For the school year 2022-2023, the school adopted the HyFlex learning modality. HyFlex sees teachers teaching students at the same time in a physical classroom and synchronously online through video-conferencing software. This study considers the learning experiences and perceptions of students and teachers from basic ang higher education. Hyflex learning. This study considers the learning experiences and perceptions of both educators and learners. The findings such as; strategic classroom setting must be explicitly planned and key enabling equipment must be considered to further enhance technology and classroom setting, more training must be conducted on Hyflex Learning -Teaching to address identified possible issues or challenges, and cogent guidelines must be provided to pupils for them to be aware of the norms in a HyFlex classroom will be used for the preparation of the full implementation of the Hyflex not only as a modality but as a pedagogy as well for the next school year.
Harmonizing Perceptions: Exploring Alignment of Numerical and Textual Evaluation Through Fine-Grained Sentiment Analysis
Authors: A. Baldelovar Myelinda
Published: April 2024

The assessment of faculty teaching effectiveness plays a pivotal role in shaping educational practices and ensuring academic excellence. While numerical evaluations provide quantifiable measures of teaching effectiveness, textual comments offer valuable qualitative insights into the faculty's instructional methods and interactions. However, discrepancies between these evaluation formats may hinder the accurate assessment of faculty performance. This study aims to determine the alignment between the numerical and textual evaluations of faculty teaching effectiveness using fine-grained sentiment analysis and determine the relationship between numerical ratings and textual comments to identify patterns of consistency or divergence. A large dataset of anonymized BSCS students’ feedback is analyzed through sentiment analysis to extract sentiments expressed in the textual comments. The study results reveal a misalignment between students’ subjective perceptions expressed in the textual comments and the numerical ratings. This misalignment arises due to several factors, such as student communication style variations, individual interpretations of rating scales, or subjective biases. This study advances faculty evaluation methodologies and offers useful recommendations for organizations looking to improve assessment procedures as well as educational institutions that want to improve the precision and efficacy of their faculty evaluation systems for better faculty development and decision-making.
Determination of the Human Resource Competency Development Goals of Indonesia in the Era of Industrial 4.0
Authors: Mas Wigrantoro Roes Setiyadi, Zahera Mega Utama Abdul Wahab Samad,
Published: April 2024

The primary objective of this research is to ascertain the various factors that impact the acquisition of human resource competencies. Specifically, the study focuses on the role of communication, collaboration, creativity, and innovation in fostering experimental competencies that can effectively address diverse challenges and opportunities while upholding the principles of integrity and equitable conduct. This research aims to ascertain the determinants that impact the formation of human resource skills. This study employs a descriptive qualitative analysis methodology to adhere to the standards of scholarly inquiry. The transition from a rudimentary version of artificial intelligence to a more advanced iteration is a notable feature of the era known as Industry 4.0. This move presents both challenges and prospects for the advancement of human resources. Given the current breakthroughs in artificial intelligence (AI) technology, the findings of this study emphasize the significance of reevaluating the approach by which the development of human resources (HR) competencies is conceptualized. This suggests that there is a need to modify the necessary skills and abilities towards novel and exploratory approaches to management to attain elevated levels of communication, collaboration, creativity, and innovation.
Dental Service Cost Analysis Using Time-Driven Activity-Based Costing
Authors: Rungchat Chompu-inwai Tanawat Puangla,
Published: April 2024

This research has the objective to analyze dental service costs of the case study dental clinic using Time-Driven Activity-Based Costing (TDABC). The study was conducted in teaching clinic where dental services were provided by undergraduate-level dentistry students under the supervision of the teachers of Dentistry Faculty, and where there were also dental assistants on hand to provide service support. The research studied only five treatment procedures with a maximum frequency of service use, cumulatively totaling 80%, according to the Pareto principle. The research started from studying various activities in the processes of providing each category of dentistry service. Then, the dental service costs using TDABC were analyzed by estimating from two key parameters: the capacity cost rate and the time taken for each activity of the treatment procedure. The study found that Sealant Compounds, Tooth Extraction, Preventive Resin Restoration, Composite Fillings and Stainless Steel Crown had treatment costs of 623, 594, 669, 642 and 761 Thai Baht/treatment respectively. This research enabled the case study dental clinic to identify its costs for each treatment procedure, and also calculate profit and loss by comparison with income from service users. These data could also be used to determine more appropriate service charges for each category of treatment.
Performance and QoS Assessment in WSN: Comparative Analysis of MBC, ECHERP, and PDCH Routing Methods
Authors: Sangeeta Vhatkar
Published: April 2024

