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Early‐stage disease detection, particularly in Point‐Of‐Care (POC) wearable formats, assumes pivotal role in advancing healthcare services and precision‐medicine. Public benefits of early detection extend beyond cost‐effectively promoting healthcare outcomes, to also include reducing the risk of comorbid diseases. Technological advancements enabling POC biomarker recognition empower discovery of new markers for various health conditions. Integration of POC wearables for biomarker detection with intelligent frameworks represents ground‐breaking innovations enabling automation of operations, conducting advanced large‐scale data analysis, generating predictive models, and facilitating remote and guided clinical decision‐making. These advancements substantially alleviate socioeconomic burdens, creating a paradigm shift in diagnostics, and revolutionizing medical assessments and technology development. This review explores critical topics and recent progress in development of 1) POC systems and wearable solutions for early disease detection and physiological monitoring, as well as 2) discussing current trends in adoption of smart technologies within clinical settings and in developing biological assays, and ultimately 3) exploring utilities of POC systems and smart platforms for biomarker discovery. Additionally, the review explores technology translation from research labs to broader applications. It also addresses associated risks, biases, and challenges of widespread Artificial Intelligence (AI) integration in diagnostics systems, while systematically outlining potential prospects, current challenges, and opportunities. [ABSTRACT FROM AUTHOR]

Copyright of Arab Journal for Scientific Publishing is the property of Research & Development of Human Recourses Center (REMAH) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This presents an innovative approach to treat diabetic foot ulcers (DFU) by leveraging Internet of Things (IoT) technology. Through a comprehensive data analysis system, DFU progress can be monitored in real time, allowing for timely intervention and improved healing outcomes. The system uses IoT devices to capture images of ulcers, identify the stage of the disease, and provide important insights into the severity of the ulcer. These images are displayed on an LCD screen and sent over the IoT network for analysis. Additionally, the system sends email notifications to healthcare providers to keep them updated on the patient's status and allow them to quickly adjust treatment plans. This continuous monitoring allows healthcare professionals to intervene quickly when needed, improving patient care and reducing strain on healthcare resources. This IoT-based approach represents a significant advance in DFU management, providing personalized treatment plans based on real-time data analysis and enabling better outcomes for patients with diabetic foot ulcers. [ABSTRACT FROM AUTHOR]

Copyright of Tikrit Journal of Engineering Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Traditional email systems can only achieve one-way communication, which means only the receiver is allowed to search for emails on the email server. In this paper, we propose a blockchain-based certificateless bidirectional authenticated searchable encryption model for a cloud email system named certificateless authenticated bidirectional searchable encryption (CL-BSE) by combining the storage function of cloud server with the communication function of email server. In the new model, not only can the data receiver search for the relevant content by generating its own trapdoor, but the data owner also can retrieve the content in the same way. Meanwhile, there are dual authentication functions in our model. First, during encryption, the data owner uses the private key to authenticate their identity, ensuring that only legal owner can generate the keyword ciphertext. Second, the blockchain verifies the data owner's identity by the received ciphertext, allowing only authorized members to store their data in the server and avoiding unnecessary storage space consumption. We obtain a formal definition of CL-BSE and formulate a specific scheme from the new system model. Then the security of the scheme is analyzed based on the formalized security model. The results demonstrate that the scheme achieves multikeyword ciphertext indistinguishability andmulti-keyword trapdoor privacy against any adversary simultaneously. In addition, performance evaluation shows that the new scheme has higher computational and communication efficiency by comparing it with some existing ones. [ABSTRACT FROM AUTHOR]

