بحوث سكوبس — محمد حسن علوان الدليمي

Environmental inspection is very significant in agriculture because it helps the producer determine optimal input distribution and reduce the adverse effects on the environment. Conventional approaches do not have adequate spatial and temporal resolution for effective actions. This paper introduces a new concept in the monitoring of the environment in agriculture using an analysis of the physical properties of optical fiber sensors. The sensor has some advantages like no vulnerability to electromagnetic interferences, is more sensitivity, and can operate in destructive environments. The aimed system entails rainfall, temperature, humidity, and soil moisture checks, in addition to checkpoints stationed through fiber optics across the agricultural field. The comments obtained from the sensors are then assessed to offer information on environmental conditions and surroundings within a limited duration of time. The information generated by these sensors can be effectively applied for purposes of adjusting irrigation and watering, as well as, the use of fertilizers and a range of other factors that can influence the outcome of crop production, and thus, the results related to savings of resources. The investigation also involves simulation and performance evaluation of the intended optical fiber sensor system and two modulation approaches. The study proves that optical fiber sensors are reliable and can be of great benefit to conserving the environment in the use of agriculture. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026.

Power losses and voltage deviations in distribution power networks (DPNs) are high since they carry more power demand than transmission power networks. Also, voltage deviation beyond the allowable range causes voltage stability problems in the DPN. The power loss (PL) in the DPN should be kept at the minimum level for the economic operation of the electric grid. Integrating distributed generation (DG) in appropriate sites of the power networks can minimize the power losses and voltage drops. An integrated optimization approach is proposed in this paper, by combining an analytical and metaheuristic algorithm to optimize the placement and sizing of multiple DGs. The active power loss sensitivity (APLS) index is an analytical mathematical computation approach used to identify the optimal bus locations for DG placement. The modified ant lion optimization (MALO) algorithm is applied to optimize the ratings of the DG systems. The MALO algorithm is proposed by adopting the Lévy flights (LF) pattern in the random walk process (RWP). LF representation of RWPs enhances the exploration phase of the ALO algorithm and helps to obtain the near-optimal solution. The proposed integrated approach optimizes multiple units of photovoltaic (PV) and wind turbine (WT) units to minimize the multi-objective function, including AP loss and voltage deviation (VD) minimizations. The effectiveness of the proposed integrated approach is validated on the IEEE 69-bus, 85-bus, and 118-bus radial DPNs. Besides, the simulation study is extended for ant lion optimization (ALO), BAT, and artificial bee colony (ABC) algorithms-based techniques. The integrated approach has reduced the total AP loss of the IEEE 69-bus and 85-bus radial DPN from 225 kW to 70.51 kW and 316.12 kW to 162.80 kW, respectively, for the optimized three PV DG units allocation. Likewise, the total AP loss of the 118-bus radial DPN is cut down from 1296.3 kW to 432.3 kW after the optimized five PV DG units allocation. Meanwhile, the total AP LOSS of the 69-bus, 85-bus, and 118-bus radial DPNs is reduced to 4.78 kW, 53.87 kW, and 112.2 kW, respectively, after the optimized WT DG allocation. Additionally, the optimized inclusion of multiple DG units significantly minimized the VD of the DPNs. The minimum VD of the 69-bus, 85-bus, and 118-bus test systems is reduced from 0.0908 p.u., 0.1297 p.u., and 0.1312 p.u. to 0.0174 p.u., 0.0384 p.u., and 0.0201 p.u., respectively, for the multiple PV unit allocations. Similarly, the minimum VDs of the 69-bus, 85-bus, and 118-bus radial DPNs are minimized to 0.0048 p.u., 0.0190 p.u., and 0.0093 p.u., respectively, following the multiple WT DG unit allocations. The simulation findings of the APLS-MALO integrated approach are related to the various optimization techniques. The comparative study reveals that the proposed integrated approach gives a more effective and efficient solution than ALO, BAT, ABC, and other optimization techniques. Finally, the simulation findings of the APLS-MALO integrated technique are verified via the calculation of conventional statistical metrics and the conduction of a non-parametric Wilcoxon test. © The Author(s) 2025.