Objective: The objective of this research is to assess the performance and Quality of Service (QoS) provided by PDCH when compared with ECHERP and MBC routing protocols(RPs) within Wireless Sensor Networks (WSNs). Methods: Utilizing the NS2 Simulator, this paper provides an in-depth exploration of three hierarchical RPs viz. Equalized Cluster Head Election RP (ECHERP), Mobility Based Clustering (MBC), and PDCH (PEGASIS with Double Cluster head). A thorough examination of each protocol's performance is conducted, focusing on a range of QoS metrics such as Energy Consumption, Packet Drop Ratio, Throughput, and Delay. Findings: A comparative assessment is conducted between PDCH, ECHERP, and MBC, extracting QoS parameters. The examination and findings reveal that PDCH exhibits superior performance over ECHERP by 26.15%, 20%, 15.71%, and 37.73%. Similarly, PDCH outperforms MBC by 23.19%, 14.29%, 4.87%, and 15.13% across the mentioned parameters. Novelty: In this simulation, the NS2 software is utilized, with a specific emphasis on the suggested parameters. These metrics are crucial for evaluating the overall performance of different RPs in Wireless Sensor Networks (WSNs).
Gender Responsiveness in the Philippine Basic Education Context: Priority Thrusts and Initiatives in the Schools Division of Batangas City
Authors: Famela Q. Barairo
Published: April 2024

In 2017, the Department of Education (DepEd) issued the Gender Responsive Basic Education Policy mandating schools to pursue initiatives to address enduring and emerging gender issues and to promote gender equality.  This explanatory-sequential research explored and analyzed the gender-mainstreaming initiatives implemented in the Schools Division of Batangas City.  Based on the Gender Mainstreaming Evaluation Framework (GMEF), the assessment of 255 basic education personnel reveal that the gender-mainstreaming in their Schools Division of Batangas City is currently at the GAD application (Level III).  The basic education personnel know the various GAD initiatives institutionalized within the organization and the strategic approach to implementing GAD-related activities.  The results and findings from the real-time Delphi survey and focus group discussion underscore the importance of targeted interventions in curriculum development, pedagogy, learning environments, research, capacity building, and knowledge management.  The study recommends strengthening awareness programs, addressing knowledge gaps, implementing real-time monitoring and evaluation mechanisms, and fostering collaborative partnerships to enhance gender responsiveness within the Schools Division of Batangas City.  These recommendations aim to foster an inclusive and equitable educational environment that supports the holistic development of all students, aligning with the principles of the Gender Responsive Basic Education Policy.
Enhancing Threat Prioritization In Cybersecurity Using Avian Shield Optimizer (ASO) In Graph Forge Elite (GFE) Framework
Authors: R. Vinoth Kumar
Published: April 2024

In the ever-evolving field of cybersecurity, the ability to effectively prioritize threats is essential for organizations to allocate resources wisely and proactively mitigate potential risks. Avian Shield Optimizer (ASO) is a revolutionary optimization technique specifically designed for threat prioritization. ASO utilizes evolutionary-inspired mechanisms to dynamically adjust the prioritization of threats based on a comprehensive analysis of factors such as severity, relevance, and potential impact on organizational assets. By continuously monitoring and adapting to the dynamic threat landscape, ASO enables organizations to anticipate and respond quickly to emerging threats, ensuring that resources are allocated appropriately to address the most critical vulnerabilities. Moreover, ASO seamlessly integrates with existing threat intelligence platforms, providing cybersecurity teams with actionable insights and recommendations to comprehensively strengthen their defense strategies. Concurrently, the GraphForgeElite framework enhances this approach by facilitating the analysis of complex network data connections and structures, enabling the detection of subtle irregularities that may indicate potential cyber threats. ASO collaborates synergistically with GraphForgeElite, utilizing evolutionary-inspired methodologies to dynamically adapt the framework's design and settings, automatically tuning the parameters. Through rigorous experimentation, the performance of ASO-GraphForgeElite's Network is compared to other state-of-the-art classifiers. The results demonstrate the superior performance of ASO-GraphForgeElite's Network, surpassing 99% accuracy, precision, recall, and F1-score. Additionally, the framework exhibits efficiency in handling complex network data structures, enabling the identification of subtle patterns that indicate potential threats.
Enhancing the Barangay Information System by Integrating Geolocation Technology and Tagalog Chatbot using NLP Algorithm for Efficient Community Engagement and Service
Authors: Joseph Allen A. Cuevas
Published: April 2024

