Proper modeling of PV cells/modules through parameter identification based on the real current-voltage (I-V) data is important for the efficiency of PV systems. Most related works have concentrated on the classical single-diode model (SDM) and double-diode model (DDM) and their parameter extraction by various metaheuristic algorithms. In order to render more accurate and representative modeling, this paper adds a small resistance in series with the diodes in SDM and DDM. The new models are named reconfigured SDM (Reconfig-SDM) and reconfigured DDM (Reconfig-DDM), and they have not been studied so far as we know. A squirrel search algorithm (SSA) is employed to globally find the parameters of the new models. The performance achieved is experimentally tested on both a commercial RTC France solar cell and a CS6P-220P polycrystalline PV module located at Düzce University in Türkiye. A vivid comparison of experimental findings, observation, and analysis clearly demonstrates that the proposed Reconfig-SDM and Reconfig-DDM tuned by the SSA have better capacity and effectiveness for modeling PV devices than some cutting-edge approaches. Specifically, compared with the best-performing approach in the literature, Reconfig-SDM and Reconfig-DDM could reduce the error rate up to 0.37% and 2.58% for the solar cell, and 3.21% and 29.0% for the solar module.

The human digestive system's electrical activity may be recorded noninvasively by Electrogastrography (EGG). Electrogastrograms are recordings of the electrical activity produced by the stomach muscles. EGG Several gastrointestinal disorders may be diagnosed and their severity measured using EGG signal properties. The literature has several contributions to the categorization of EGG signals. The majority of them make use of either the EGG's frequency or time data. The wide variety of EGG signals is a challenge for current automated categorization methods. Therefore, this study's objective is to develop a lightweight classifier that achieves high classification accuracy while using little processing resources. To acquire normal and abnormal EGG signals at a reasonable cost, a three-electrode measuring device is created here, with classification performed by a hybrid of Linear Vector Quantization and the African Buffalo Search Algorithm (HLVQ-ASO). The results show that the information richness of recorded EGG signals from healthy persons is greater for EGG signals captured using a surface electrode with a contact diameter of 19 mm as compared to 16 mm. To demonstrate their validity and degree of classification accuracy, the results computed using the suggested classifiers are compared with the current classifiers like Artificial Neural Network, Multimodal Support Vector Machine (MSVM), and Improved Convolutional Neural Network (CNN). Additionally, the HLVQ-ASO-based classification method is effective in differentiating between normal and diabetic EGG signals, found a sensitivity of 97% and a specificity of 98.8%. For a dataset of 500 samples, the classification accuracy is 97%.

During the software development process, numerous bugs are reported daily in the software bug repositories. Bug triage assigned these bugs to the most relevant and expert developer for resolution. Moreover, assigning bugs to an incompetent or an over-engaged developer causes repeated reassignment to other developers until it is resolved. This problem can be solved by devising a triage process that assigns bugs to not only expert developers but also to those who are either under-engaged or reasonably engaged but are not over-engaged in work. This paper has designed and implemented work engagement sensitive bug triage that resolves the issue of assigning bugs to developers considering their due work engagement, expertise as well as the current state of activity. For this purpose, a developer profile is built by using metrics to generate three types of scores: technical skill, work engagement and work experience. Metadata features like developer-name, email, developer-work-experience in bug resolution, last and present-work-activity, timestamp, component and priority are used for it. A multi-criteria-based Henry-Garret technique is used to generate a single ranked list of developers from three ranked lists. The performance of the proposed approach is evaluated on four large OOS projects: Mozilla, Eclipse, Netbeans, and Open Office covering 895,439 bug reports accumulated for 33 years of development. The overall system accuracy of the proposed triage using all four datasets is 91.96 +/- 0.05% which is 5.87% better than previously published work. The proposed method is achieved up to 90.30 +/- 0.05 and 97.1 +/- 0.05 MRR and accuracy of reassignment respectively that indicates how significantly it re-assigns the bug to the relevant developers. The results demonstrate improvement in the accuracy of software-bugs triaging as well as a reduction in the probability of bug-tossing.

Saudi Arabia's agriculture heavily depends on effective water management, given its limited freshwater resources and arid climate. Real-time monitoring of soil moisture levels, weather conditions, and crop watering needs, facilitated by IoT integration, plays a crucial role in conserving water and minimizing waste. The resultant improvements in crop yields and quality are essential for the long-term success of agriculture in the country. This study employs the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method to investigate the transformative potential of the Internet of Things (IoT) in enhancing water management practices in Saudi Arabia's agriculture sector. The research begins by highlighting the significance of water management in agriculture, emphasizing the proportion of land in Saudi Arabia allocated to agricultural purposes. The problem statement underscores the pressing challenges in water management, encompassing issues such as water scarcity, inefficient irrigation methods, and the need for real-time data to inform decision-making. To address these challenges, the study proposes an IoT-based Agricultural Water Management System (IoT-AWMS) that leverages sensors, real-time data analytics, and machine learning algorithms. This system is designed to optimize water utilization in agriculture. Simulations conducted within the study demonstrate a significant enhancement in water usage efficiency, resulting in reduced water wastage and increased crop yields. In conclusion, this research underscores the critical importance of the proposed IoT-based water management system for Saudi Arabia. It is positioned as a valuable tool for mitigating water scarcity challenges and promoting environmentally sustainable agricultural practices in the country.

