_ Imran

Photovoltaic (PV) systems nowadays represent an essential component of renewable energy production. However, undetected faults often compromise their reliability, leading to significant energy losses and high maintenance costs. Existing deep learning approaches for PV fault diagnosis have achieved high accuracy, but they require massive, labeled datasets and high computational resources, which make them unsuitable for real-time applications. This paper proposes a lightweight, self-supervised hybrid learning framework for real-time PV fault diagnosis to address these limitations. First, the dataset is split into training, testing, and validation subsets. Thereafter, weighted class calculation steps are performed to overcome the issue of imbalance in the data. Then, a self-supervised pre-training phase is established to enable the encoder to produce effective internal representations prior to the implementation of a supervised fine-tuning classifier, characterized as a lightweight feed-forward network (Dense–Dropout–Dense Softmax), which will be trained using categorical cross-entropy and fault-type labels. Finally, a supervised fine-tuning stage is employed based on the pre-trained hybrid CNN–transformer encoder to perform PV fault classification. The experimental results indicate that the proposed approach outperforms existing models by achieving an overall accuracy of 99.8%, a recall of 99.6%, and an outstanding specificity of 100%. The confusion matrix demonstrates that classification is excellent on all operating types. Runtime analysis indicates that the model processes each sample in 2.78 ms and requires 0.07 MB to store weights of 19,429 parameters, confirming its suitability for real-time deployment. These findings highlight that using a hybrid CNN–Transformer encoder with self-supervised learning can improve fault detection and classification performance while significantly reducing inference time, making it an effective and efficient solution for intelligent PV system monitoring.

In the original publication [1], concerns were raised regarding the following references and they were removed from the reference list:17

Viruses infect millions of people worldwide each year, and some can lead to cancer or increase the risk of cancer. As viruses have highly mutable genomes, new viruses may emerge in the future, such as COVID-19 and influenza. Traditional virology relies on predefined rules to identify viruses, but new viruses may be completely or partially divergent from the reference genome, rendering statistical methods and similarity calculations insufficient for all genome sequences. Identifying DNA/RNA-based viral sequences is a crucial step in differentiating different types of lethal pathogens, including their variants and strains. While various tools in bioinformatics can align them, expert biologists are required to interpret the results. Computational virology is a scientific field that studies viruses, their origins, and drug discovery, where machine learning plays a crucial role in extracting domain- and task-specific features to tackle this challenge. This paper proposes a genome analysis system that uses advanced deep learning to identify dozens of viruses. The system uses nucleotide sequences from the NCBI GenBank database and a BERT tokenizer to extract features from the sequences by breaking them down into tokens. We also generated synthetic data for viruses with small sample sizes. The proposed system has two components: a scratch BERT architecture specifically designed for DNA analysis, which is used to learn the next codons unsupervised, and a classifier that identifies important features and understands the relationship between genotype and phenotype. Our system achieved an accuracy of 97.69% in identifying viral sequences.

Facial expression is an unspoken message essential to collaboration and effective discourse. An inner emotional state of a human is expressed using facial expressions and is very effective for communication with actual emotions. Anger, happiness, sadness, contempt, surprise, fear, disgust, and neutral are eight common expressions of humans. Scientific community proposed several face emotion recognition techniques. However, due to fewer face landmarks and their intensity for deep learning models, performance improvement for facial expression recognition still needs to be improved for accurately predicting facial emotion recognition. This study proposes a zoning-based face expression recognition (ZFER) to locate more face landmarks to perceive deep face emotions indemnity through zoning. After face extraction, landmarks from the face, such as the eyes, eyebrows, nose, forehead, and mouth, are extracted. The second step is zoning each landmark into four regions and zone-based face landmarks are passed to the VGG-16 model to generate a feature map. Finally, the feature map is given as input to fully connected neural network (FCNN) to classify facial emotions into multiple classes. Various experiments are performed on facial expression recognition (FER) 2013 and CK+ datasets to evaluate our proposed model with state-of-the-art facial expression recognition approaches using performance assessment metrics like accuracy. The accuracy of the proposed method with face features on CK+ and FER2013 are 98.4% and 65%, respectively. The experimental zoning results improve from 98.47% to 98.74% on the CK+ dataset.

