This study investigates the effectiveness of machine learning models in identifying fraudulent financial transactions in real-time. Using a large dataset of transactions, we compare the accuracy, precision, and speed of various models, including logistic regression, random forests, and neural networks. Our findings suggest that ensemble methods yield higher detection rates while minimizing false positives, thus providing a promising approach to financial fraud prevention.

In the ever-changing digital world, strong security protocols are essential. As a vital line of defence against unwanted access, blockchain uses several verification techniques to boost security. This article investigates the use of blockchain technology to tackle privacy, security, and usability issues. By reducing the dangers associated with conventional centralised systems, blockchain's decentralised and immutable structure offers a secure platform for storing and verifying authentication credentials. This method increases user trust by using smart contracts to guarantee transparent and unchangeable authentication procedures. The suggested blockchain-based method strengthens security and enhances privacy by removing sources of failure and decreasing dependence on outside verification. Furthermore, user-centric design and expedited procedures improve the system's usability by making secure authentication more approachable and less obtrusive. This paper offers a thorough examination of the suggested system, stressing its benefits, possible drawbacks, and directions for future investigation. The results indicate that blockchain technology presents a viable solution to ensure that digital authentication frameworks combine privacy, security, and usability.

This study aims to analyze the efficiency of the curriculum of Islamic universities in Indonesia in ensuring the readiness of students to enter the workforce, by reviewing the role of Islamic religious education in improving intellectual competence. Through a literature study approach, this study identifies the challenges and opportunities faced by Islamic universities in integrating religious values ??with the intellectual and professional needs of students. The main focus of this study is the effectiveness of the curriculum in balancing theoretical aspects of religion and practical skills needed in the workplace. This study found that the curriculum of Islamic universities in Indonesia has attempted to adopt an integrative approach that combines religious education with the development of soft skills and hard skills that are relevant to professional careers. However, there are still some weaknesses in the implementation, such as the lack of curriculum adjustment to the dynamics of the labor market, as well as the imbalance between religious education and practical skills. The results of this study indicate that Islamic religious education has an important role in shaping the character and professional ethics of students, which are the main capital in entering the workforce. Therefore, increasing the efficiency of the curriculum through collaboration between academics, industry practitioners, and policy makers is needed to ensure that students' intellectuality is in accordance with the demands of the workplace. This study contributes to the academic discussion on the reform of Islamic higher education curriculum and emphasizes the importance of Islamic religious education in producing graduates who are not only intellectually competent, but also have strong ethical values ??to face professional challenges. The implication of this study is the need for a more flexible and adaptive curriculum strategy to improve the competitiveness of Islamic higher education graduates in the global job market.

As cyber-physical systems (CPS) gain prevalence in sectors such as manufacturing, transportation, and critical infrastructure, ensuring their security and reliability is paramount. Traditional anomaly detection methods often fall short due to the dynamic and complex nature of CPS, leading to missed or false alarms. This study introduces an enhanced machine learning model that integrates statistical and deep learning techniques for real-time anomaly detection in CPS. By employing a hybrid approach of convolutional neural networks (CNNs) with statistical pattern recognition, the model demonstrates improved detection accuracy and responsiveness. Performance is evaluated using industry-standard CPS datasets, showing that the proposed model outperforms existing techniques in both accuracy and efficiency.

Energy efficiency is a critical concern in wireless sensor networks (WSNs) due to the limited power resources available in sensor nodes. Prolonging network lifespan while ensuring reliable data transmission is essential for successful deployment in various applications, such as environmental monitoring, military operations, and industrial automation. This paper presents a mathematical model designed to optimize energy consumption across various nodes in WSNs. By implementing simulations and analyzing data from these models, the study demonstrates significant improvements in extending network lifespan while maintaining reliable data throughput. The findings contribute valuable insights into energy management for large-scale sensor deployments.

