bluuebunny/crossref_metadata_2025 huggingface dataset:
The Analyze Comparative of Physics Computational Thinking Skill (CTs) in Experiment Laboratory
Authors: Fayanto Suritno , Naba Sul Daeng , Kurniawan Aris , Putri Utami , Padang Veronika Dua
Published: July 2024

Objective: This study aimed to analyze students' response to the use of computational thinking from the perspective of computational tools and to analyze the influence of gender on students' computational thinking skills. Method: Research design using a comparative approach with data collection techniques involved a survey using a Likert scale questionnaire comprising 25 items, covering five dimensions of computational thinking skills: abstraction, decomposition, algorithm thinking, evaluation, and generalization. The study subjects involved five classes: physics, physics education, geography, mining engineering, and vocational-technical education, focusing on students' ability to analyze data using JASP and IBM SPSS. The data analyze methods included: (1). Comparative Analyze; (2). Correlation analyzes (Spearman); (3). Chi-square test. Finding: The results showed that the computational thinking skills of students from various classes varied, with significant correlations between the skill dimensions. Physics and Physics Education stood out with exemplary achievements, while Geography and Mining Engineering also showed good progress. The vocational-technical education program displayed nearly perfect correlations in all aspects of computational thinking skills. Meanwhile, from the gender aspect, gender significantly influenced computational thinking skills (Sig<0.00). The analyze highlighted the differences in computational thinking skills between classes and the significant influence of gender. Implication: This emphasized the importance of developing computational thinking skills in higher education and the need for inclusive approaches to enhance computational excellence among students. The implications of this study give valuable insights for improving the teaching of computational thinking in physics education. Steps that might be addressed include identifying and enhancing weak components, such as abstraction and generalization, and using particular tactics to increase students' knowledge.
Immediate Effect of Jala Neti (Nasal Irrigation) on Nasal PeakInspiratory Flow on Healthy Volunteers
Authors: Rani R N  , S Prashanth , Prema Latha P  , Venkateswaran S T  , K Maheshkumar
Published: July 2024

Background: Jala neti (nasal irrigation) is an ancient yogic practice believed to cleanse the nasal passages and enhance respiratory health. The objective of this study was to evaluate the immediate effect of jala neti on nasal airflow measured by peak nasal inspiratory flow (PNIF) in healthy volunteers.Methods: This was a pre-post study involving 60 healthy volunteers (16 males, 44 females) aged 18-25 years. Baseline PNIF measurements were taken before the participants performed jala neti under expert supervision. PNIF was re-measured immediately after the jala neti practice. Paired t-tests were used to compare pre and post PNIF values.Results: The mean±SD pre-neti PNIF was 84.56±12.56 L/min which increased significantly to 110.85±12.56 L/min post-neti (p=0.001). The pre and post PNIF values for males were 86.45±12.82 and 124.25±10.96 L/min respectively (p=0.02). For females, the values were 78.32±14.52 pre-neti and 98.65±12.78 L/min post-neti (p=0.04).Conclusion: Jala neti practice resulted in an immediate significant improvement in nasal inspiratory flow in healthy volunteers, suggesting it may be a beneficial practice for enhancing nasal airflow and respiratory health.
The cardiovascular and metabolic effects of ovarian suppression in combination with tamoxifen or an aromatase inhibitor as adjuvant therapy for early estrogen receptor-positive breast cancer: a systematic review
Authors: Gue Ying , Lau Dorothy , Lip Gregory , Shantsila Alena , Palmieri Carlo
Published: July 2024

Adjuvant endocrine therapy is a key treatment in oestrogen receptor positive early breast cancer (BC). For premenopausal women ovarian function suppression (OFS) in combination either tamoxifen or aromatase inhibitor (AI) is utilised in high-risk cases. The resultant suppression of circulating oestradiol from OFS could have adverse cardiovascular and metabolic effects, subsequently increasing the risk of cardiovascular disease.

Our study highlights the potential cardio-metabolic impact endocrine therapy and OFS have on premenopausal women with BC. It also highlights the pressing need for standardisation of routine collection and recording of cardiometabolic history and risk factors as part of baseline assessment and adverse event reporting to better understand the cardiometabolic impact these treatment has on our patients. Further studies, with particular focus on baseline and subsequent development of cardiovascular risk factors are needed to explore the impact of these drugs on the cardiovascular health of these young women with BC
Security Assessment of in-Vehicle Network Intrusion Detection in Real-Life Scenarios
Authors: Yusupov Kamronbek , Islam Md Rezanur , Oh Insu , Sahlabadi Mahdi , Yim Kangbin
Published: July 2024