Spam emails and phishing attacks continue to pose significant challenges to email users worldwide, necessitating advanced techniques for their efficient detection and classification. In this paper, we address the persistent challenges of spam emails and phishing attacks by introducing a cutting-edge approach to email filtering. Our methodology revolves around harnessing the capabilities of advanced language models, particularly the state-of-the-art GPT-4 Large Language Model (LLM), along with BERT and RoBERTa Natural Language Processing (NLP) models. Through meticulous fine-tuning tailored for spam classification tasks, we aim to surpass the limitations of traditional spam detection systems, such as Convolutional Neural Networks (CNNs). Through an extensive literature review, experimentation, and evaluation, we demonstrate the effectiveness of our approach in accurately identifying spam and phishing emails while minimizing false positives. Our methodology showcases the potential of fine-tuning LLMs for specialized tasks like spam classification, offering enhanced protection against evolving spam and phishing attacks. This research contributes to the advancement of spam filtering techniques and lays the groundwork for robust email security systems in the face of increasingly sophisticated threats. [ABSTRACT FROM AUTHOR]

Rapid advancements in innovation, the Internet of Things (IoT), and edge computing have provided room for companies, government agencies, well-being administrations, and organizations to offer their kind of assistance through the cloud. Operating these cloud administrations requires monetary and computational assets to acquire and protect information from vindictive elements and to handle the multitude of information these administrations generate through nervous and IoT gadgets. Scalability, system availability, network transmission, and load relocation are some of the issues with the parallel file system. In the literature, few works are reviewed for obtaining load balancing but it does not consider the secure authentication phase in fog computing. Secure authentication and load balancing are critical challenges in a fog computing environment. In this paper, we develop Adaptive Load Balancing and Secure Authentication (ALBSA) for managing load balancing schemes and secure authentication schemes in fog computing. Normally, the Edge Data Centres (EDC) are utilized to set up as a distributed system and it is located among the data source and cloud datacentre. So, the proposed ALBSA is utilized to enable efficient authentication and workload management (load balancing). The proposed method is a combination of Blockchain technology and the Pelican Optimization Algorithm (POA). Based on resource utilization and response time, efficient load balancing is achieved. Additionally, this paper develops a blockchain-based authentication system that utilizes the characteristics and advantages of blockchain and smart contracts to authenticate users securely. The implemented system uses the email address, username, Ethereum address, password, and data from a biometric reader to register and authenticate users. The proposed methodology is implemented and evaluated based on performance metrics. To justify the efficiency of the proposed technique, it is contrasted with the traditional techniques. The proposed method is achieved, Encryption time is 4.2ms, the waiting time is 0.28ms and the decryption time is 10.21ms. [ABSTRACT FROM AUTHOR]

The object of this study is the modeling of radar portraits (RPs) for intelligent recognition systems based on the use of faceted 3D models. In order to solve the problems of target identification in homing systems of high-precision missile weapons, a technology is needed that could make it possible to efficiently and quickly generate RPs of military objects of complex shape in the required quantity. The research results are based on a combination of separate component technologies, in particular: the devised technology of using faceted 3D models – their construction and further processing with invisible surfaces excluded from it for an arbitrary viewing angle. The basic part of the work is the development of an algorithm and technological procedures for the formation of a spatial tracing grid for the current observation angle. A feature of the proposed technology is the application of a facet selection algorithm using an array of tracing facets and the application of the Huygens-Fresnel principle to recognize objects of complex shape. The RP database of military objects of complex shape was built. The results of modeling faceted RP’s, in particular the armored boat "Gyurza-M", are given. The results of the experimental study showed the ability to recognize the type of military object of complex shape at the level of 80‒90 %, which makes the use of this technology appropriate for recognizing military objects of complex shape. The achieved high-speed and quality characteristics of RP generation of military objects of a complex shape makes it possible to assume that the main prospective field of practical application is the identification and visual interpretation of targets in homing systems of high-precision missile weapons. [ABSTRACT FROM AUTHOR]

The article presents the discussion on proliferation of artificial intelligence (AI) technologies presents unprecedented business transformation and innovation opportunities. Topics include widespread adoption of AI also brings forth multifaceted risk, including privacy breaches, data security vulnerabilities, and ethical dilemmas; and manages the AI product life cycle to maintain consistent responsibility and protection.