The Industrial Internet of Things (IIoT) enhances the connectivity and efficiency of living lifestyles. However, it also comes with significant security vulnerabilities. Traditional authentication methods are often inadequate, leading to IIoT devices opened to security threats. This paper proposes a comprehensive security framework integrating blockchain, cryptographic techniques, smart contracts, and deep learning-based Intrusion Detection Systems (IDS) to tackle the mentioned issue. Blockchain ensures data integrity and prevents tampering through a decentralized ledger. A decentralized device identity management system enhances user verification, while secure communication protocols using Hash-based Message Authentication Codes (HMAC) safeguard data integrity. Smart contracts automate transactions, providing transparent, secure record-keeping without a central authority. The deep learning-based IDS, utilizing Contractive Sparse Autoencoder (CSAE) and Attention-Based Bidirectional Long Short-Term Memory (ABiLSTM) networks, effectively detects cyber threats. Evaluation metrics, including precision, recall, F1-score, and False Acceptance Rate (FAR), demonstrate high accuracy and low false alarm rates across datasets. This framework addresses the need for secure, efficient, and scalable authentication in IIoT, combining blockchain's security features with advanced cryptographic and anomaly detection techniques, offering robust defence against cyber threats.

One of the important problems addressed by researchers and specialists is the problem of agriculture and agricultural land and how to sustain or address them in the event that they need a certain treatment or work is being done to develop methods and agricultural tools and patterns used, which in turn affects production, and in this research has been working to study the impact of several factors on production and has been selected specific workers agricultural land, which is harvested land and affected land and total land and has Mathematical statistical methods were used in the analysis and the STIPWISE method  of linear regression was used to determine the effect and any variables affecting and not affecting the dependent variable, which is agricultural production, and the results of the paper found that one of the variables does not affect the dependent variable, which is the harvested land variable.

Students who excel must receive guidance on the development of each student's individual potential in order to produce superior and quality students. To complete this task, student achievement is taken into account. The Analytical Hierarchy Process (AHP) approach is one method that can be utilized in decision support systems. can do weighting to determine selection criteria and alternatives, starting from the definition of the problem. The purpose of evaluation or weighting in a decision support system, such as that carried out using the Analytic Hierarchy Process (AHP) approach, is to assess and compare the value or characteristics of each alternative based on predetermined criteria. This process allows us to understand how important each criterion is in achieving the main goal, so that the decisions taken are more structured and objective. Based on the method used, the system provides valid and accurate findings. The final result of determining the priority weight of alternatives based on ranking
 
 

Numerical integration is a method used to calculate the integral of functions that cannot be integrated analytically. Several methods that are often used in numerical integration include that Simpson 1/3 method, Simpson 3/8 method, and the Trapezoidal method. This research aims to apply and compare the accuracy and efficiency of the third method in solving various types of integral functions using computing software. With the help of software such as MATLAB and Python, the precision of the results obtained from each method is analyzed and compared with analytical solutions (if available) or approximate integral value estimates. The results show that the Simpson 3/8 method tends to be more accurate for more complex functions than the Simpson 1/3 and Trapezoid method. However, the Trapezoidal method has advantages in computational speed and algorithm simplicity. This study provides important insights for computing software users in selecting appropriate numerical integration methods based on function complexity and accuracy requirements.

Along with the development of the times, the use of information technology using computers as its media has increased rapidly. Indonesia is currently starting to adopt the concept of society 5.0 or what is known as Society 5.0, where this kind of activity focuses on community settings that are oriented towards IT-based human activities (Wijayanti et al., 2022). The application of the concept of "society 5.0" has brought many technological advances in various fields of life that are connected to the internet, especially in Indonesia. The base64 technique produces values ??during text encoding that are difficult for laypeople to understand. This encryption program has the ability to convert plain text to cipher text and reverse cipher text after it is received. Several recommendations for the development of better applications in further research can be made based on the test findings of this study and the conclusions mentioned above. These include the following: The Vigenere Chipper application can only encrypt text messages