This research aims to address the need for improved efficiency and accessibility of a local government constitution which is the barangay services in the Philippines through the integration of an English – Tagalog Chatbot using NLP Algorithm into a Progressive Web-Application based Barangay Information System and Geolocation Technology which detects the location of the affected residents and nearest evacuation centers. The specific objectives include developing an information system for efficient resident data management, training an AI chatbot to handle both English and Tagalog inquiries, and integrating geolocation technology for disaster risk reduction monitoring. The study’s significance lies in enhancing the service provided by barangays, which are often the most accessible government agencies to the public. The system benefits the public by increasing transparency and providing convenient access to services, barangay officials by streamlining transactions and reducing their workload and future researchers by laying the groundwork for further technological advancements in governance. The overall acceptance of the system based on user’s feedback falls within the satisfactory category, which indicates its potential to improve barangay operations. The Barangay Information System, as demonstrated in this study, efficiently manages resident information, streamlines the delivery of certificates, enhances user experience, and ultimately contributes to more effective and efficient community service delivery.
ABC-WOA Optimization Strategy for UPQC Fuzzy PI Tuning
Authors: Sriramula Srimatha
Published: April 2024

Growing numbers of power electronics-based non-linear loads have raised worries about power quality (PQ) problems, which include equipment failures, overheating, and unplanned shutdowns. Power filters have traditionally been used for harmonics elimination, but their limitations in dynamic compensation and size present challenges. The present research presents a new methodology using the Artificial Bee Colony - Whale Optimizations Algorithm (ABC-WOA) to optimize the Unified Power Quality Conditioner (UPQC) controller. Assignment aims to tackle obstacles that come with it. The suggested algorithm's efficacy is clear, as shown from the stabilization of objective function values. Highlighting the expertise of the Proportional-Integral (PI) controller in effectively regulating the DC-link voltage, this study successfully addresses power quality concerns. The analysis of voltage injection emphasizes the significant influence of the controller on the power system, showcasing seamless integration and enhanced power quality. This research provides valuable insights into the application of advanced control algorithms in power systems, specifically in addressing challenges related to non-linear loads and transient conditions.
Securing the Patient’s Breast Cancer Data using Blockchain-based IBE with Deep Learning Model in IoT
Authors: D. Baby Sathiya
Published: April 2024

Computational intelligence (CI) and artificial intelligence (AI) have a portion to offer in the way of making healthcare systems smart and long-lasting. These technological advances have the potential to lessen our environmental impact while simultaneously increasing expectations for performance. The use of electronic health monitoring systems in healthcare management has been crucial. E-health has the potential to offer patients useful and efficient tracking tools. However, the existing E-Health system has protection disputes. However, there are safety concerns with the present online healthcare system. Doctors with bad intentions might collude with CSPs to compromise their patients' electronic health records (EHRs) or quickly leak EHR information to other opponents for financial gain. Patients might be manipulated by clinicians who work in collusion with a Patient Healthcare Monitoring Service Provider (PHMSP). EHRs for financial gain or disclose the HER content of EHRs directly to other adversaries. Recently, block-chain has emerged as one of the most potent strategies for maintaining privacy and security. The current security issues in e-health monitoring systems are expected to be replaced by this promising security method. To prevent unauthorised parties from accessing encrypted data, blockchain uses encryption. Using a computational intelligence approach, this study presented a blockchain-based encryption framework based on identity-based encryption (IBE) to deliver secure solutions. Furthermore, the study effort utilises optimised deep learning (ODL) to forecast the patient's condition from breast cancer input data. The suggested method improves performance, with an accuracy of 0.93 during training and 0.91 during validation.
The Effects of Different VR Designs for An Instructional Lesson on Students Performance and Motivation: A Design-Based Research
Authors: Azeer Al Zadjali
Published: April 2024

The purpose of this study is to define design guidelines for instructional virtual reality (VR) and examine how three different VR designs affect students' motivation and performance. Pre-post tests measuring performance and the modified Reduced Instructional Materials Motivation Survey (RIMMS) measuring motivation were used to evaluate the effects of various VR designs. 29 students were split up into three instructional VR groups. The findings revealed no significant differences among the three student groups in terms of the performance variable. However, notable differences were observed in the motivation variable. The findings of this study highlight that although students may learn identically from a variety of VR designs, their motivation levels differ depending on the design. This study emphasizes the significance of taking motivational factors into account when implementing virtual reality (VR) in educational contexts, acknowledging the role that motivation plays in fostering long-term and enhanced learning experiences.
Evaluation of Common Security Vulnerabilities of State Universities and Colleges Websites Based on OWASP
Authors: Carlos P. Flores Jr.
Published: April 2024