This research focuses on evaluating the security of an intrusion detection system in a CAN bus-based vehicle control network. A series of studies were conducted to evaluate the performance of models proposed by previous researchers, testing their effectiveness in real-world scenarios as opposed to those on which they were trained. The article demonstrates that models trained and tested on the same dataset can only sometimes be considered adequate. An approach that included models trained only on CAN ID, payload, or full data was chosen. The research results show that such methods are ineffective enough in real-world attack scenarios because they cannot distinguish between new scenarios not presented during training. The results of testing the models in various attack scenarios are presented, and their limitations are identified. In addition, a new method is proposed explicitly for attack scenarios that may occur in the real-world use of an invehicle CAN communication system.
Secrecy with Intent: Malware Propagation through Deep Learning-Driven Steganography
Authors: Diyachkov Mikhail , Yakubov Arkadi , Daltrophe Hadassa , Danilchenko Kiril
Published: July 2024

With the proliferation of deep learning, steganography techniques can now leverage neural networks to imperceptibly hide secret information within digital media. This presents potential risks of propagating malware covertly. We present an innovative deep-learning framework that embeds malware within images for stealthy distribution. Our methodology transforms malware programs into image representations using a specialized neural network. These image representations are then embedded seamlessly within innocuous cover images using an encoding network. The resulting stego images appear unmodified to the naked eye. We develop a separate network to extract the malware from stego images. This attack pipeline allows the malware to bypass traditional signature-based detection. We experimentally demonstrate the efficacy of our approach and discuss its implications. Our framework achieves high-fidelity reconstruction of embedded malware programs with minimal distortions in the cover images. We also analyze the impact of loss functions on concealment and extraction capacity. The proposed technique represents a significant advancement in AI-driven steganography. By highlighting an intriguing attack vector, our work motivates research into more robust defensive solutions. Our study promotes responsible disclosure by releasing the attack implementation as open-source.
Federated Learning-Based Privacy Protection Methods for Internet of Things Systems
Authors: Akter Mahmuda , Moustafa Nour
Published: July 2024

The Internet of Things (IoT) forms intelligent systems, such as smart cities and factories, to enhance productivity and provide revolutionary and automated services to end-users and organisations. An IoT ecosystem requires more dynamics and heterogeneity with advanced privacy preservation. Federated Learning (FL)addresses the challenge of maintaining data privacy using a privacy-preserving sharing mechanism instead of transmitting raw data. However, the latest cyber threats cause privacy and security breaches. This study systematically analyses federated learning-based privacy-preserving methods in IoT systems. A standard IoT architecture with possible privacy threats is illustrated. Also, Federated Learning schemes and their taxonomies are discussed in a privacy-preserving manner, with initial experiments proving the significance of FLbased privacy preservation in IoT environments. This finds acceptable noise addition in differential privacy by keeping higher testing accuracy in different settings to enhance privacy preservation of federated learning. Various Federated Learning schemes, challenges and future research directions are covered.
Cybersecurity Incident Response Dynamics: Unveiling Emerging Trends and Confronting Persistent Challenges
Authors: Maamari Waleed A Al , R Ahmed Muhammad , Said , Rusmawati Binti , H Marhaban Mohammad
Published: July 2024

In the current cybersecurity environment, incident response holds paramount importance for organizations concerned with the need to maintain security processes and mitigate potential breaches. Therefore, this paper analyzes the emerging trends and persistent challenges that shape incident response practices. These emerging trends—ransomware attacks, the integration of AI and ML, proactive threat hunting, cloud security incident response, and threat intelligence sharing—also bring with them new opportunities for the development of incident response. However, current challenges to incident response effectiveness include resource constraints, skill shortages, issues of regulatory compliance, organizational silos, and cultural barriers. These can be addressed through investment in advanced technologies, ongoing training, collaborative partnerships, and proactive efforts in regulatory compliance. Organizations can strengthen their incident response postures and effectively mitigate cyber risks by prioritizing leadership commitment, fostering a cybersecurity-aware culture, and embracing proactive measures to meet the exigencies of the changing cybersecurity landscape.
A Systematic Approach Towards Enhancing Digital Privacy in Industrial Applications
Authors: Asadollah Sara Abbaspour
Published: July 2024

Ensuring digital privacy is critical to protecting sensitive information and guarding against malicious actors in today's interconnected world. This paper explores the concept of digital privacy and its importance in maintaining online security. We highlight the importance of robust strategies to protect information by examining the consequences of failing to prioritize digital privacy, including identity attack scenarios in industrial applications by proposing a systematic approach to improving digital privacy. Our methodology includes creating a Data Flow Diagram (DFD) to visualize the data flow within the system and applying the STRIDE threat modelling framework to identify potential threats, with a focus on privacy-related aspects. We then extract privacy-related threats and create attack vectors and scenarios to guide testers to validate system security. To validate our methodology, we plan to conduct a case study in an industrial application, an automated train control system. By analyzing the data flow and identifying potential attack scenarios, we want to demonstrate the effectiveness of our approach in real-world applications. We also want to automate the process and collaborate with more industries to ensure scalability and practical applicability.
Evaluating Persona Prompting for Question Answering Tasks
Authors: Olea Carlos , Tucker Holly , Phelan Jessica , Pattison Cameron , Zhang Shen , Lieb Maxwell , Schmidt Doug , White Jules
Published: July 2024