Some arguments against the assumption that ordinary people may share common knowledge are sound. The apparent cost of such arguments is the rejection of scientific theories that appeal to common knowledge. My proposal is to accept the arguments without rejecting the theories. On my proposal, common knowledge is shared by ideally rational people, who are not just mathematically simple versions of ordinary people. They are qualitatively different from us, and theorizing about them does not lead to predictions about our behavior. Nevertheless, models of action that assume common knowledge have a role to play in our understanding of collective rationality. [ABSTRACT FROM AUTHOR]

Introduction: Despite the diverse roles of pharmacists in immunization, the vaccinator role remains uncharted for Thai pharmacists. This survey assesses pharmacist attitudes, knowledge, and willingness as vaccinators, providing insights to enhance their pivotal contribution to vaccination outcomes in Thailand. Methods: The survey encompassed three sections: respondent characteristics, attitudes toward the vaccinator role, and vaccination knowledge. Disseminated via the Pharmacy Council of Thailand (PCT)'s email system, it received 400 valid responses. Descriptive and inferential statistics were employed, including logistic regression to identify factors affecting willingness to become vaccinators (p < 0.05). Results: The survey was conducted between January 19, 2023, and February 28, 2023. The respondents, with an average age of 39.8 ± 10.25 years, were predominantly female (54.5%) with bachelor's degrees (68%). Hospital (45%) and community (20.7%) pharmacists were prominent, primarily in the central region (51%). Notably, 12.3% received vaccination training from PCT. Of the respondents, 169 (42.25%) were willing to become vaccinators, while 231 (57.75%) were uncertain or uninterested. Factors driving willingness differed; knowledge and skills for vaccine administration (72.59%) were crucial for those not willing to become vaccinators, while public benefits (73.91%) influenced vaccinators. In multivariate analysis, longer experience (10–19 years) (odds ratio [OR] 2.317; p = 0.009) and vaccination training (OR = 1.926; p = 0.047) were significant factors supporting the decision to become vaccinators. Average vaccination knowledge scores were 5.68 ± 2.63 out of 12 points, slightly higher in the vaccinator group (6 ± 2.3) compared to non‐vaccinators (5.4 ± 2.8) with a significant difference (p = 0.034). Post‐vaccination counseling knowledge was high, but questions related to adverse events following immunization (AEFI) had low correct response rates (12.5%). Conclusion: The study underscores substantial willingness among Thai pharmacists to assume the vaccinator role, revealing influential factors and knowledge gaps. This insight can guide training enhancements and implementation strategies to seamlessly expand pharmacist contributions to vaccination efforts. [ABSTRACT FROM AUTHOR]

Many successful researchers in the biomedical sciences have benefitted from mentors and networks earlier in their career. However, early-career researchers from minoritized and underrepresented groups do not have the same access to potential mentors and networks as many of their peers. In this article we describe how ‘cold emails’ and social media platforms – notably Twitter/X and LinkedIn – can be used to build virtual networks, and stress the need to invest in maintaining networks once they have been established. [ABSTRACT FROM AUTHOR]

In facing digital transformation challenges, universities need to set up their data governance strategies. They include effective solutions to trace and value data about key assets (such as researchers, publications, courses, research projects) scattered across multiple legacy IT systems. As part of an overall solution to deal with the unavoidable data fragmentation and diversity, we provide the complete code of a simple and very efficient framework that can be employed by universities to develop their own knowledge graph, offering a comprehensive picture of the strategic data of the university, such that it can be consistently exploited by different digital services. [ABSTRACT FROM AUTHOR]

E-mail has become a popular communication tool widely used by universities, enterprises and governments. Despite the convenience it brought to people, attacks on e-mail happen very frequently in the range of the world, causing large economic loss and occupying a mass of network bandwidth every year. The hazards from e-mail attacks underline the importance of detecting and resisting spam in an efficient and timely way. Using Python, we built Naïve Bayes (NB) and support vector machine (SVM) filters for emails. The filtering performance of NB and SVM email filters applying different kernel functions was compared and evaluated based on several evaluation indices including accuracy, precision, and total cost ratio (TCR). Also, in order to optimize the filters, the influences of stop words removal, feature numbers and other parameters in the filtering algorithms were monitored. [ABSTRACT FROM AUTHOR]