The security of state universities' and colleges' websites in the Philippines is vital because they play a critical role in delivering education and information to a wide variety of users. However, these institutions are also exposed to several security flaws due to their growing reliance on digital platforms. The objective of this study is to analyze security vulnerabilities in state universities and colleges websites, utilizing the OWASP Zed Attack Proxy (ZAP), an open-source tool. By adhering to the Open Web Application Security Project (OWASP) Top 10, we can identify potential hazards and suggest appropriate measures to mitigate risks. The steps of the test include gathering data about the test target, using OWASP ZAP to do automatic scanning, exploitation of the scan results, reporting, and offering recommendations. Seventeen (17) SUCs were examined, and the results show that 23.53% are vulnerable to injection, 40.06% had insecure design, 70.59% had outdated components, 88.24% have security misconfiguration, and 94.12% are vulnerable to Broken Access Control.  Malicious actors use these vulnerabilities to obtain unauthorized access to software, networks, and systems. By raising the privileges and granting the user ID additional access inside the ecosystem, it can harm the availability, confidentiality, or integrity of data. SUCs should embrace the OWASP Top 10 and begin the process of ensuring that the risks associated with their websites are minimized.
Issues and Challenges Encountered by Police Officers in Automated Fingerprint Identification System Operation
Authors: Jarold Kinsman B. Alagos
Published: April 2024

Automated Fingerprint Identification Systems (AFIS) play a vital role in modern law enforcement by aiding in the identification and apprehension of criminals through fingerprint analysis. However, despite their significance, challenges in operating AFIS remain the main hindrance to its effective utilization. Thus, this study aims to investigate the issues and challenges faced by police officers in the Philippines with AFIS. Employing a convergent parallel mixed methods research design, quantitative data were collected through a survey questionnaire, while qualitative data were obtained through in-depth interviews. The study participants comprised six experienced Filipino police officers purposively chosen for their exposure to AFIS operations. The study uses the mixed method. Descriptive statistics and Mann-Whitney U Test were used during quantitative data analysis, while thematic analysis was used in analyzing qualitative data. The study findings revealed that the top challenge faced by police officers in the Philippines is comparing and storing fingerprints in the AFIS database, primarily due to insufficient training and technical support. Themes such as regular training, technical support, and continuous learning emerged to address the challenges related to fingerprint quality, technical issues, storage limitations, and hardware problems. The study also revealed that Filipino police officers encounter moderate challenges when operating AFIS, with variations depending on sex, age, and length of service. In conclusion, the study underscores the significance of continuous training, technical support, and effective communication for successful AFIS operations. Law enforcement agencies are urged to prioritize providing proper training and technical support to ensure accurate and reliable utilization of this technology in policing.
Customer Engagement and the Corporate Sustainability Reputation of UNIQLO Impact Consumer’s Purchase Intention as Mediated by Customer Satisfaction
Authors: Harvey T. Ong
Published: April 2024

The ongoing debate on “fast fashion” and environmental impact has created a great divide amongst fashion industry players as well as consumers. The two sides of the narrative posits the upsides to retail manufacturing and consumer accessibility, whereas the other offers responsible consumption with long-term impact. Where fast fashion is defined as clothing manufactured at a rapid production cycle—from design to retail—-for the purpose of creating or leveraging trends. This innovation in the supply chain has enabled clothing companies to introduce capsule collections over a two-week period. The United Nations Environment Programme (2019) published an article citing the fashion industry as responsible for two to eight percent of global carbon emissions, and with implications expanding to a social context. With cognizance of the United Nation’s Sustainable Development Goal (UN SDG) 12, the conversation leads to a study on sustainable consumption and production patterns. One such company now participating in this conversation is UNIQLO. Considered among global players in the fast fashion sector, the company has decided to consciously depart from this approach towards a more sustainable model. This study focused on an investigation of the customer engagement and the corporate sustainability reputation of UNIQLO consumers as mediated by customer satisfaction. The main concepts include two independent variables of customer engagement, the dependent variable of purchase intention, and the mediator of customer satisfaction. The researchers intend to take a quantitative approach via survey questionnaire, adapting items from measurements scales in previous studies. A total of 449 responses will be obtained from students from Metro Manila, Philippines. The data obtained will undergo statistical tests, including Cronbach’s alpha, Pearson’s correlation, simple linear regression, multiple linear regression, and mediation analysis. The results highlighted, firstly, that the variables of customer engagement, corporate sustainability reputation, and customer satisfaction significantly correlate and impact purchase intention. Secondly, it indicated that customer engagement and corporate sustainability reputation can substantially influence purchase intention. And, thirdly, the observation on customer satisfaction partially mediating the relationship between customer engagement and corporate sustainability reputation on purchase intention. As we accelerate towards the United Nation’s call to action on the SDGs come 2030, this study may bear significance to fashion industry practitioners, consumers, the Academe, on legislation, and the UN Alliance for Sustainable Fashion towards shifting the narrative on the fashion sector.
Brain Damage Detection Using Ensemble Classifier for Children Health Progress
Authors: B. Kayathri
Published: April 2024