Using large language models (LLMs) effectively by applying prompt engineering is a timely research topic due to the advent of highly performant LLMs, such as ChatGPT-4. Various patterns of prompting have proven effective, including chain-of-thought, self-consistency, and personas. This paper makes two contributions to research on prompting patterns. First, we measure the effect of single- and multi-agent personas in various knowledge-testing, multiple choice, and short answer environments, using a variation of question answering tasks known as as ”openness.” Second, we empirically evaluate several persona-based prompting styles on 4,000+ questions. Our results indicate that single-agent expert personas perform better on high-openness tasks and that effective prompt engineering becomes more important for complex multi-agent methods.
An Interactive Role-Playing Game based Learning System for History using Artificial Intelligence and 3D Modeling
Authors: Yang Haocheng , Boulom Tyler
Published: July 2024

This paper addresses the challenge of disengagement and lack of motivation among students learning history, often attributed to traditional teaching methods that fail to captivate their interest. We propose an innovative solution through an educational roleplaying game (RPG) that integrates adaptive AI to create a personalized, immersive learning environment [1]. This game allows students to explore historical events as active participants rather than passive recipients, enhancing engagement and retention of information. Key technologies include an adaptive AI system that tailors content to individual learning styles and a game-based platform that encourages exploration and decision-making [2]. Challenges such as ensuring historical accuracy, maintaining user engagement, and addressing diverse educational needs were addressed through iterative design and testing, incorporating feedback from real-world classroom settings. Experimental application across various educational scenarios demonstrated significant improvements in student engagement and historical knowledge comprehension. Results indicated that students not only enjoyed learning about history through this interactive platform but also developed a deeper understanding of the subject matter. Ultimately, this RPG-based educational tool offers a compelling alternative to traditional history education, promoting active learning and sustained interest [3]. Its ability to adapt to individual learner profiles and engage students in meaningful ways makes it an invaluable resource in educational environments striving to enhance both learning outcomes and student motivation.
Enhancing File Organization in the Modern Computing Era: An AI-Driven Approach with the File Sorter Application
Authors: Shao Chenyue , Washburn Garret
Published: July 2024

In the modern computing age, where it seems that almost everyone has a computer, the organization of individuals file structures has become increasingly more and more unorganized [1]. In an effort to solve this problem, the File Sorter application proposed in this paper enables a user to easily sort their files with the help of an AI model designed to sort files accurately and efficiently. The File Sorter application utilizes AI to predict where a file should go given a dataset [2]. To test this application, we employed experiments to design how time efficient the File Sorter was as well as how accurately it sorted files, in which both experiments displayed no inefficiency. The public should use the File Sorter application because it accurately and efficiently sorts files in varying manners, and it is good practice for an individual to maintain a healthy file structure because it enables a productive file structure workflow.
Review on Blockchain for IoT Security and Data Integrity
Authors: Shaima Mujiba , Rana Md Nasir Uddin , Islam Md Tanvir , Nabi Norun , Tusher Mazharul Islam , Ahmed Estak , Saha Sushanta , Shiam Sarder Abdulla Al
Published: July 2024

IoT, or the Internet of Things, describes a network of networked objects that are equipped with software, sensors, and other technologies to gather and share data. However, blockchain is a distributed ledger technology that makes it possible to record transactions over a network of computers in a safe, transparent, and unchangeable. The way that blockchain and IoT can enhance each other's advantages is how they are connected: Blockchain technology, with its decentralized and impenetrable ledger, offers safe and effective storage and transfer of the massive volumes of data generated by Internet of Things devices. Organizations may guarantee the security and integrity of IoT data by incorporating blockchain technology into IoT systems. This will allow for reliable and open communications and transactions between users and devices. Here, we summarize the current body of research and draw attention to the main cybersecurity issues facing blockchain-based Internet of Things platforms. These problems are divided into three primary categories: (i) security of IoT devices; (ii) security of blockchains; and (iii) integration of IoT devices with blockchain (network security). To further address a little about these issues and improve the cybersecurity of blockchain-based IoT systems, we also analysis future research directions.
A Robust Set of Emailing Applications that Allowaneffective Solution for Bulk Email Sendingandreceiving
Authors: Chen Zixuan , Washburn Garret
Published: July 2024