Peer-to-peer (P2P) overlay networks provide message transmission capabilities for blockchain systems. Improving data transmission efficiency in P2P networks can greatly enhance the performance of blockchain systems. However, traditional blockchain P2P networks face a common challenge where there is often amismatch between the upperlayer traffic requirements and the underlying physical network topology. This mismatch results in redundant data transmission and inefficient routing, severely constraining the scalability of blockchain systems. To address these pressing issues, we propose FPSblo, an efficient transmission method for blockchain networks. Our inspiration for FPSblo stems from the Farthest Point Sampling (FPS) algorithm, a well-established technique widely utilized in point cloud image processing. In this work, we analogize blockchain nodes to points in a point cloud image and select a representative set of nodes to prioritizemessage forwarding so that messages reach the network edge quickly and are evenly distributed. Moreover, we compare ourmodel with theKadcast transmissionmodel, which is a classic improvementmodel for blockchain P2P transmission networks, the experimental findings show that the FPSblo model reduces 34.8% of transmission redundancy and reduces the overload rate by 37.6%. By conducting experimental analysis, the FPS-BTmodel enhances the transmission capabilities of the P2P network in blockchain. [ABSTRACT FROM AUTHOR]

With the advent of digital technologies as an integral part of today's everyday life, the risk of information security breaches is increasing. Email spam, commonly known as junk email, continues to pose a significant challenge in the digital realm, inundating inboxes with unsolicited and often irrelevant messages. This relentless influx of spam not only disrupts user productivity but also raises security concerns, as it frequently serves as a vehicle for phishing attempts, malware distribution, and other cyber threats. The prevalence of spam is fueled by its low-cost dissemination and its ability to reach a wide audience, exploiting vulnerabilities in email systems. This paper marks the inception of an in-depth investigation into the viability and potential implementation of a robust spam filtering and prevention system tailored explicitly to university networks. With the escalating threat of email-based hacking attacks and the incessant deluge of spam, the need for a comprehensive and effective defense mechanism within academic institutions becomes increasingly imperative. In exploring potential solutions, this study delves into the applicability and efficacy of Bayesian filters, a class of probabilistic classifiers renowned for their aptitude in distinguishing between legitimate emails and spam messages. Bayesian filters utilize statistical algorithms to analyze email content, learning patterns and features to accurately categorize incoming emails. [ABSTRACT FROM AUTHOR]

Advancements in technology have paved the way for innovative solutions that bridge accessibility gaps for individuals with visual impairments. This paper presents a pioneering system, designed to empower visually impaired users in accessing, composing, and managing email messages with attachments using spoken language. Through seamless integration with popular email services, the application empowers users to navigate their inbox, access attachments, and stay connected in the digital realm. By bridging the gap between the visually impaired and email communication, this solution showcases the trans formative capacity of technology in nurturing inclusivity and empowering greater independence. This paper explores the conceptualization, development, and implications of this voice-driven application, underscoring its role in enabling a more connected and participatory experience for individuals with visual impairments. [ABSTRACT FROM AUTHOR]

This article explores the evolving duty of technological competence for construction lawyers. The American Bar Association and 40 states have amended their ethical rules to include this duty, while others imply it. The duty requires lawyers to keep up with changes in technology and understand its benefits and risks. The article discusses the historical development of this duty, including issues related to email, metadata, and cybersecurity. It also highlights the importance of competence in areas such as social media, cryptocurrency, and data breaches. Lawyers may need to undergo technology training to fulfill this duty. Many states have adopted rules or comments on technological competence, with Florida being the first state to require technology continuing education. North Carolina and New York have also implemented technology training requirements for attorneys. The article provides cautionary case law examples that demonstrate the importance of technology competence for lawyers, including issues related to electronic communication, preservation of electronic evidence, and court e-filing requirements. It emphasizes the ethical considerations and obligations that lawyers face in the era of technology and the need for ongoing supervision and competence in utilizing technology. The article also addresses the importance of lawyers being able to trust and supervise AI tools to ensure accurate and unbiased data interpretation. Different state rules provide variations on the requirements for supervising AI tools, with some emphasizing confidentiality and others focusing on the lawyer's duty of competence. It concludes by emphasizing the importance of lawyers staying informed about technological advancements and seeking assistance from experts when necessary. [Extracted from the article]