Purpose – This study explores tourist adoption of ChatGPT-powered digital itineraries. It investigates the factors influencing their intention to use these AI-driven travel planning applications by building upon the Unified Theory of Acceptance and Use of Technology (UTAUT) and incorporating experiential consumption theory, specifically focusing on utilitarian and hedonic values. Design/methodology/approach – This research surveyed 384 travelers who use mobile applications like ChatGPT for tourism, employing an online survey and purposive sampling. PLS-SEM was used for data analysis. Findings – The results confirmed a significant relationship between the intention to adopt ChatGPT’s digitalized itinerary and all UTAUT dimensions, with the exception of the facilitating condition. Both the hedonic and utilitarian values of personal consumption significantly motivate travelers in their behavioral intention to adopt ChatGPT’s digitalized itinerary. Practical implications – This study advises AI travel tool developers and marketers to prioritize both utilitarian and hedonic values, such as AR integration, and user-friendly interfaces. Social influence should be leveraged through in-app sharing and communities. Ethical considerations, including data privacy and algorithmic fairness, are crucial, along with adherence to data protection laws. Originality/value – This study investigates how travelers adopt AI-generated digital itineraries (like those from ChatGPT), filling a gap in research that often focuses on general smart travel app adoption. It develops a new model to explain user intentions, providing novel insights into this growing trend. © 2025 Emerald Publishing Limited

The continuous developments in vehicular communication technology have brought a significant interest in Vehicular Ad-Hoc Networks (VANETs). VANETs aim to enhance road safety, improve traffic management, and provide a suite of infotainment services to passengers. This type of network is characterized by high-speed, dynamically varying mobility, leading to increased Energy Consumption (EC), End-to-End (E2E) delay, and Routing Overhead (RO) during network communication. Various researchers have developed ways to overcome this drawback through the employment of clustering techniques in VANETs. However, utilizing clustering techniques in VANETs is critical as it requires maintaining robust communication links, optimizing resource allocation, and minimizing E2E delay. Subsequently, this paper proposes an improved Fuzzy-based Cluster Head Selection (FCHS) technique to enhance the overall performance of VANET. In VANET, the clustering is formed from Cluster Head (CH), Cluster Child (CC), and Backup-Cluster Head (BCH) along with the other network nodes. The FCHS optimizes the CH selection using a fuzzy logic algorithm based on various VANET parameters, including average distance, satisfaction degree, EC, Packet Delivery Ratio (PDR), and vehicle connectivity level. The performance of the proposed FCHS technique is simulated utilizing Network Simulator (NS) 2.35 with the Simulation of Urban MObility (SUMO) platform. The performance metrics that are considered for the result evaluation are PDR, EC, E2E delay, and RO. The overall results of the VANET is compared with two recent methods. The results show that the VANET performance with the aid of the proposed FCHS technique achieves the highest PDR, low EC, E2E delay, and RO. © 2025, Penerbit UTHM. All rights reserved.

The application of multi-generation systems has seen significant growth in recent years. This research explores an innovative Rankine organic cycle that generates electricity, hydrogen, and potable water by integrating geothermal and heat recovery as energy sources. The cycle's efficiency is assessed in two configurations: utilizing geothermal energy and not utilizing it. Calculations show that the highest exergy destruction, at 32.5%, is linked to the proton electrolyzer membrane (PEM). Additionally, the lowest exergoeconomic factor, at 7.9, is found for the PEM. The cycle generates 1.81 L/s of hydrogen and 4.52 kg/s of desalinated water. Increasing the temperature of the geothermal source from 125 °C to 161 °C leads to a 30.2% increase in hydrogen production and an 18.1% increase in desalinated water production. If geothermal energy is not used and all energy comes from heat recovery, carbon dioxide emissions will increase to 71%. © The Author(s) 2025. This article is distributed under the terms of the Creative Commons Attribution 4.0 License (https://creativecommons.org/licenses/by/4.0/) which permits any use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).

Rapid urban expansion of cities in many developing countries, such as Nigeria, is aggravating occurrences of devastating urban floods because of sudden changes in climate and uncontrolled land use. Methods based on traditional flood prediction are expensive, primarily binary-classification-based, and lacking generality, which hampers their capability for disaster preparedness purposes. In this paper, we propose a standardized multiclass flood classification framework with spatial, topographic, hydrological and meteorological covariates based on three ML classifiers, including Random Forest, Support Vector Machine and Logistic Regression. The model was evaluated strictly using stratified 5-fold cross-validation and a 20% held-out test set. From the right, the RF model recorded the highest performance accuracy, at 92%, indicating desirable generalisation and resistance to overfitting. SVM was successful with 87% and LR achieved 83%, both being relatively unstable at minor flood classification. The experimentation-based feature importance analysis indicated that the environmental data index is the most important predictor, increasing interpretation and transparency in modelling. The results introduce RF as a trustworthy multi-class urban flood classification tool in data-poor contexts, with potential applications for early warning systems, city management and climate-resilient policies in the Global South. © 2025, Penerbit UTHM. All rights reserved.