In the current digital age, the capability of a business or workforce team to send out or receive a mass amount of emails is increasingly becoming more important. However, with this becoming more and more of a necessity, the tools to do so are not developing as rapidly. The method this paper seeks to provide as a solution to this problem is the Email Sender and Receiver applications that are described within this paper [1]. The main technologies used to create these Python applications behind the scenes are the Mailgun API, the Gmail API, and the PySimpleGui Python library [2]. One of the challenges that we faced during the development of the Email Sender and Receiver applications was the scheduling of emails for recipients in different timezones, as it is important that the sending of emails be accurate to their time across the globe. Another challenge faced during development was the configuration and maintenance of the Google Project in order to use the Gmail API, as the backend for the Email Receiver application relies on the Gmail API to grab from the user account [3]. To ensure the functionality and efficiency of the Email Sender and Receiver applications, we conducted experiments on both applications to test their limits. For the Email Sender application, an experiment was conducted to see how the size of the email being sent would affect how the application runs, and we found the application to be quite stable. Additionally, for the Email Receiver application, another experiment was conducted to test and see how far into the past we could grab emails and not impact the application. As a result, we found the Gmail API limits how many emails you may grab in one request. To conclude, the Email Sender and Receiver applications are viable tools for companies and individuals alike, as both applications provide an easy-touse bulk email tool that doesn’t compromise on functionality.
Safeguarding Voice Privacy: Harnessing Near-Ultrasonic Interference to Protect Against Unauthorized Audio Recording
Authors: McKee Forrest , Noever David
Published: July 2024

The widespread adoption of voice-activated systems has modified routine human-machine interaction but has also introduced new vulnerabilities. This paper investigates the susceptibility of automatic speech recognition (ASR) algorithms in these systems to interference from near-ultrasonic noise. Building upon prior research that demonstrated the ability of near-ultrasonic frequencies (16 kHz - 22 kHz) to exploit the inherent properties of micro electromechanical systems (MEMS) microphones, our study explores alternative privacy enforcement means using this interference phenomenon. We expose a critical vulnerability in the most common microphones used in modern voice-activated devices, which inadvertently demodulate near-ultrasonic frequencies into the audible spectrum, disrupting the ASR process. Through a systematic analysis of the impact of near-ultrasonic noise on various ASR systems, we demonstrate that this vulnerability is consistent across different devices and under varying conditions, such as broadcast distance and specific phoneme structures. Our findings highlight the need to develop robust countermeasures to protect voice-activated systems from malicious exploitation of this vulnerability. Furthermore, we explore the potential applications of this phenomenon in enhancing privacy by disrupting unauthorized audio recording or eavesdropping. This research underscores the importance of a comprehensive approach to securing voice-activated systems, combining technological innovation, responsible development practices, and informed policy decisions to ensure the privacy and security of users in an increasingly connected world.
Analyzing Persuasive Strategies in Meme Texts: A Fusion of Language Models with Paraphrase Enrichment
Authors: Nayak Kota Shamanth Ramanath , Kosseim Leila
Published: July 2024

This paper describes our approach to hierarchical multi-label detection of persuasion techniques in meme texts. Our model, developed as a part of the recent SemEval task, is based on fine-tuning individual language models (BERT, XLM-RoBERTa, and mBERT) and leveraging a mean-based ensemble model in addition to dataset augmentation through paraphrase generation from ChatGPT. The scope of the study encompasses enhancing model performance through innovative training techniques and data augmentation strategies. The problem addressed is the effective identification and classification of multiple persuasive techniques in meme texts, a task complicated by the diversity and complexity of such content. The objective of the paper is to improve detection accuracy by refining model training methods and examining the impact of balanced versus unbalanced training datasets. Novelty in the results and discussion lies in the finding that training with paraphrases enhances model performance, yet a balanced training set proves more advantageous than a larger unbalanced one. Additionally, the analysis reveals the potential pitfalls of indiscriminate incorporation of paraphrases from diverse distributions, which can introduce substantial noise. Results with the SemEval 2024 data confirm these insights, demonstrating improved model efficacy with the proposed methods.
A Smart Environmentally Friendly Lamps and Air Quality Improvement System using Artificial Intelligence and IoT System (Internet of Things)
Authors: Zhu Chunming , Andow Zachary
Published: July 2024

In recent years, environmental and technological concerns have rapidly approached, emphasizing the importance of energy consumption. The paper details the design and implementation of a motion detection-based lamp system, capable of switching a lamp on and off based on predefined conditions, leading to significant energy savings. The project includes two-way communication between devices, a user-friendly control interface, and an accurate way to save energy. Users can control their system through the application and specify the requirements to their likening. This application can help ease of life along with a reduced energy consumption. Careful testing has been done along with control cases to compare the findings and shows the amount of precision that must be used for intelligent control systems. The results show that automated lights or electronics can be extremely useful along with the ability to remotely control your devices with the application. This application is suitable for consumption and offers a great opportunity for those invested to save energy with minimal effort.
Evaluating Smart Government Maturity: Insights From Abu Dhabi Government
Authors: Hujran Omar
Published: July 2024

The rapid advancement of smart technologies has significantly bolstered the government's capabilities for digital transformation. The integration of these technologies in various government functions is expanding, showcasing dynamic capabilities that have the potential to fully unlock the transition from digital to data-driven smart government. Despite the widespread acknowledgment of the importance of smart government services, there is a notable dearth of research in this area. Additionally, the existing e-government literature lacks a comprehensive criterion for evaluating smart government, serving as a single reference to guide governments in this transformative journey. Thus, the aim of this study is to fill these gaps in the literature. Results from this study reveal that Abu Dhabi government employs emerging technologies, including artificial intelligence, cloud computing, open-access government data, mobile applications, sensors, and social networking. The results of this paper are valuable as they included specific measures to assess smart government maturity.
Improving Breast Cancer Detection with Naive Bayes: A Predictive Analytics Approach
Authors: Garba Muhammad , Usman Muhammad Abdurrahman , Gulumbe Anas Muhammad
Published: July 2024

