International Journal on Artificial Intelligence Tools

Recently, poly-pharmacy persistence has greatly improved in treating multiple diseases effectively. However, determining potential drug–drug interaction (DDIs) during the drug design is critical for controlling the target clinical drug during secure testing. In the medical field, DDIs are significant for disease diagnosis and treatment, mainly aiding researchers in predicting the link between biomolecules for efficient drug discovery (DD). Artificial intelligence (AI) has recently witnessed researchers accurately predict DDIs at minimum time consumption. Although the AI models show accurate results by aiding physicians to determine poly-pharmacy, several unresolvable issues remain in promoting reliability due to high error, complexity and cost-effectiveness. This paper aims to provide a comprehensive review using AI techniques (machine learning (ML)–deep learning (DL) models) and security enhancement techniques to improve DDI prediction and DD, respectively. The recent state of DDI prediction and the security concerns are presented initially, along with a short discussion about the need for effective techniques. Then, the critical evaluation related to existing studies is analyzed and compared to current issues faced in those existing studies. Various pharmaceutical drugs and their pros are also surveyed in addition to the security analysis for the newly invented drugs. Several assessment measures for the surveyed techniques are also conquered and put forth the need for advancements in future techniques effectively. The performance variations produced by the existing studies are also surveyed, and their use in the medical industry is also provided in this review study. The review of this research encourages the researchers to analyze the various issues faced in the pharmaceutical industry so that a novel technique can be introduced in the upcoming studies.

Nowadays, the Recommender System (RS) has gained much attention in industry and academia. RS offers valuable ideas to users while interacting with a website or application. With the advancements in the mobile environment (ME), the users’ interest has turned to the movie recommendation system. This model overcomes the problem of surplus information about movies and offers users only the relevant items by analyzing their interests and preferences. This work presents a hybrid movie recommendation system based on unsupervised clustering and supervised deep learning. Here, the dataset used is the IMDB movie reviews dataset. The clustering technique used is adaptive density-based clustering or adaptive DBSCAN. Next, the similarity calculation is performed using the cosine similarity metric. Finally, the Convolutional Neural Network (CNN) model integrated with the Adaptive Red Deer (ARD) Optimization offers the most relevant depictions on the movie list with higher accuracy. The implementation is performed on the MATLAB simulation environment. The system’s performance is measured in terms of accuracy, recall, precision, F-measure, RMSE and MAE.

Dermatograms are pivotal in the early detection of skin cancer, a disease with significant mortality rates. This paper introduces a novel feature extraction method that captures irregularities in the boundaries of abnormal skin regions. Each raw dermatogram is converted into a binary mask image using an effective segmentation algorithm. The boundary of the lesion region is extracted from the mask. The boundary, together with the centroid of the lesion mask, is used to define a set of directional vectors. An Arc is defined using these directional vectors, and a new Arc feature is calculated based on the number of times the lesion boundary crosses the arc. The proposed Arc feature is evaluated using three standard skin lesion datasets: ISBI 2016, HAM10000, and PH2. Additionally, color features and Local Binary Pattern (LBP) features are implemented for comparison. Classical machine learning algorithms are employed to evaluate these features. Results indicate that for the ISBI 2016 and HAM10000 datasets, the Arc feature set demonstrates superior classification accuracy. In contrast, the PH2 dataset benefits more from the LBP feature. Comparative analysis with recent studies highlights the dependency of accuracy on datasets and classifiers, underscoring the necessity for models incorporating feature fusion and ensemble classifiers. The proposed method outperforms traditional color and texture features and shows competitive results against deep learning models, particularly in scenarios with limited computational resources. These findings suggest that the Arc feature is a promising approach for improving skin cancer detection, although further investigation is needed to fine-tune performance, optimize classifier selection, and explore feature fusion strategies.

The purpose of this paper is to give a brief demonstration on how deontic logic can be used to help the design of robots capable of choice, equipped with artificial intelligence, by providing a framework that will help maintain ethically sound behavior. We begin by presenting an overview of the potential applications of robots and the expansion of their use in various areas of our society as well as the ethical concerns artificial intelligence and robots raise. Then, we give a quick introduction to deontic logic, highlighting its key concepts and explaining what it offers to the field of ethics. In the third part of the paper, we present our own approach to deontic logic, based on common sense reasoning. The fourth part includes three short applications of our common sense deontic logic approach in the field of artificial intelligence and robotics. These applications illustrate how deontic logic can be used to guide robots in making morally sound decisions, using examples from the health sector. In the final section, we have the conclusions of our paper as well as our limitations and plans for future research.

This paper explores future energy systems primarily powered by innovative floating reactors. These reactors exhibit high potential, particularly for supplying power to remote areas beyond the reach of traditional power grids. In this study, a novel artificial intelligence (AI) system is introduced to optimize the management of floating reactors in remote, distributed environments, where the reactors must move between different sites and energize their local grids. This research marks the first application of AI in providing management decisions for those reactor types in such distributed assembled environments. Specifically, an intelligent management system has been developed to coordinate the reactor’s operations across various sites. The proposed system leverages fuzzy logic tools, which enable it to account for multiple uncertain factors when making management decisions. The system’s performance is tested through simulated scenarios and benchmarked against a round-robin fixed time method. The results demonstrate the system’s efficiency in delivering both economic and operational benefits.

The $k$-plex is a relaxation of the classical clique structure, which is usually used to characterize the cohesiveness of a subgroup in social networks. In this paper, we propose a new notion called the $k$-plex influence to depict the outward connection of a $k$-plex. The influence of a $k$-plex is defined as the number of outside vertices adjacent to the vertices of the $k$-plex. With the new notion, we can distinguish different $k$-plexes and find more pivotal subgroups in networks. We propose an exact algorithm to find the $k$-plex with the maximum influence. The algorithm implements a branch-and-bound approach, in which we integrate a novel upper bound and an effective preprocessing strategy. We conducted experiments to evaluate the performance of the algorithm and compared it with the general mixed integer linear programming approach. The results show that both the preprocessing strategy and the upper bound can effectively reduce the search space, and the proposed branch-and-bound algorithm can solve the problem in massive graphs effectively and has a significant performance advantage over the general mixed integer linear programming approach.