In recent years, the growth of female employees in the commercial market and industries has increased. As a result, some people think travelling to distant and isolated locations during odd hours generates new threats to women’s safety. The exponential increase in assaults and attacks on women, on the other hand, is posing a threat to women’s growth, development, and security. At the time of the attack, it appears the women were immobilized and needed immediate support. Only self-defense isn’t sufficient against abuse; a new technological solution is desired and can be used as quickly as hitting a switch or button. The proposed Women Safety Gadget (WSG) aims to design a wearable safety device model based on Internet-of-Things (IoT) and Cloud Technology. It is designed in three layers, namely layer-1, having an android app; layer-2, with messaging and location tracking system; and layer-3, which updates information in the cloud database. WSG can detect an unsafe condition by the pressure sensor of the finger on the artificial nail, consequently diffuses a pepper spray, and automatically notifies the saved closest contacts and police station through messaging and location settings. WSG has a response time of 1000 ms once the nail is pressed; the average time for pulse rate measure is 0.475 s, and diffusing the pepper spray is 0.2–0.5 s. The average activation time is 2.079 s.

Every farmer requires access to rainfall prediction (RP) to continue their exploration of harvest yield. The proper use of water assets, the successful collection of water, and the successful pre-growth of water construction all depend on an accurate assessment of rainfall. The prediction of heavy rain and the provision of information regarding natural catastrophes are two of the most challenging factors in this regard. In the twentieth century, RP was the most methodically and technically complicated issue worldwide. Weather prediction may be used to calculate and analyse the behaviour of weather with unique features and to determine rainfall patterns at an exact locale. To this end, a variety of methodologies have been used to determine the rainfall intensity in Saudi Arabia. The classification methods of data mining (DM) approaches that estimate rainfall both numerically and categorically can be used to achieve RP. This study, which used DM approaches, achieved greater accuracy in RP than conventional statistical methods. This study was conducted to test the efficacy of several machine learning (ML) approaches for forecasting rainfall, utilising southern Saudi Arabia's historical weather data obtained from the live database that comprises various meteorological data variables. Accurate crop yield predictions are crucial and would undoubtedly assist farmers. While engineers have developed analysis systems whose performance relies on several connected factors, these methods are seldom used despite their potential for precise crop yield forecasts. For this reason, agricultural forecasting should make use of these methods. The impact of drought on crop yield can be difficult to forecast and there is a need for careful preparation regarding crop choice, planting window, harvest motive, and storage space. In this study, the relevant characteristics required to predict precipitation were identified and the ML approach utilised is an innovative classification method that can be used determine whether the predicted rainfall will be regular or heavy. The outcomes of several different methodologies, including accuracy, error, recall, F-measure, RMSE, and MAE, are used to evaluate the performance metrics. Based on this evaluation, it is determined that DT provides the highest level of accuracy. The accuracy of the Function Fitting Artificial Neural Network classifier (FFANN) is 96.1%, which is higher than that of any of the other classifiers currently used in the rainfall database.

: Biometric applications widely use the face as a component for recognition and automatic detection. Face rotation is a variable component and makes face detection a complex and challenging task with varied angles and rotation. This problem has been investigated, and a novice algorithm, namely RIFDS (Rotation Invariant Face Detection System), has been devised. The objective of the paper is to implement a robust method for face detection taken at various angle. Further to achieve better results than known algorithms for face detection. In RIFDS Polar Harmonic Transforms (PHT) technique is combined with Multi-Block Local Binary Pattern (MBLBP) in a hybrid manner. The MBLBP is used to extract texture patterns from the digital image, and the PHT is used to manage invariant rotation characteristics. In this manner, RIFDS can detect human faces at different rotations and with different facial expressions. The RIFDS performance is validated on different face databases like LFW, ORL, CMU, MIT-CBCL, JAFFF Face Databases, and Lena images. The results show that the RIFDS algorithm can detect faces at varying angles and at different image resolutions and with an accuracy of 99.9%. The RIFDS algorithm outperforms previous methods like Viola-Jones, Multi-block Local Binary Pattern (MBLBP), and Polar HarmonicTransforms (PHTs). The RIFDS approach has a further scope with a genetic algorithm to detect faces (approximation) even from shadows.