The new fourth industrial revolution is creating opportunities for artificial intelligence to boost business, society, and economies. The advance in AI algorithms is enabling new innovations. AI shows great promise in strategic decision-making, and it has the potential to transform strategic thinking, since AI-based strategies can create exceptional results. The artificial intelligence strategy is an emerging field that aims to use big data, analytics, machine learning, cognitive computing, and other intelligence technologies, to produce highquality output and function in various commercial, strategic, operational, and customer satisfaction departments. The research focuses on the General Company for Electrical and Electronic Industries, a government-owned enterprise operating under the Ministry of Industry and Minerals. This study employed a sample approach to collect data from 100 managers, particularly chosen for their pertinent decisionmaking responsibilities inside the firm. This paper looks at how strategic thinking affects the implementation of artificial intelligence strategy, therefore supporting digital leadership. The obtained data was investigated using AMOS and SPSS tools using several statistical approaches including reliability analysis, correlation, and multiple regression using the Upper Echelons Theory. The results show that artificial intelligence strategy acts as a partial mediator in the link between strategic thinking and digital leadership, so stressing the interdependence of these factors and their fundamental relevance in enabling organizational development over the period of digital transformation. © 2025 IEEE.

Integrating renewable energy with biomass valorization offers a scalable pathway toward circular and climate-resilient campus operations. This study presents a replicable model implemented at Alanya Alaaddin Keykubat University (ALKU, Türkiye), where post-consumer food waste from 30 cafeteria menus is converted into pet food and compost using a 150 L ECOAIR-150 thermal drying and grinding unit powered entirely by a 1.7 MW rooftop photovoltaic (PV) system. The PV infrastructure, established under Türkiye’s first public-sector Energy Performance Contract (EPC), ensures zero-electricity-cost operation. On average, 260 kg of organic waste are processed monthly, yielding 180 kg of pet food and 50 kg of compost, with an energy demand of 1.6 kWh h−1 and a conversion efficiency of 68.4%, resulting in approximately 17.5 t CO2 emissions avoided annually. Economic analysis indicates a monthly revenue of USD 55–65 and a payback period of ~36 months. Sensitivity analysis highlights the influence of input quality, seasonal waste composition, PV output variability, and operational continuity during academic breaks. Compared with similar initiatives in the literature, this model uniquely integrates EPC financing, renewable energy generation, and waste-to-product transformation within an academic setting, contributing directly to SDGs 7, 12, and 13. © 2025 by the authors.

Email spam refers to junk files, images, or data sent through email that might contain links leading to phishing websites. This email is often sent repeatedly to random users, and sometimes it may be dangerous. The objective of this study is to predict and recognize whether the emails sent to users are spam or not by using machine learning classification algorithms. Email Spam Classification (ESC) datasets are used in this study for spam detection tests. The ESC datasets contain 5172 rows and 3002 columns of spam and non-spam features. The methodology used in this study is the CRISP-DM to guide the process of evaluating the performance of three machine learning algorithms: Naive Bayes (NB), Logistic Regression (LR), and Random Forest (RF). Subsequently, an ensemble model that integrates the three machine learning algorithms is proposed to improve the performance of spam email recognition. The selected evaluation metrics are F1-Score, accuracy, precision, and recall. Based on the results, the RF algorithm has the highest accuracy of 97.3% in classifying spam emails, with an F1 score of 96.8%, precision of 96.2%, and recall of 96.0%. The NB achieves the best second results, which are slightly different from the RF, and the LR achieves considerably lower results than the other two algorithms. The ensemble model that integrates the three algorithms performs best in classifying spam emails with 98.9% accuracy, 97.6% precision, 97.4% recall, and 96.7% F1-score. © 2024, Penerbit UTHM. All rights reserved.

Breast cancer is a result of uncontrolled human cell division. The vast growth of breast cancer patients has been an issue worldwide. Most of the patients are women, but breast cancer also affects men with a much lesser percentage. Breast cancer might lead to death for those who are suffering from it. Numerous types of research have been done to make an early diagnosis of breast cancer. It has been proven that the tumor can be detected by using an ultrasound image. Artificial Intelligence techniques have been used to detect breast cancer fundamentally. This paper studies the effectiveness of deep learning (DL) techniques in automating breast cancer diagnosis. Subsequently, the paper evaluates the diagnosis performance of three DL models utilizing the criteria of accuracy, recall, precision, and f1-score. The Densenet-169, U-Net, and ConvNet DL models are selected based on the examination of the related work. The DL diagnosis process involves identifying two types of breast cancer tumors: benign and malignant. The evaluation outcomes of the DL models show that the most effective model for diagnosing breast cancer among the three is the ConvNet, which achieves an accuracy of 91%, a recall of 83%, a precision of 85%, and an F1-score of 83%. © 2024, Politeknik Negeri Padang. All rights reserved.