The study focuses on predicting breast cancer survival using naïve bayes techniques and compares several machine learning models across large dataset of 310,000 patient records. The survival and non-survival classes were the two main categories. The objective of the study was to assess the effectiveness of the Naïve Bayes classifier in the data mining area and to attain noteworthy outcomes for survival classification that were consistent with the body of existing literature. Naive Bayes achieved an average accuracy of 91.08%, indicating reliable performance but with some variability across folds. Logistic Regression achieved an accuracy of 94.84%, excelling in identifying instances of class 1 but struggling with class 0. Decision Tree model, with an accuracy of 93.42%, showed similar performance trends. At 95.68% accuracy, Random Forest outperformed Decision Tree. However, all models faced challenges in classifying instances of class 0 accurately. The Naive Bayes algorithm was compared with K-Nearest Neighbors (KNN) and Support Vector Machines (SVM). Future research will enhance prediction models with new methods and address the challenge of accurately identifying instances of class 0.
Enhancing Biomedical Education through Innovative Virtual Reality Platforms: Development and Assessment of a Unity-Based Forensic Laboratory Simulation
Authors: Liu Andrew , Park Andrew
Published: July 2024

This research paper illuminates the growing necessity for innovative educational platforms in the biomedical field, given the burgeoning population and consequent demand for medical professionals. Proposing a novel solution, the project introduces a Virtual Reality (VR) application, designed using the Unity game engine, to foster scientific exploration within a simulated forensic laboratory environment [1]. Key components of the program include a VR Control System, accurate emulations of scientific equipment, and a cohesive puzzle design to drive user engagement and analytical thinking. Challenges such as VR controls comfort, environment scaling, and asset acquisition were navigated, enhancing the user experience. The application was subjected to user experimentation, particularly with high school students, through two distinct surveys assessing user experience and feedback for enhancements. Results indicated a notable engagement among the younger demographic with a demand for additional levels and specific improvements on equipment like the centrifuge [2]. The feedback gathered underscores the project’s potential in revolutionizing biomedical education, providing an immersive, interactive, and enjoyable learning platform. By addressing the feedback and expanding the project further, the VR application holds promise in significantly contributing to the educational domain, preparing the next generation of medical professionals in a costeffective and engaging manner.
A System to Analyze and Modulate the Political Biases of Large Language Models using Prompt Engineering techniques
Authors: Chang Yuanshou , Sun Yu
Published: July 2024

In the burgeoning landscape of artificial intelligence, Large Language Models (LLMs) such as GPT have surged in popularity, embedding themselves into the fabric of daily digital interactions [1]. As these models assume a pivotal role in shaping discourse, understanding their inherent political biases becomes crucial. This paper delves into the political stance of GPT, examining its consistency and the potential for modification through prompt engineering. Our investigation reveals that GPT exhibits a consistent left-libertarian stance, a finding that underscores the importance of recognizing and addressing the ideological underpinnings of AI technologies [2]. Furthermore, we explore the feasibility of altering GPT's political stance towards neutral and right-authoritarian positions through strategic prompt design. This research not only illuminates the political dimensions of LLMs but also opens avenuesfor more balanced and controlled AI interactions, offering insights into the complex interplay between technology, ideology, and user agency.
Enhancing Identity Management: Best Practices for Governance and Administration
Authors: Ghadge Nikhil
Published: July 2024

Identity management has become increasingly critical in today's digital landscape, where sensitive data is exposed to frequent breaches and unauthorized access. This research seeks to explore optimal governance and administrative practices to enhance identity management systems, with a particular emphasis on security, privacy, and usability. By examining current industry standards, regulations, and technological advancements, the study seeks to provide valuable insights for organizations looking to enhance their identity management capabilities. The research methodology utilizes a mixed-methods approach, integrating quantitative surveys and data analysis with qualitative interviews. This combination aims to provide a comprehensive understanding of current practices and challenges in identity governance and administration. The study explores key components such as authentication, authorization, and access control, offering practical recommendations to improve the effectiveness of identity management strategies. The research highlights the importance of adopting role-based access control (RBAC), continuous monitoring and compliance, identity lifecycle management, and the integration of identity governance with IT infrastructure. It also emphasizes the significance of effective password management, authentication measures, and the implementation of Single Sign-On (SSO) solutions to enhance security and user experience. Furthermore, the study underscores the critical role of data encryption and protection measures in safeguarding sensitive information and mitigating the risk of data breaches. By adhering to best practices in identity management, organizations can improve their overall cybersecurity posture, ensure compliance with regulations, and foster trust among stakeholders in an increasingly complex digital environment.
A New Ontology-Based Approach for Web Services Selection
Authors: Feddaoui Ilhem , Limam Hela , Jarrar Hashim , Akaichi Jalel
Published: July 2024