Today's technological developments are in accordance with the demands of Bureaucratic Reform, where every work unit must change work patterns, namely from conventional work patterns to technology-based work patterns, if previously the performance and postal posts to carry out office administration were carried out manually at this time must be done digitally. The object of this research is the East Barito State Prosecutor's Office in Tamiang Layang. Where the implementation of mail administration has run 85% with an electronic / digital system that is accommodated in the SIPEDE Application (Electronic Mail Information and Disposition System). The performance implementation system and the application of digital mail administration is the implementation of changes in performance weights that were previously physical, but now the presentation of data is virtual and online, this is an advancement in information technology. The research methods used are interviews, observations, documentation, and literature studies. The result of this study is that the waterfall method is a system method and the blackbox method is a system and questionnaire testing method. Programming languages namely PHP (hypertext Preprocessor) and MySQL as database management, then as implementers, of course, qualified human resources are needed and have IT expertise (Computer Institutions) for the implementation and successful use of the technology in question. [ABSTRACT FROM AUTHOR]

Background: During the last decade, social media have affected people's lives in an unprecedented way, today, different parts of people's lives, from shopping to education, business, and electronic messages, are influenced by these media. In fact, social media have become an integral part of human life today, and in recent years this dependence has become even greater than before. Because following the spread of the COVID-19, people used social media more to work and study, to keep in touch with their loved ones, and to get information about Covid-19. The present study aimed to determine the positive and negative impact of social media in the Covid-19 pandemic. Methods: The present study was a brief review designed. From 01 to 10 January 2022, the researchers of this study examined 6 international databases: Medline/PubMed, ProQuest, Scopus, EMBASE, Google Scholar, and ISI Web of Science. After reviewing the titles and abstracts of the articles, 46 articles entered the next stage, in which the full text of the articles was reviewed. Results: Social media provides a space where health information is easily accessible to everyone. This capability is very efficient in global emergencies such as the outbreak of the Covid-19 pandemic so the use of social networks during the Covid-19 pandemic has increased significantly compared to previous data. In today's world, social media has become a new field in business and marketing. With the involvement of social media in many aspects of life, we see its significant impact on various aspects of business, from brand development to guaranteeing buyer's trust. Conclusion: Today, the role of social media in social communication, health, economy and other cultural fields is undeniable. However, it is important to consider the negative effects of these media. It seems that it is necessary that social media users be given the principal training before and during their use so that they do not become victims of its undesirable and unconscious effects. [ABSTRACT FROM AUTHOR]

Effective spam filtering plays a crucial role in enhancing user experience by sparing them from unwanted messages. This imperative underscores the importance of safeguarding email systems, prompting scholars across diverse fields to delve deeper into this subject. The primary objective of this research is to mitigate the disruptive effects of spam on email usage by introducing improved security measures compared to existing methods. This goal can be accomplished through the development of a novel spam filtering technique designed to prevent spam from infiltrating users' inboxes. Consequently, a hybrid filtering approach that combines an information gain philter and a Wrapper Grey Wolf Optimizer feature selection algorithm with a Naive Bayes Classifier, is proposed, denoted as GWO-NBC. This research is rigorously tested using the WEKA software and the SPAMBASE dataset. Thorough performance evaluations demonstrated that the proposed approach surpasses existing solutions in terms of both security and accuracy. [ABSTRACT FROM AUTHOR]