Securing communications in drone networks is an essential aspect of ensuring good network performance. Data transferred over the Internet of Drones (IoD) Communications, which is rapidly growing, holds crucial information for navigation, coordination, data sharing, and control, and enables the creation of smart services in many sectors. Sixth-generation (6G) mobile systems are anticipated to be impacted by the plethora of IoD. The possibility of malevolent drones intercepting or altering data before it reaches its target is a serious worry. Operations on IoD networks may be hampered by this, and safety issues may arise. Utilizing three security levels, the suggested method solves the issue of malicious drones in the IoD network. The suggested system’s first level allocates a trust value to IoD drones based on behaviors including prior drone behavioral histories, packet losses, and processing delays. This can be accomplished by choosing drones as investigators to monitor the actions of neighboring drones and assess the level of trust value. The second level involves communication protection, which is accomplished by historical communication behavior. The purpose of the final security level is to safeguard the reliability of the data used to calculate trust values. The fundamental topical of our proposed system is to propose and explore a novel tactic for detecting malicious UAVs within the internet of drone framework, using theoretical and simulations models. Because that 6G networks are still now in the developmental stage, the results presented are based on predictive analyses and simulations rather than real-world applications. © 2024 River Publishers.

The DTA-LI system's fusion data method is crucial in the monitoring of appliance loads for the purposes of improving energy efficiency and management. Common home electrical devices are identified and classified from smart meter data through the analysis of voltage and current variations, allowing for the measurement of energy usage in residential buildings. A load identification system based on a decision tree algorithm may infer information about the residents of a building based on their energy usage habits. Better power savings rates, load shedding management, and overall electrical system performance are the results of the clusters' ability to capture families' purchasing patterns and geo-Demographic segmentation. The DTA-LI system's fusion data method presents a promising avenue for improving residential buildings' energy performance and lowering their carbon footprint, especially in light of the widespread use of smart meters in recent years. © 2023, American Scientific Publishing Group (ASPG). All rights reserved.

As a result of the COVID-19 pandemic in numerous nations across the globe, all public schools in Iraq were forced to close. Teachers were put in a difficult position because of the necessity of accommodating online learning. The Ministry of Education in Iraq has obligated all schools to substitute face-to-face teaching processes with online learning. The Ministry also put forward several educational projects and platforms, such as Newton, Classera, and My School through Educational TV. Although these projects were urgent, they created many issues for students, teachers and schools. The results of this study reveal that online learning in Iraq experienced substantial growth during the breakout of the Corona virus crisis. Schools have the potential to take advantage of the new educational methods, and also transform their teaching practices. On the other hand, this new approach faces many challenges, including technological, educational, and social. Unreliable communications and a weak Internet are among the biggest challenges, as well as many students' lack the electronic devices required for learning. Moreover, despite the abundance of digital resources, both teachers and students have low abilities at using electronic tools. The study recommended the necessity of accelerating technological readiness by training students and teachers to use technological applications and providing educational platforms that have the ability to meet their needs, as well as strengthening the role of educational supervision. © 2023 Author(s).

A short time ago Internet of Things (IoTs) is being applied in many fields like healthcare systems, disease forecasting, etc. Even though the IoTs has enormous promise in a variety of applications, there are several areas where it may be improved. In the present work, we have concentrated on improvement of the performance of IoT by adding two technologies such as machine learning algorithms (Naïve Bayes (NB), Random Forest (RF)) and Ant Colony Meta-Heuristic (ACMH) algorithm to select best features from data. The efficient proposed framework applied on the data of SARS-Co V2 for disease prediction to minimize the time consumption and improve the accuracy of forecasting COVID disease. Thus, the lifetime network of IoT will lead to an increase. The performance of proposed work evaluated using reliable metrics such as precision, accuracy, running time, balance accuracy, recall, and F-Measure. We conclude from the results of evaluating, that ML algorithms in IoT achieved best performance than without using ACMH algorithm; RF with ACMH in IoT framework achieved best performance that NB with ACMH algorithm. But NB is best from RF in running time with and without ACMH algorithm. © 2022 IEEE.

in this paper, the design and evaluation of the Wi-Fi heat map generator are proposed. There are many similar systems are presented with different techniques and features but, the proposed system is a collection of many systems' features that managed all together in one completed system to facilitate user requirements. The existing Wi-Fi network heat map generator is either an expensive tool, not free for the user or not easy to use. Therefore, we generate a system of the low-cost device and efficient functionality that meets user requirements with a less possible cost. The main feature of the proposed kit is measuring the access point's signal strength and predicting the high/low coverage areas of the access point in any wireless network and show the result as a heat map. The system supported by an application that facilities access and choose a network to assess and analyze. The result of using the system is more efficient in terms of access point position and signal strength measurement in different locations as it shows the result in heat maps which makes it clearer than mathematical operations and equations. Moreover, the system is available for everyone, easy to use, low cost and meets user's requirements. Wi-Fi analyzing and heat map generating system becomes very much required and an important tool that utilizes to notify the coverage areas of a network in any location. It helps in notifying the problem and solving it in terms of exceeding the range of network coverage areas and increase the strength in some locations that leak the signal. © 2019 IEEE.