Magnetic Resonance Imaging (MRI) is a medical imaging technique used to detect brain damage, particularly in children. Previous classification techniques for brain damage had low accuracy, complex edge detection, and high noise levels. In this proposed work, use an ensemble classification technique to analyze MRI brain images of 6-10 year old children. The input data is preprocessed using a Median filter to remove noise and improve image quality before being sent to a segmentation technique. Each part of the segmented image, such as tissue, inner layer, overall shape, and texture, is classified based on pixel size. The classification is done using an ensemble of Support Vector Machines (SVM) and K-Nearest Neighbors (KNN) techniques. The ensemble prediction is multiplied with several input values to classify the MRI image, aggregating different layer values. The output classified image is validated and developed using Mat Lab software simulation, with an accuracy of 97.86%. This method can accurately classify various types of brain damage and aid in early detection in children.
Diabetes Mellitus prediction using Improved Chaotic Whale Optimization and Data mining techniques
Authors: Anand Viswanathan
Published: April 2024

Automation of disease detection is now prevalent in healthcare systems. Diabetes mellitus is a serious issue that has become prominent worldwide. It is a hereditary illness that impairs human existence at every stage of development. Each year, millions more people develop diabetes, which also has an impact on young people. Currently, the medical industry is shifting toward automation, as the process of identifying diseases needs periodic human verification. A single model for diabetes prediction is insufficient for complicated problems since it might not be appropriate for training huge data. In this proposed approach, a novel method which combines the outcomes of more than one algorithm is suggested. An ensemble approach using data mining techniques such as Decision Trees, Random Forests, AdaBoost, Gradient Boosting and XGBoost is employed to predict diabetes mellitus. Feature selection methods such as Averaged Fisher score and Kolmogorov-Smirnov score are used to choose the prominent characteristics from PIMA Indian Diabetes dataset. Further, the most optimal features are selected using Improved Chaotic Whale Optimization algorithm. The proposed approach produces predictions that are 98.8% accurate and reliable.
Fuzzy Logic In The Digital Travel Era: Enhancing Tourist Experience Through Comprehensive Hotel Reviews And Sentiment Analysis
Authors: Sripriya Arunachalam
Published: April 2024

In today's society, social media is becoming a more and more important part of our everyday lives as it continues to evolve. Lot of of users assess and tourist rate attractions daily, and hotels play a significant part in travel. Following reviews can be implicated to fuzzy logic, which will be beneficial for resolving the fame of tourist attractions and hotel reviews. Tourists can choose their accommodation and travel destinations with ease based on the outcome of the fuzzy control system. In this research, fuzzy logic has been used to implement sentiment analysis. To provide precise pass on relating to a few aspects chosen by the end user, the system uses attributes including food quality, service, locations, and ratings. This method enables customers to assess a certain hotel service's quality using a variety of factors. Additionally, a user can compare various hotels in depth. To accomplish this, a membership task that specifies how a smooth value maps to the term on a scale of 0 to 1 is used in the proposed system of fuzzy logic. It essentially outlines "how good" something is and how to produce complex behaviour from a condensed, understandable set of professional guidelines before passing these variables to a fuzzy control system. Using a set of rules, a fuzzy control system connects fuzzy variables. These laws are merely mappings that explain the relationships between one or more fuzzy variables. After specifying the inputs and obtaining the priority of the alternatives, the control system is finally simulated. As food preferences, service quality, and user-specific amounts vary, the system is scaled for each individual user. As a result, the system offers more accurate outcomes and increased classification method accuracy for the supplied review data set. Future research will use the Trapezoidal Fuzzy Number (TpFNs) as an evaluation tool to reflect clearly the variation of an element from 0 to 1 in order to increase the model's accuracy.
Blockchain-based Scalable IoT Cloud for Electronic Health Record using Horizontal and Vertical Mining
Authors: K. Lino Fathima Chinna Rani
Published: April 2024