In the contemporary online environment, the increasing prevalence of distributed and accessible web services presents a challenge, as users struggle to locate services that are suitable for their specific needs. In this context, it is essential to categorize web services that offer the same functionality to facilitate the selection of appropriate options. The imprecision of current web service selection methods can be attributed to the disregard for users' feedback and past selections. Furthermore, users encounter difficulties in selecting the most suitable web services when using conventional keyword-based search strategies. Our research introduces a novel hybrid approach that combines syntactic and semantic methods for selecting appropriate web services based on collaborative filtering, ontology-based querying, and QoS. Experiments demonstrate the effectiveness of our proposed selection technique in accurately recommending the required web services.
PEMUDA PEDULI: MERAJUT HARAPAN MASYARAKAT MELALUI AKSI BAKTI SOSIAL SEBAGAI SIMBOL GENERASI PENERUS
Authors: Sawir Muhammad , Kamaluddin Santrio , Abubakar Faisal , Fatimah Siti , Kalapadang Yuliati , Susilawaty Susilawaty , Tambawang Laela , Pongtuluran Rhina , Juli La , Usulu Elvira M.
Published: July 2024

Kebersihan lingkungan pantai sangat penting untuk kesehatan masyarakat dan daya tarik wisata. Pantai Base G sebagai destinasi wisata yang populer di Kota Jayapura menghadapi tantangan dengan meningkatnya jumlah dan aktivitas wisatawan perlu diimbangi dengan peningkatan kesadaran menjaga kebersihan lingkungan Pantai. Untuk mengatasi masalah ini, Universitas Yapis Papua menyelenggarakan program bakti sosial di Pantai Base G dengan melibatkan mahasiswa dan civitas akademika. Kegiatan ini bertujuan untuk meningkatkan kesadaran lingkungan, melatih mahasiswa baru dalam kepedulian lingkungan, dan memenuhi Tri Dharma Perguruan Tinggi. Partisipasi dalam kegiatan pembersihan ini membantu mengurangi sampah di pantai, menciptakan lingkungan yang lebih bersih dan nyaman, serta meningkatkan kepuasan pengunjung. Keberhasilan program ini menunjukkan pentingnya kolaborasi antara masyarakat, institusi pendidikan, dan pemerintah dalam menjaga kebersihan lingkungan pantai. Diharapkan kegiatan ini dapat berkelanjutan untuk mendukung keberlanjutan wisata dan kesehatan lingkungan di Pantai Base G Kota Jayapura.
Kolaborasi dalam Pemetaan Wilayah Adat di Kabupaten Jayapura
Authors: Usulu Elvira M. , Majid Hasbi , Jelita Indah
Published: July 2024

Upaya mendorong pengakuan terhadap masyarakat adat, maka pemerintah Kabupaten Jayapura telah melakukan beberapa upaya diantara dengan melakukan pemetaan terhadap masyarakat adat yang ada di Kabupaten Jayapura. Upaya pemetaan yang dilakukan ini membutuhkan peran serta semua komponen masyarakat sehingga kolaborasi menjadi penting untuk dilakukan. Oleh karen itu, tujuan penelitian ini adalah untuk mengetahui proses kolaborasi dalam pemetaan wilayah adat di Kabupaten Jayapura. Metode penelitian yang digunakan adalah metode kualitatif bersifat deskriptif. Pengumpulan data dilakukan melalui wawancara, observasi, dan telaah dokumen. Data hasil penelitian penelitian yang diperoleh kemudian diolah dan dianalisis melalui tahapan-tahapan, yakni pengumpulan data, reduksi data, display data, dan penarikan kesimpulan. Hasil penelitian menunjukan bahwa masyarakat adat mendapat pengakuan resmi dari pemerintah atas wilayah adatnya, sehingga menjamin kejelasan dan kepastian hukum. Selain itu, pemetaan membantu mengurangi konflik lahan dan memberikan hak untuk mengelola dan memanfaatkan sumber daya alam di wilayah mereka.
Sorensen Trust Based and Invasive Weed Algorithm Based Wireless Sensor Network Optimization
Authors: Jayant Shukla  , Laxmi Singh  , Sanjeev kumar Gupta
Published: July 2024

