Software Fault Prediction (SFP) is the process of predicting fault-prone software constructs during the initial phases of software development. Deep Neural Networks (DNN) have been hugely successful in the field of computer vision, audio, etc., where the input data is correlated spatially and temporally. In contrast, SFP operates on tabular data, rows of software metrics that lack the inherent spatial structure exploited by convolutional architectures in image domains. The authors have tried to remodel the most successful Convolutional Neural Network (CNN) for tabular data. A novel framework is proposed employing a tree-based feature selection technique, LSelect, to find the most significant features and an adaptive 1-dimensional Convolutional Neural Network (ACNN) for the classification task, which selects an optimal learning rate automatically. ACNN converts the tabular data (1-D) into 2-D using adaptive pooling layers, thereby forming an image from 1-D data. The framework classification results (Area under Curve) are compared with nine state-of-the-art algorithms, such as XGBoost, LightGBM, etc., and performance is validated using the Bayesian Signed Rank Test. It is found that the proposed framework performs comparably with the state-of-the-art methods with reduced model complexity. Also, the LSelect feature selection technique improves average model performance by 1.3%.

The Internet of Things (IoT) with its pervasive connectivity imposes substantial challenges regarding privacy, security, and trust. Traditional security measures, such as cryptography and access control, often prove inadequate in the dynamic and resource-constrained IoT environment. The decentralized nature of IoT networks, coupled with the heterogeneity of devices, demands innovative security solutions. Trust is essential in human interaction and plays a crucial role in reducing challenges and security risks within IoT networks. This article focuses on trust-based methods for securing IoT systems, particularly within the context of Wireless Sensor Networks (WSNs). Trust-based approaches offer advantages such as lower computational complexity, leading to improved power management compared to traditional authentication and encryption methods. This article examines the latest trust models, as well as machine learning and deep learning algorithms specifically designed and implemented to enhance the security and reliability of trust-based WSN-assisted IoT applications.

Robotic manipulation is a challenging research area, particularly in developing policies that generalize across diverse real-world scenarios. While real-world data can be slow and unsafe, simulations offer a safer and faster alternative. However, training in simulations can still be time-consuming, hindering rapid model iteration. This paper explores using graphics processing unit (GPU) acceleration to speed up training for robotic manipulation tasks in simulations. By comparing GPU and CPU performance, we demonstrate a significant reduction in training time. The findings show that GPU hardware enhances policy development efficiency, accelerating research and applications, including sim-to-real transfer. Additionally, it broadens exploration of state and action spaces, providing agents with a diverse range of training experiences. A simulation benchmark was also created to test GPU acceleration, detailing task selection, environment setup, and performance measurement. This benchmark forms the basis for evaluating the speedup achieved by GPUs in training robotic manipulation models.

This paper presents a systematic literature review on Intelligent Process Automation (IPA), focusing on its role in modernizing business operations. It explores the integration of AI, including machine learning and robotic process automation, and their combined impact on improving efficiency and supporting human-centric tasks. The review examines the synergy between AI and human expertise, highlighting how this collaboration enhances decision-making, reduces errors, and speeds up task execution. It also investigates how digital assistants within the IPA framework redefine operational processes, supported by case studies and empirical research. The paper addresses challenges and ethical concerns related to AI integration, offering insights on risk management and ethical AI use. In conclusion, it emphasizes the paradigm shift created by AI-driven IPA, optimizing business processes and focusing on cognitive tasks, and contributing to the ongoing discussion about AI's transformative impact on human-machine collaboration.

Improving the learning process requires to improve the cognitive traits of individuals with low mental skills. The artificial intelligence (AI) has been used to support the different individuals with impairments. People with Down syndrome fall in intellectual impairment. Different AI techniques of convolution neural network, artificial neural network and decision tree are widely applied to address the different cognitive traits. We have summarized the artificial intelligence review utilized for such individuals. The aim of this research article is investigate the usability of computational intelligence for addressing the deficits of cognitive skills and other traits. The individuals with cognitive impairment survive with limited mental challenge, therefore, they hardly perform daily life assignments. The individuals with down syndrome face mild to severe cognitive challenges that affects to their daily life activities, education and performing employment. So, they can have reduced the social and economic burden of their family and to make their live productive. Achieving these goals requires improvement in their cognitive challenge. A survey of (N = 50) of the individuals of Down syndrome has been carried out with the support of team of psychologists and teachers of homogeneous education system.

Proteins are fundamental compounds in biological processes during the analysis of drug target indication for drug repurposing. The identification of relevant features is a necessary step in determining protein structure. A classification technique is used to identify the most important features in a dataset, which is why feature selection is so important. For protein structure prediction, recent research has developed a wide range of new methods to improve accuracy. The authors use principal component analysis (PCA) with correlation-matrix-based feature selection to analyse breast cancer data. In this paper, they discussed a therapeutic agent that is used to reduce the dataset by reduction-based algorithm and after that applied reduced dataset labelled as Standard Gold Dataset on machine learning model to analyze drug target indication. They get the higher accuracy of 92.8%, 93.9%, and 95.3%, each of the three datasets with 200, 500, and 1000 features with SVM with RBF kernel function. Also they found the best result, 97.8%, with the same classifier.

The tomato crop is a strategic crop in the Egyptian market with high commercial value and large production. However, tomato diseases can cause huge losses and reduce yields. This work aims to use deep learning to construct a robust intelligent system for detecting tomato crop diseases to help farmers and agricultural workers by comparing the performance of four different recent state-of-the-art deep learning models to recognize 9 different diseases of tomatoes. In order to maximize the system's generalization ability, data augmentation, fine-tuning, label smoothing, and dataset enrichment techniques were investigated. The best-performing model achieved an average accuracy of 99.12% with a hold-out test set from the original dataset and an accuracy of 71.43% with new images downloaded from the Internet that had never been seen before. Training and testing were performed on a computer, and the final model was deployed on a smartphone for real-time on-site disease classification.