The twin technology of Blockchain with IoT(BIoT) deployed in healthcare systems has fetched out the mutative potentiality for accessing and managing Electronic Health Records(EHRs). However, this divergence also has considerable challenges, primarily in terms of the protection of data, scalability, and interoperability. To overcome these challenges, this research paper presents a new approach, leveraging Blockchain technology and mining mechanisms, to launch a secure, scalable, and proficient IoT cloud ecosystem for the administration of Electronic Health Records. This research study includes Blockchain scalable IoT cloud architecture intended to address the scalability problem of storing and managing large amounts of EHR records produced through IoT sensors. The proposed mechanism implements both horizontal and vertical mining concepts for the optimization of data storage as well as the retrieval processes. Horizontal mining concentrates on allocating and distributing Electronic Health Records data to more than one node, which can ultimately sink the overload on each node in the network and increase the scalability. Vertical mining, on the other flip of the coin, prioritizes the transactions, and optimizing the data with each transaction can drastically improve the acceleration speed of retrieving the data access competency. Blockchain technology constructs the core technology of BEHRHVM, assuring data integrity, privacy, and transparency. All the EHR record transactions are authorized as mutable blocks on the Blockchain framework. This mechanism improved patient privacy while accessing the secure distribution of EHRs among authorized health providers. In addition, the proposed system is built to integrate with current healthcare systems and the Internet of Things.
Modified Adaptive CNN for Deep Learning based Histopathological Image Analysis for Cancer Diagnosis
Authors: V. Purushothaman
Published: April 2024

This research on cancer diagnosis is motivated by the need for more accurate and efficient diagnostic tools in the field of oncology. Traditional methods of histopathological examination are time-consuming and can be subject to human error. The adoption of deep learning techniques, particularly Convolutional Neural Networks (CNNs), presents an opportunity to significantly improve the precision and speed of cancer diagnosis through automated analysis of tissue samples. This research addresses current challenges, including variability in samples and limited dataset diversity, with the goal of enhancing the reliability and applicability of the developed models. The ultimate aim is to contribute to the advancement of diagnostic methodologies, providing clinicians with more reliable tools for early and accurate cancer detection and ultimately improving patient outcomes and treatment strategies. Using a modified adaptive CNN algorithm, trained on extensive pathology image datasets, the approach enhances the precision and speed of cancer cell detection in tissue samples. By utilizing these artificial intelligence tools, the diagnostic process becomes not only more accurate but also significantly expeditious. Despite the promising strides, certain challenges persist. Variability in tissue samples, limited availability of diverse datasets, and interpretability of deep learning models pose hurdles to widespread adoption. To address these challenges, this research focuses on developing robust models that can generalize well across diverse datasets. Additionally, efforts are being made to create more comprehensive and diverse datasets to improve model training. Results show accuracy exceeding 97%, sensitivity and specificity at 97% and 95%, F1 score reaching 96%, and precision at 96%.
Cervical Cancer Detection Using Gabor Featured Convolutional Neural Networks
Authors: D.Baskar
Published: April 2024

Cervical cancer is identified as the most killing disease in women patients from past two decades. Even though various medical treatments are available at present, its death rate is not reduced. Hence, various computational and automated methods are proposed to improve the medical treatments as early as possible at an earlier stage. From the past decades, machine and deep learning methods are used for detecting the cervical cancer images. The deep learning algorithms provide superior results for the cervical cancer detection system. Therefore, Gabor Featured Convolutional Neural Networks (GFCNN) is proposed to detect the cancer regions in cervical images. It proposes Cervical Cancer Detection System (CCDS) is significantly analyzed by applying the methodologies on the cervical images which are available in Guanacaste Dataset. The proposed CCDS is constructed with the following modules Gabor transforms, Feature computations, Classification and Segmentation. The Gabor transform transforms the pixel coordinates and then intrinsic variation features are determined. These features are classified by the GFCNN classifier.  The GFCNN method is tested on the set of cervical images in Guanacaste Dataset and its performance is analyzed with respect to precision, recall and cancer accuracy rate. The proposed CCDs method achieved 98.42% of PN, 98.54% of RL and 98.45% of AY. The results of GFCNN method for cervical cancer detection and segmentation system are compared with other state-of-the-art methods.
Power-Efficient Dispatching of Time-Sensitive Requests in the Fog-Enabled Industrial Internet of Things
Authors: Huda Qasim ALGawwam
Published: April 2024