Wireless Sensor Networks (WSNs), characterized by their openness, dynamism, and lack of infrastructure, are highly susceptible to a range of attacks due to their ad hoc nature. Routing, being a pivotal process within WSNs, relies heavily on the contribution of intermediary nodes, thereby accentuating the network's vulnerability to black and gray hole attacks. This work has proposed a Sorensen Trust and Invasive Weed based Wireless Network optimization (STIWWNO) model that estimates the confidence of the network with the social Sorensen trust evaluation function. Once the network knows the nodes trust then cluster centers selection makes easy and safe for routing of sensed data. Packet node path were generate by the invasive weed optimization genetic algorithm. Experiments were conducted under various network conditions, including different node counts and area sizes, to evaluate the effectiveness of the proposed method. The results of these experiments demonstrated that the use of the proposed Sorensen function, combined with the invasive weed optimization technique, significantly enhances the lifespan of the network. The adaptive nature of IWO allows the network to respond effectively to changes in node positions, ensuring sustained performance and energy efficiency. By applying this combined approach, the network can dynamically adjust to varying conditions, maintain optimal performance, and extend the operational life of the WSN.
Implementing machine learning algorithms to detect and prevent financial fraud in real-time
Authors: Halima Oluwabunmi Bello  , Courage Idemudia  , Toluwalase Vanessa Iyelolu
Published: July 2024

Financial fraud poses a significant threat to the stability and integrity of global financial systems. This paper explores the potential of machine learning (ML) algorithms to enhance the detection and prevention of financial fraud in real-time. We employed a quantitative research methodology, utilizing a combination of supervised and unsupervised ML techniques applied to a dataset comprising transactional data from a multinational bank over a five-year period. Key algorithms tested include Random Forest, Support Vector Machines, and Neural Networks, alongside anomaly detection methods like Isolation Forest and Autoencoders. Our findings reveal that ML algorithms can effectively identify patterns and anomalies that signify fraudulent activities, with Neural Networks demonstrating the highest accuracy in detection. The study also uncovered that real-time processing of transactions using these algorithms significantly reduces the detection time, thus preventing potential fraud before it can cause substantial harm. Furthermore, integrating ensemble techniques improved the robustness and accuracy of fraud detection systems.
The paper concludes that the implementation of ML algorithms in financial institutions is not only feasible but also imperative for real-time fraud prevention. It recommends ongoing training of models with updated transaction data and increased collaboration between data scientists and financial security experts to continually enhance the effectiveness of fraud detection systems. This research contributes to the evolving field of financial security by providing a clearer understanding of how ML can be strategically utilized to combat financial fraud dynamically and effectively.
Keywords: Machine Learning, Fraud Detection, Financial Institutions, Ethical Considerations, Privacy Protection, Regulatory Compliance, Technology Integration, Collaborative Frameworks, Deep Learning, Blockchain Technology, Data Security, Adaptive Systems, Real-time Processing, Algorithmic Bias, Data Anonymization.
Data-Driven decision making in agriculture and business: The role of advanced analytics
Authors: Eyitayo Raji  , Tochukwu Ignatius Ijomah  , Osemeike Gloria Eyieyien
Published: July 2024

Advanced analytics has revolutionized decision-making processes in agriculture and business by harnessing data-driven insights to optimize operations, manage risks, and drive innovation. This paper explores the transformative role of advanced analytics in these sectors, highlighting key benefits, challenges, and future directions. In agriculture, advanced analytics enables precision farming by integrating AI, IoT sensors, and satellite imagery. Predictive models forecast crop yields, optimize irrigation, and enhance soil management practices, improving productivity and sustainability. Similarly, advanced analytics supports strategic decision-making in business by analyzing consumer behavior, predicting market trends, and optimizing supply chain operations. However, adopting advanced analytics faces challenges such as data quality, technical expertise, cost constraints, and ethical considerations. Addressing these challenges requires investments in data infrastructure, talent development, and regulatory compliance to ensure secure and ethical data usage. Emerging trends include AI-driven automation, blockchain for supply chain transparency, and augmented analytics for democratizing data access. Recommendations for stakeholders include investing in data capabilities, fostering collaborative partnerships, and promoting a culture of data-driven decision making. In conclusion, advanced analytics offers profound opportunities to enhance efficiency, inform decision making, and drive sustainable growth in agriculture and business. Embracing these technologies is essential for organizations seeking to thrive in a data-driven economy.
Keywords: Advanced Analytics, Precision Farming, Predictive Analytics, Data-driven Decision Making, Business Intelligence.
Digital access and inclusion for SMEs in the financial services industry through Cybersecurity GRC: A pathway to safer digital ecosystems
Authors: Oluwatosin Abdul-Azeez  , Alexsandra Ogadimma Ihechere  , Courage Idemudia
Published: July 2024