Mail management in an organization plays a vital role in the administrative process. In this case, archiving is one of the influential factors in mail management at AKPER Rumkit Tk. III Manado. So it is hoped that managing incoming and outgoing letters can be done better, faster, and more accessible. With the application of incoming mail and outgoing mail, it can reduce the use of long enough time in filing letters and dispositions, reduce paper use, minimize the possibility of errors in recording, speed up the process of searching letters, facilitate control of mail disposition, and easy to use. This study aims to design a computerized mail filing application that can facilitate the search for archiving information quickly, accurately, and efficiently. The author uses tools such as PHP in programming and MySQL as a database for application design. The study results are applications that can manage incoming and outgoing mail and help the administrative administration manage archiving effectively and efficiently, using computer media to manage incoming and outgoing mail records. [ABSTRACT FROM AUTHOR]

Spam emails have become a pervasive issue in recent years, as internet users receive increasing amounts of unwanted or fake emails. To combat this issue, automatic spam detection methods have been proposed, which aim to classify emails into spam and non-spam categories. Machine learning techniques have been utilized for this task with considerable success. In this paper, we introduce a novel approach to spam email detection by presenting significant advancements to the Dandelion Optimizer (DO) algorithm. The DO is a relatively new nature-inspired optimization algorithm inspired by the flight of dandelion seeds. While the DO shows promise, it faces challenges, especially in high-dimensional problems such as feature selection for spam detection. Our primary contributions focus on enhancing the DO algorithm. Firstly, we introduce a new local search algorithm based on flipping (LSAF), designed to improve the DO's ability to find the best solutions. Secondly, we propose a reduction equation that streamlines the population size during algorithm execution, reducing computational complexity. To showcase the effectiveness of our modified DO algorithm, which we refer to as the Improved DO (IDO), we conduct a comprehensive evaluation using the Spam base dataset from the UCI repository. However, we emphasize that our primary objective is to advance the DO algorithm, with spam email detection serving as a case study application. Comparative analysis against several popular algorithms, including Particle Swarm Optimization (PSO), the Genetic Algorithm (GA), Generalized Normal Distribution Optimization (GNDO), the Chimp Optimization Algorithm (ChOA), the Grasshopper Optimization Algorithm (GOA), Ant Lion Optimizer (ALO), and the Dragonfly Algorithm (DA), demonstrates the superior performance of our proposed IDO algorithm. It excels in accuracy, fitness, and the number of selected features, among other metrics. Our results clearly indicate that the IDO overcomes the local optima problem commonly associated with the standard DO algorithm, owing to the incorporation of LSAF and the reduction in equation methods. In summary, our paper underscores the significant advancement made in the form of the IDO algorithm, which represents a promising approach for solving high-dimensional optimization problems, with a keen focus on practical applications in real-world systems. While we employ spam email detection as a case study, our primary contribution lies in the improved DO algorithm, which is efficient, accurate, and outperforms several state-of-the-art algorithms in various metrics. This work opens avenues for enhancing optimization techniques and their applications in machine learning. [ABSTRACT FROM AUTHOR]

For years, the labor market has been facing the problem of finding suitable employees for vacant positions. Despite a large number of unemployed persons, recruitment and selection is still a problem for companies. Today, companies face challenges in recruiting and retaining a talented workforce. New technologies bring with them new requirements and job descriptions, so the lack of qualified candidates is becoming more and more obvious. Employer branding can serve as a strategy for business organizations to position themselves on the labor market so that potential candidates will perceive them as a desirable place to work, and also for the purpose of retaining existing employees. Every business organization has its own reputation, ie. Brand, but it is important how it will be managed and what benefits could bring. [ABSTRACT FROM AUTHOR]