Recent advancements in service offerings and the emergence of new application areas have reveal the limitations of cloud computing as a solely solution. One of these new areas is the industrial internet of things, where industrial processes are controlled in a network-based and automated way which brings many new challenges. To solve the challenges, fog computing has been introduced as a complementary paradigm to the cloud. Resource management is one of those challenges need further investigation. The industrial internet of things by its own characteristics has led traditional methods to be inefficient within this evolving ecosystem. Therefore, fully utilizing potential of the new system necessitates effective resource management. This manuscript aims to investigate the problem of resource management in the emerging cloud-fog ecosystem of the industrial internet of things. Considering the dynamic nature of users, this study proposes the algorithms for dynamic resource allocation and provisioning based on deadlines. We formulate the problem as a multiple criteria stochastic problem and introduced an online solution. Our proposed solution takes into account the unique characteristics of Fog computing and the dynamic behavior of users. To effectively handle the scheduling of requests, the research suggests utilizing the Lyapunov optimization method and Lyapunov drift theory. The Lyapunov method helps to merge and measure all the related objectives in terms drift. The objective function is optimized, in each time slot, using a drift-plus-penalty weight parameter. By adopting this approach, the system can operate efficiently while meeting the constraints and performance requirements of IoT applications.
Design of an Iterative Method for Enhanced Multimodal Time Series Analysis Using Graph Attention Networks, Variational Graph Autoencoders, and Transfer Learning
Authors: Vijaya Kamble
Published: April 2024

In the ever-evolving landscape of data analysis, the need to efficiently and accurately interpret multimodal time series data has become paramount. Traditional methods often fall short in addressing the complex dependencies and dynamics inherent in such data, limiting their effectiveness in real-world applications. This work introduces a comprehensive approach that leverages Graph Attention Networks (GATs), Variational Graph Autoencoders (VGAEs), transfer learning with pretrained transformers, and Bayesian state-space models to overcome these limitations. GATs are selected for their ability to dynamically focus on relevant modalities through attention mechanisms, thereby capturing the intricate relationships between different data modalities. This method significantly enhances the model's ability to integrate multimodal information, leading to notable improvements in classification, prediction, and anomaly detection tasks. VGAEs are utilized to learn latent representations within a graph-based framework, promoting unsupervised learning while unveiling the underlying data structure. The resultant embeddings are pivotal for downstream tasks like clustering and visualization, encapsulating the interactions within multimodal time series data effectively. Furthermore, this work incorporates transfer learning with pretrained transformers to harness extensive knowledge from large datasets, adapting it to multimodal time series analysis. This strategy excels in capturing long-range dependencies, thereby augmenting generalization and performance in data-scarce scenarios. Bayesian state-space models are employed to elucidate the temporal dynamics and uncertainties of time series data, offering a robust framework for probabilistic inference and enhancing the interpretability and reliability of forecasting and anomaly detection. The efficacy of the proposed model is rigorously evaluated using diverse datasets, including the Yahoo! Stock Dataset, Forest Cover Dataset, and an empirical collection of 100k time series data samples. The results demonstrate a significant leap in performance metrics, including a 9.5% increase in precision, 8.5% boost in accuracy, 8.3% rise in recall, 10.4% reduction in delay, 9.4% enhancement in AUC, and a 5.9% improvement in specificity, alongside superior pre-emption capabilities compared to existing methods. This work not only addresses the pressing need for advanced multimodal time series analysis techniques but also sets a new benchmark for efficiency and accuracy. The integration of GATs, VGAEs, transfer learning with pretrained transformers, and Bayesian state-space models presents a formidable approach that significantly advances the field, offering profound impacts on a wide array of applications.
Otobiyografiler Üzerinden Balkan Savaşı Sürecinde Çocukluk Algısının Değerlendirilmesi
Authors: Ak Emine , Çapar Ali
Published: April 2024