With the assistance of machine learning, difficult tasks can be completed entirely on their own. In a smart grid (SG), computers and mobile devices may make it easier to control the interior temperature, monitor security, and perform routine maintenance. The Internet of Things (IoT) is used to connect the various components of smart buildings. As the IoT concept spreads, SGs are being integrated into larger networks. The IoT is an important part of SGs because it provides services that improve everyone’s lives. It has been established that the current life support systems are safe and effective at sustaining life. The primary goal of this research is to determine the motivation for IoT device installation in smart buildings and the grid. From this vantage point, the infrastructure that supports IoT devices and the components that comprise them is critical. The remote configuration of smart grid monitoring systems can improve the security and comfort of building occupants. Sensors are required to operate and monitor everything from consumer electronics to SGs. Network-connected devices should consume less energy and be remotely monitorable. The authors’ goal is to aid in the development of solutions based on AI, IoT, and SGs. Furthermore, the authors investigate networking, machine intelligence, and SG. Finally, we examine research on SG and IoT. Several IoT platform components are subject to debate. The first section of this paper discusses the most common machine learning methods for forecasting building energy demand. The authors then discuss IoT and how it works, in addition to the SG and smart meters, which are required for receiving real-time energy data. Then, we investigate how the various SG, IoT, and ML components integrate and operate using a simple architecture with layers organized into entities that communicate with one another via connections.

Context: The success or failure of any software development project significantly depends on the accuracy of its effort estimates. Software development effort estimation is the foundation for project bidding, budgeting, planning, and cost control. Problem: The literature shows that a lot of work has been done on software effort estimation. But still, there is a need for improvement in effort estimation by introducing new methodologies. The structured group-based and analogy-based effort estimations are the widely used estimation methods. Nevertheless, there are several shortcomings of using these methods such as lack of experts, lack of historical data, and biasness in expert opinion, which negatively affect the estimation results. Motivation: With the advancement of technologies, such limitations could be overcome. Such as leveraging the applicability of blockchain in several domains such as improvement in software development process and network security. Method: In this article, we have proposed a Blockchain-Based Software Effort Estimation (BBSEE) methodology to improve the software effort estimation. We employ the proposed method using Web and blockchain technologies. Moreover, we also proposed an evaluation criteria to assess the efficacy of the proposed method in terms of Mean Magnitude of Relative Error (MMRE), Mean Absolute Error (MAE), and percentage of successful predictions falling (PRED (25)). Result: We performed several case studies and analyses expert opinion of 52 organizations to present the efficacy of the proposed method. Conclusion: We observe that BBSEE method outperforms than expert judgment and analogy-based effort estimation methodologies in terms of software effort estimation.

Research interest in ceramic materials increased due to their extensive environmental, biomedical, and electronic applications. Increased demand for ceramics with specialized experimental conditions and limited resources has resulted in a higher cost for scientific practices and applications. Enormous material data is accumulated from traditional and high‐tech experimentation, but the manual recording process has shown inconsistencies in the analysis results. Recently, application based on artificial intelligence (AI) and machine learning has been able to address the issues of traditional scientific experiments in material science. However, no machine learning mechanisms are proposed for sophisticated data preparation and AI‐based discovery application of ceramics. This paper proposed an intelligent material data preparation mechanism based on ensemble learning for AI‐assisted material screening and discovery. The current method can potentially resolve the problems of missing and inconsistent material data. As a case study, a material data preparation platform for ceramic material data pre‐processing is developed. For performance evaluation of the proposed mechanism, machine learning regression models are trained before and after the imputation techniques applied to the data. Performance analysis shows that the ensemble model of deep learning network (DNN) and automated machine learning (autoML) performed better as compared to previously reported imputation approaches.

The software development industry or organizations increasingly emerging day by day have adopted global software development (GSD) practices due to the large significance of outsourcing. These industries face many challenges due to a lack of understanding customer perspective in the GSD environment. For any organization, the customer is the major stakeholder, and customer relationship management (CRM) plays a vital role in customer satisfaction with software development projects. These challenges create serious risks for any software development project’s success. Thus, CRM is a crucial challenge in the success of software projects in the GSD environment. This research study aims to address the factors that negatively influence CRM implementation in the global context and proposes a conceptual model based on the identified factors for enhancing software product quality. The systematic literature review (SLR) phase investigates the potential barriers to CRM implementation in GSD. Based on identified barriers, an initial conceptual model is developed. The proposed conceptual model is validated using a questionnaire survey of the GSD industry and CRM practitioners of Pakistan. Statistical analysis and several suitable tests are also performed to develop the final conceptual model for CRM implementation in the GSD environment. This research is performed from the client’s perspective. The results are promising and accommodating to avoid any software project failure due to customer-related issues in a GSD environment.