Abstract: Energy Harvesting (EH) is an emerging communications paradigm to defeat the limitation of network longevity by recharging the nodes by harvesting energy from the environment. The Energy Harvesting Network (EHN) requires a stable and efficient power control scheme like other conventional communication systems. It is more complicated than conventional communication networks, in that it should not only consider the quality of service requirements of the network but also adapt to the randomness of the energy arrival. In this thesis, several optimal offline and online resource allocation strategies for point-to-point and two-hop EH communication networks over wireless fading channels are investigated. As a first step, the RGWF (Recursive Geometric Water-filling) algorithm is introduced, which provides an optimal offline transmission policy for a point-to-point EH communication system. Next, a network composed of a source, a relay, and a destination, where the source is an EH node is considered. Joint time scheduling and power allocation problems are formulated to maximize the network throughput by considering conventional and bufferaided link adaptive relaying protocols. Based on the modified RGWF algorithm, the joint power allocation and transmission time scheduling problem are decoupled, and efficient offline schemes are proposed for a two-hop wireless network for delay-tolerant and delay sensitive applications. In the second part, the aim is to obtain the optimal transmission policy that maximizes the average total throughput of a point-to-point EH communication system with low and high data arrival rate in an online manner. The solution is obtained using dynamic programming by casting the proposed problem as a semi-Markov decision process (SMDP). In a delay-tolerant approach with high data rate, a cross-layer adaptation is considered, where the proposed policy chooses modulation constellation for EH networks dynamically, depending on battery state, data buffer state in addition to channel state. The proposed SMDP-based dynamic programming approach has proven to be dynamically adaptive to the change of the channel and/or buffer states that optimally satisfy the BER requirements at the physical layer, and the overflow requirements at the data-link layer.

Energy harvesting (EH) technology in wireless communication is a promising approach to extend the lifetime of future wireless networks. A cross-layer optimal adaptation policy for a point-to-point energy harvesting (EH) wireless communication system with finite buffer constraints over a Rayleigh fading channel based on a Semi-Markov Decision Process (SMDP) is investigated. Most adaptation strategies in the literature are based on channeldependent adaptation. However, besides considering the channel, the state of the energy capacitor and the data buffer are also involved when proposing a dynamic modulation policy for EH wireless networks. Unlike the channeldependent policy, which is a physical layer-based optimization, the proposed cross-layer dynamic modulation policy is a guarantee to meet the overflow requirements of the upper layer by maximizing the throughput while optimizing the transmission power and minimizing the dropping packets. Based on the states of the channel conditions, data buffer, and energy capacitor, the scheduler selects a particular action corresponding to the selected modulation constellation. Moreover, the packets are modulated into symbols according to the selected modulation type to be ready for transmission over the Rayleigh fading channel. Simulations are used to test the performance of the proposed cross-layer policy scheme, which shows that it significantly outperforms the physical layer channel-dependent policy scheme in terms of throughput only.

Energy harvesting (EH) becomes more desirable to save the world's energy consumption. This paper investigates energy resource allocation problem for EH networks. We propose a resource allocation framework based on a Semi-Markov Decision Process (SMDP). The objective of the framework is to provide a solution for a throughput maximization problem in EH networks by maximizing the total long-term expected reward of the EH system. The system reward is derived by considering both the income and the cost of the EH wireless communications. The numerical results illustrate a significant expected reward performance gain over a Greedy scheme. Moreover, simulations illustrate that the proposed approach is efficient and provides important guidelines for network deployment and resource management in a green radio network with EH technology.

This research work considers the utilize of energy harvesters, instead of conventional time-invariant energy sources, in wireless communication. For the purpose of exposition, we study the traditional two-hop communication system for delay limited (DL) and delay tolerant (DT) relaying networks over fading channels, in which the source node transmits with power drawn from energy harvesting (EH) sources and the relay transmits with conventional non-EH sources. We address the throughput maximization problem for the proposed system model for DL and DT cases. We find that the optimal power allocation algorithm for the single-hop communication system with EH constraints, namely, recursive geometric water-filling(RGWF), can be utilized as a guideline for the design of the two-hop system. We first introduce RGWF algorithm and we show the advantages of the geometric approach in eliminating the complexity of the Karush-Kuhn-Tucker (KKT) condition as well as providing a closed-form and exact solutions to the proposed problem. Based on the RGWF algorithm, we propose offline joint power allocation and transmission time scheduling schemes for DL relaying network and DT relaying network. We also propose efficient online resource allocation schemes for both relays’ cases. The performance of the proposed schemes is evaluated via simulation and the results demonstrate that a network with delay tolerant ability provides better performance in term of throughput.

In this paper, we consider a two-hop communication system model, where a source is able to harvest energy and a relay is non-EH half-duplex node over a fading channel. We address the throughput maximization problem for the proposed system model with and without delay tolerant. Two effective algorithms are proposed to solve the joint power allocation and transmission time scheduling problems. The performance of the proposed schemes is evaluated via simulation and the results demonstrate that a network with delay tolerant ability provides better performance in term of throughput.