Monitoring is a prerequisite for evidence‐based wildlife management and conservation planning, yet conventional monitoring approaches are often ineffective for species occurring at low densities. However, some species such as large mammals are often observed by lay people and this information can be leveraged through citizen science monitoring schemes. To ensure that such wildlife monitoring efforts provide robust inferences, assessing the quantity, quality, and potential biases of citizen science data is crucial. For Eurasian moose (Alces alces), a species currently recolonizing north‐eastern Germany and occurring in very low numbers, we applied three citizen science tools: a mail/email report system, a smartphone application, and a webpage. Among these monitoring tools, the mail/email report system yielded the greatest number of moose reports in absolute and in standardized (corrected for time effort) terms. The reported moose were predominantly identified as single, adult, male individuals, and reports occurred mostly during late summer. Overlaying citizen science data with independently generated habitat suitability and connectivity maps showed that members of the public detected moose in suitable habitats but not necessarily in movement corridors. Also, moose detections were often recorded near roads, suggestive of spatial bias in the sampling effort. Our results suggest that citizen science‐based data collection can be facilitated by brief, intuitive digital reporting systems. However, inference from the resulting data can be limited due to unquantified and possibly biased sampling effort. To overcome these challenges, we offer specific recommendations such as more structured monitoring efforts involving the public in areas likely to be roamed by moose for improving quantity, quality, and analysis of citizen science‐based data for making robust inferences. [ABSTRACT FROM AUTHOR]

The current issues in spam email detection systems are directly related to spam email classification's low accuracy and feature selection's high dimensionality. However, in machine learning (ML), feature selection (FS) as a global optimization strategy reduces data redundancy and produces a collection of precise and acceptable outcomes. A black hole algorithm-based FS algorithm is suggested in this paper for reducing the dimensionality of features and improving the accuracy of spam email classification. Each star's features are represented in binary form, with the features being transformed to binary using a sigmoid function. The proposed Binary Black Hole Algorithm (BBH) searches the feature space for the best feature subsets, and feature selection is based on a fitness function that is proportional to the accuracy achieved using a Naive Bayesian Classifier (NBC). When measuring the performance of the BBH with the SpamBase dataset, the performance of the classifier and the dimension of the selected feature vector used as a classifier input are considered. The experiments revealed that the BBH can produce good FS results even with a small set of selected features. This shows that when utilizing the NBC-based BBH, good spam email categorization accuracy is possible. [ABSTRACT FROM AUTHOR]

This study aimed to design a system utilizing an ESP8266 microcontroller, GPS, and MPU6050 sensors to observe and record the movement patterns of elderly patients. A scale prototype was developed by integrating an MPU6050 IMU sensor and a GPS module with an ESP8266 module configured for Wi-Fi communication with a nearby server. Data were collected and evaluated by sensors positioned on test subjects to imitate the movements of elderly patients. Upon successful real-time tracking of the test subject's movements, the data was transmitted to the network using Blynk and email applications for analysis and subsequent display. The collected data yielded valuable insights into the patient's conduct, encompassing frequent walking, pacing, and wandering. The utilization of this device has the potential to improve the safety and well-being of elderly patients and provide valuable data to healthcare professionals and caregivers through its real-time tracking capabilities. [ABSTRACT FROM AUTHOR]

Superannuation is the fund set aside by employers to provide their employees with a dignified retirement. Studies highlight that issues can arise with retirement funds from employers, such as failure to make required contributions to an employee's superannuation fund, incorrect payments, or debiting the wrong fund, contrary to legal or contractual obligations. To address these issues, the Australian Government has implemented laws and regulations to ensure employers fulfil their contribution obligations. Despite these safeguards and highly secured information systems, there has been a significant increase in fraudulent activity in the finance sector, and there have been several instances of employers not making contributions, misusing retirement funds, or reporting incorrectly in their systems. The current process restricts employees from viewing recent data until the contributions reach their super fund, which opens the doors for fraud. Recently, blockchain technology has gained popularity because of its ability to improve security and prevent fraud across many sectors, including finance. Prior studies have shed limited light on how superannuation fraud can be prevented. Moreover, there is limited literature on the possibility of utilizing blockchain technology to address this issue. Therefore, this paper aims to review the current superannuation contribution process and identify the factors contributing to non-payment, incorrect payments, misallocation of funds and communication gaps. This study presents a novel process model and develops a blockchain-based application to mitigate fraudulent practices. This research provides valuable insights into the design of innovative process models that utilize blockchain technology to address superannuation challenges. Furthermore, the paper presents a sample simulated smart contract to explore additional implications and advancements in this domain. [ABSTRACT FROM AUTHOR]