The integration of digital technologies into the financial services industry has revolutionized how small and medium-sized enterprises (SMEs) access and utilize financial services. However, this digital transformation also brings heightened cybersecurity risks, making robust governance, risk management, and compliance (GRC) frameworks essential for fostering a safer digital ecosystem. This paper explores the pivotal role of cybersecurity GRC in enhancing digital access and inclusion for SMEs within the financial sector. By analyzing current challenges and opportunities, we propose a comprehensive approach to fortifying cybersecurity measures that align with the unique needs of SMEs. Firstly, the paper identifies the primary cybersecurity threats facing SMEs, including data breaches, phishing attacks, and ransomware, which can severely impact their operations and financial stability. It underscores the importance of a proactive GRC strategy that encompasses risk assessment, policy development, and continuous monitoring to mitigate these threats effectively. Moreover, the paper highlights the necessity for regulatory compliance, stressing how adherence to standards such as GDPR, PCI DSS, and ISO/IEC 27001 can bolster SMEs' defenses and enhance their credibility with customers and partners. Secondly, the research delves into the benefits of enhanced digital access and inclusion facilitated by a strong cybersecurity GRC framework. These benefits include improved financial inclusion for underbanked SMEs, streamlined access to digital financial services, and the promotion of innovation and competitiveness. The paper argues that by ensuring a secure digital environment, SMEs can confidently adopt emerging technologies such as blockchain, artificial intelligence, and cloud computing, driving growth and efficiency. Lastly, the paper presents case studies of successful cybersecurity GRC implementations in the financial services sector, showcasing best practices and lessons learned. It provides practical recommendations for SMEs to develop and maintain robust GRC frameworks, including leveraging automated tools for threat detection, fostering a culture of cybersecurity awareness, and engaging with cybersecurity experts for continuous improvement. In conclusion, the paper asserts that a comprehensive cybersecurity GRC strategy is crucial for enhancing digital access and inclusion for SMEs in the financial services industry. By addressing cybersecurity risks and ensuring compliance, SMEs can safely navigate the digital landscape, unlocking new opportunities for growth and innovation while contributing to a more secure and inclusive digital economy.
Keywords:  Digital Access, Inclusion, SMEs, Financial Services, Cybersecurity.
Financial compliance as a pillar of corporate integrity: A thorough analysis of fraud prevention
Authors: Maxwell Nana Ameyaw  , Courage Idemudia  , Toluwalase Vanessa Iyelolu
Published: July 2024

Financial compliance stands as a cornerstone of corporate integrity, essential for safeguarding organizational assets and maintaining stakeholder trust. This review examines the critical role of financial compliance in preventing fraud, highlighting its multifaceted approach and the strategies employed to mitigate risks effectively. At its core, financial compliance involves adherence to regulatory standards and internal controls designed to ensure transparency and accountability in financial operations. It encompasses rigorous monitoring, reporting, and auditing practices that aim to detect and deter fraudulent activities within organizations. By implementing robust compliance frameworks, companies not only uphold legal obligations but also cultivate a culture of integrity that permeates throughout all levels of the organization. Fraud prevention within the realm of financial compliance involves proactive measures to identify and mitigate potential risks. This includes conducting thorough risk assessments, establishing internal controls, and implementing anti-fraud policies and procedures. Compliance officers play a pivotal role in overseeing these efforts, working in tandem with stakeholders to enforce compliance measures and promptly address any deviations from ethical standards. Moreover, technology plays a crucial role in enhancing fraud prevention capabilities. Advanced analytics, artificial intelligence, and machine learning algorithms enable proactive monitoring of financial transactions and anomalies, facilitating early detection of suspicious activities. These technological advancements not only streamline compliance processes but also bolster organizational resilience against emerging threats in an increasingly digital landscape. Effective fraud prevention through financial compliance requires a comprehensive approach that integrates proactive risk management with robust internal controls and continuous monitoring. By fostering a culture of compliance and ethical conduct, organizations can mitigate financial risks, protect shareholder value, and uphold their reputation as responsible corporate entities. In conclusion, financial compliance serves as a linchpin of corporate integrity, anchoring fraud prevention efforts through stringent regulatory adherence and proactive risk management practices. As businesses navigate evolving regulatory landscapes and technological advancements, prioritizing financial compliance remains paramount in safeguarding organizational assets and preserving stakeholder trust in today's interconnected global economy.
Keywords:  Financial Compliance, Pillar, Corporate Integrity, Fraud Prevention, Thorough Analysis
Improving agricultural practices and productivity through extension services and innovative training programs
Authors: Eyitayo Raji  , Tochukwu Ignatius Ijomah  , Osemeike Gloria Eyieyien
Published: July 2024

Agricultural productivity is fundamental to global food security and economic development. This paper explores the critical role of extension services and innovative training programs in enhancing agricultural practices and productivity. Traditional farming methods, while sustainable, often fail to meet the demands of modern agriculture due to limited efficiency and vulnerability to environmental changes. Extension services bridge the gap between research and practical application, offering advisory services, training, information dissemination, and resource facilitation. Innovative training programs provide farmers with essential knowledge and skills, including workshops, on-field training, and digital platforms. These initiatives lead to significant productivity improvements, evidenced by success stories like the Farmer Field Schools and India's e-Extension program. Comparative analyses show that regions with robust extension services and training programs report higher productivity and resilience. Scaling up these successful models, integrating modern technologies, fostering public-private partnerships, and building local capacity are recommended to further enhance their impact. Ultimately, these efforts are vital for sustainable agricultural development and food security.
Keywords: Agricultural Productivity, Extension Services, Innovative Training Programs, Sustainable Agriculture, Food Security.