Bu makale, Balkan Savaşları boyunca çocukluk algısının siyasi, sosyal ve kültürel açıdan nasıl şekillendiğini incelemektedir. Osmanlı toplumunda, özellikle 19. yüzyılın ikinci yarısından itibaren, barış zamanındaki çocukluk algısı ile savaş zamanındaki çocukluk algısı arasında önemli farklılıklar olmuştur. Savaş dönemindeki çocukluk algısı, siyasi gelişmelerden etkilenmiş ve bazı çocukların rejimin ideolojisi ve dönemin koşulları bağlamında militarizasyonu ile sonuçlanmıştır. Balkan Savaşları boyunca çocuklar, ebeveynlerinin sorumluluklarını büyük ölçüde üstlenmişlerdir, bu da çocukluk ile yetişkinlik arasındaki ayrımın bulanıklaşmasına neden olmuştur. Savaşın sosyal sonuçları, çocukların yaşamlarını olumsuz yönde etkilemiş ve eğitim, oyun ve hikâye anlatımı gibi alanlara sirayet etmiştir. Makale boyunca, çeşitli yöntemler kullanılarak Balkan Savaşı'nın çocukların yaşamları üzerindeki etkisi ortaya konulmaya çalışılmıştır. Çocukların bütün deneyimlerini resmi belgeler aracılığıyla yansıtmak mümkün olmadığından, onların deneyimlerini daha kapsamlı bir şekilde yansıtabilmek için anılar, biyografiler ve dönemin etkisi altında kurgulanan edebi eserler kaynak olarak tercih edilmiştir. Ayrıca, çocukluk algısını yansıtan farklı disiplinlerden çeşitli kaynakların incelenmesi nedeniyle, kaynak kullanımında disiplinler arası bir yaklaşım benimsenmiştir. Sonuç olarak, dönem politikalarının ve egemen çocukluk algısının hem kişisel anlatılarda hem de kurgusal metinlerde kendini gösterdiği görülmüştür. Bu durum, çocukluk tarihinin ve çeşitli kaynakların kullanımının önemini vurgulamakta ve alandaki potansiyel araştırma konularına örnekler sunmayı hedeflemektedir.
A Review of Possibilities and Challenges of Pit Thermal Energy Storages in Swedish District Heating Networks
Authors: Fogelström Frej , Danielski Itai , Nguyen Truong , Nair Gireesh
Published: April 2024

The use of pit thermal energy storages (PTES) enables higher solar fraction in district heating networks by counteracting the mismatch between heat demand and production in solar district heating (SDH) installations. Capital costs linked to land areas with site-specific geological conditions are the deciding factors for PTES constructions. This study investigates non-technical and technical factors for the implementation of PTES in Swedish district heating networks. Having several SDH and PTES installations in operation the country of Denmark is used as a reference. This study, based on literature review, discusses the drivers and challenges for the use of PTES in district heating networks.
SÜRDÜRÜLEBİLİR ÇEVRE İÇİN GEÇMİŞE BAKIŞ: TÜRKİYE'DE ÇEVRESEL KUZNETS EĞRİSİNİN DÖNEMSEL ANALİZİ
Authors: Ekinci Yusuf
Published: April 2024

Bu çalışmada Türkiye’de sürdürülebilir bir çevre ile ilgili neler yapılacağını görebilmek için Çevresel Kuznets Eğrisi (ÇKE) hipotezinin geçmiş yıllardaki hareketleri dönemsel olarak incelenmiştir. Analiz 1970-2022 zaman aralığını kapsamaktadır. Bu kapsamda yapısal kırılmalı en küçük kareler yöntemi kullanılarak Türkiye de GDP ve CO2 emisyonlarını etkileyen 15 kritik döneme bakılmıştır. Çalışma sonucunda elde edilen en önemli bulgu ÇKE hipotezi bir zaman aralığında U, ters-U, N gibi tek bir şekille sabit kalmayıp dönemsel olarak tüm bu grafikleri aynı anda çizebildiği yönündedir. Buna ilaveten M şeklinde grafiğin de Türkiye ekonomisinde geçerli olduğu çalışmada ulaşılan bulgular arasındadır. Yapılan analizlerde üretimle birlikte çevreye daha az CO2 emisyonu salgılanmasının, maliyetleri düşürme, kaynak bulunmasını kolaylaştırma ve üretimde çevre kirliliğini önlemeye yönelik teknolojik gelişmeleri sağlamanın yanı sıra buna ilaveten çevre kirliliğini önlemeye yönelik çevre sözleşmelerine taraf olmak vb. unsurlara bağlı olduğu da çalışmada ulaşılan sonuçlar arasında yer almaktadır. Çalışma Türkiye örnekleminde yapılan zaman serisi analizlerine kıyasla ÇKE hipotezinin 15 kritik tarihi kapsayan kırılmalı test ile dönemsel olarak incelenmesi bakımından benzeri bir çalışmaya rastlanılmaması nedeniyle ilk özelliği taşıdığı düşünülmektedir.