Al-Salam Journal for Engineering and Technology

Numerical solutions to integer-order differential equations frequently employ decomposition as one of many methods. This paper provides a generalization of integer-order differential equations into fractional order, which is more general, as well as generalizing the decomposing method into the fractional case and then applying it to solve differential equations of fractional order in both space and time. A fractional calculus's theorems and properties to generalize both differential equations and the decomposition method have been utilized. This method to solve different fractional time and space partial differential equations, demonstrating its ability and applicability to various problems have been applied. The method to various cases to highlight its strengths has been used. The graphs and tables of results obtained using Matlab programs have been presented and discussed.

That research aims to develop road maintenance operations that include the road survey process to determine types of defects, knowing the type of proposed maintenance, solving the problem of reporting road problems, and also seeks to facilitate the process of road maintenance at the lowest possible costs To achieve these goals, a project is being designed that automates road maintenance and uses artificial intelligence to determine kind of defect in the way by using the libraries (tensorflow with Detection Object). The proposed study will reach a number of results, which are identifying the defect, knowing its type, , and conducting some engineering operations and calculations to determine the state of the roadway, as a final output. This study will use a deep learning-based object detection method to identify road cracks under different shooting, weather, and illumination scenarios. This makes it possible to identify any cracks in a very short amount of time and at a low cost. Images will take in different weather conditions, such as the intensity of brightness being high, medium or very low. They will also be taken in foggy, rainy and dark conditions. Pictures are also taken at different distances (such as one meter, half a meter, one and a half meters) to study the behavior of the pictures and the possibility of extracting defects from them in different circumstances.

This research aims to provide a comprehensive and in-depth review of the field of machine learning, focusing on its types and techniques, and clarifying the impact of this approach on many fields. Machine learning techniques are one of the most important pivotal tools that contribute to understanding and analyzing data more deeply and accurately. These techniques allow for improving efficiency and enhancing the learning ability of intelligent systems, making them essential in modern technological developments. The main objective of this research is to provide a classification that includes the types of machine learning and review the different techniques used in this field with the results of the impact of using these techniques as well as clarifying the benefits and disadvantages of these techniques while clarifying the impact of these techniques on different fields. The research is based on reviewing the strengths and weaknesses of these techniques and addressing the most important ways to deal with them. The research also includes studying the impact of machine learning on various fields, as well as the challenges and opportunities available to benefit from machine learning to obtain useful and comprehensive results on a global scale and discussing the future direction of machine learning techniques. This study, despite our entry into the crossroads of technological development, has demonstrated how deep learning techniques can be used in various fields to improve outcomes, highlight the importance of machine learning in enhancing intelligent technologies, and show how this learning can play a pivotal role in enhancing the learning and adaptive capabilities of intelligent systems and improving the performance of these systems to advance significantly in various fields.

Central Pivot Irrigation System (CPIS) is a significant method for intelligent irrigation used in global agriculture. It is used to cultivate important crops like wheat, which contribute to global food security. However, the CPIS encounters technical issues that result in malfunctions in its automatic control system, leading to damage to the primary pipes and towers. This can result in significant material losses for farmers and their crops. Repairing the system is also a time-consuming process. To address this issue, a study was conducted using YOLO algorithm which contains several models, and this study used the Yolov7 models to detect defects in the CPIS machine accurately. The study used a dataset gathered from agricultural areas in Salah al-Din Governorate. The models were used to determine whether the CPIS was in a safe or dangerous state. The RGB color system with Yolov7x achieved a 95% detection rate with an accuracy, F1-score, and precision values of 0.798 and 0.954. Similarly, Yolov7x achieved a 93% detection rate with accuracy, F1-score, and precision values of 0.77 and 0.932 in the Grayscale color system. Based on the outcome, this study can conclude that the yolov7x model with RGB Color System accurately detects the CPIS in both its safe and dangerous states. This makes them useful in real-time systems for CPIS defect monitoring and control.

Polluted water causes diseases, affecting the ecosystem. Water quality is crucial due to high demand. Regular monitoring is essential for a consistent supply of fresh water. Internet of Things (IoT) advancements enable real-time monitoring and timely intervention for water quality issues. The current investigation evaluated a proposed smart, cost effective, high-efficacy IoT application of a water quality detection device that real-time monitors six measurement points along the University of Baghdad canal for five water quality parameters. With the Arduino platform and specific sensor properties, a low-cost monitoring system has been built. The data is shown on an LCD monitoring screen and presented on a Wi-Fi module before being transferred to the cloud and presented on a smartphone and internet platform. The results from the monitoring system for both winter and spring water quality are within the permissible limit according to standard limitations were average pH 7.687, total dissolved solid 406.75 mg/l, turbidity 25.883 NTU, and electrical conductivity 630.8 µS/cm. This framework may successfully offer an environment where there is enough water for agricultural purposes while also keeping a careful check on the contamination of water resources. On the other hand, this permits a well-managed water quality standard.

Through a systematic review of the literature, this study provides a comprehensive summary of the latest findings on the acceptance and application of blockchain technology in supply chain management. It was founded on a comprehensive analysis of thirty studies published in prestigious journals. The need for secure, decentralized AI systems to thwart potential cyberattacks is growing as AI is increasingly used in cybersecurity. The ideal choice for enhancing the security and privacy of AI systems is blockchain technology (BT), due to its immutable and decentralized data storage properties. This comprehensive literature review focuses on the integration of decentralized IT with cybersecurity. It provides a comprehensive BT and IoT cybersecurity taxonomy to get you started. This work contributes to a more sophisticated understanding of the integration between BT and IoT by demonstrating compelling results that highlight the benefits of these connections for cybersecurity. These findings are coupled with the potential cybersecurity advantages and difficulties of IoT. It also addresses the beneficial uses of blockchain-enabled IoT to solve cybersecurity problems. Finally, this paper addresses future directions for blockchain-based cybersecurity research across a range of sectors, including industry, agriculture, IoT, and health.