Artificial Intelligence in Drug Discovery: A Review of AI Approaches for Target Identification

Faustino D.

doi:https://doi.org/10.54216/MOR.030102

International Journal of Neutrosophic Science | 2025

Neutrosophic Burr Distribution for Modeling Health Risk Factors

The Burr distribution is one of the most important and commonly used probability distribution in statistical analysis. In this study, a new class of univariate distribution based on the Burr random variable is proposed. Characteristics of the proposed neutrosophic Burr distribution (NBD) are discussed. The neutrosophic form of the proposed distribution is particularly advantageous for handling the imprecise and uncertain information commonly present in real-world problems. The statistical properties and the shapes of corresponding probability density and cumulative density functions are illustrated. Some important functions commonly utilized in survival studies are formulated within neutrosophic structures. General expressions for other distributional properties of the proposed NBD are developed under neutrosophic framework. The inverse cumulative method is used to find random numbers from the suggested model. Maximum likelihood method for estimating the model parameters is described, and the performance of estimated parameters are assessed using a Monte Carlo simulation experiment. Finally, the paper demonstrates the practical use of the proposed model through a real-world application of malaria cases per thousand population at risk.

groups

Fuad S. Alduais mail -

Zahid Khan mail

link https://doi.org/10.54216/IJNS.250423

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

Fusion: Practice and Applications | 2025

Compiler Sequence Optimization Using Machine Learning Prediction Method

Compiler optimization is crucial in improving program performance by improving execution speed, reducing memory usage, and minimizing energy consumption. Nevertheless, modern compilers, such as LLVM, with their numerous optimization passes, present a significant challenge in identifying the most effective sequence for optimizing a program. This study addresses the complex problem of determining optimal compiler optimization sequences within the LLVM framework, which encompasses 64 optimization passes, causing in an immense search space of 264264. Identifying the ideal sequence for even simple code can be an arduous task, as the interactions between passes are intricate and unpredictable. The primary objective of this research is to utilize machine-learning techniques to predict effective optimization sequences that outperform the default -O2 and -O3 optimization flags. The methodology involves generating 2,000 sequences per program and picking the one that achieves the shortest execution time. Three machine learning models—K-Nearest Neighbor (KNN), Decision Tree (DT), and Feedforward Neural Network (FFNN)—were employed to predict the optimization sequences based on features extracted from programs during execution. The study used benchmarks from Polybench, Shootout, and Stanford suites, each with varying problem sizes, to validate the proposed technique. The results demonstrate that the KNN model produced optimization sequences with superior performance compared to DT and FFNN. On average, KNN achieved execution times that were 2.5 times faster than those achieved using the O3 optimization flag. This research contributes to the field by programming the process of selecting optimal compiler sequences, which significantly reduces execution time and eliminates the need for manual tuning. It highlights the potential of machine learning in compiler optimization, offering a robust and scalable approach to improving program performance and setting the foundation for future advancements in the domain.

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Diyar Mohammed mail -

Esraa Hadi Alwan mail -

Ahmed Fanfakh mail

link https://doi.org/10.54216/FPA.180121

Volume & Issue

Vol. Volume 18 / Iss. Issue 1

Details open_in_new

American Journal of Business and Operations Research | 2025

A Multi-Server Queuing-Inventory System with Attraction-Retention Mechanisms for Impatient Customers and Catastrophes in Warehouse

This paper presents a multi-server Markovian queuing-inventory system (MQIS) that incorporates attractionretention (AR) mechanisms for impatient customers and models catastrophic inventory losses within a warehouse setting. The system consists of C identical servers, a limited waiting area, and a storage capacity of Q items. Periodic disruptions may destroy all inventory in the system, compelling waiting customers either to remain until stock is replenished or to exit the system. A subset of servers may take joint vacations when no customers are waiting. To analyze this queuing-inventory system (QIS), we derive balance equations using a three-dimensional continuous-time Markov chain framework, solving for steady-state solutions through a recursive method. We then derive performance metrics and identify special-case queuing-inventory models within the broader system. A cost-loss model is formulated to optimize the service rate and server vacation strategies, minimizing overall costs. A genetic algorithm is employed to conduct a cost analysis. We collected primary data from the Ethio Telecom district head office in Arba Minch, Ethiopia to validate our theoretical findings. The empirical analysis serves a dual purpose: to investigate performance measure sensitivity to parameter variations and to discuss an optimization problem aimed at minimizing expected total cost (ETC) while assessing the impacts of AR mechanisms and catastrophic events on ETC.

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Berhanu Mekonen Alemu mail -

Natesan Thillaigovindan mail -

Getinet Alemayehu Wole mail

link https://doi.org/10.54216/AJBOR.120203

Volume & Issue

Vol. Volume 12 / Iss. Issue 2

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2025

Optimal Bayesian Neural Network based Decision Support System for Mitotic Nuclei Detection on Histopathologic Imaging

A Decision Support System (DSS) for the recognition of mitotic nuclei (MN) on the histopathological image (HI) aids pathologists in cancer diagnoses by automating the MN detection, a key indicator of tumor proliferation and cell division. Leveraging innovative image processing and machine learning (ML) algorithms, such a system can accurately detect MN, which are crucial indicators of cell division and tumor proliferation. By automating these processes, pathologists can focus more on complicated diagnostic tasks while ensuring efficient and consistent analysis. ML approaches, comprising support vector machines (SVMs) or convolutional neural networks (CNNs) can be widely applied for the classification task. These techniques learn from annotated data to accurately discriminate between mitotic and non-MN. Incorporating these technologies into pathology workflow facilitates research efforts in oncology for improved treatment strategies, enhances diagnostic accuracy, and reduces variability among observers. This study presents an Optimal Bayesian Neural Network based Decision Support System for Mitotic Nuclei Detection (OBNN-DSSMND) technique on Histopathologic Imaging. The goal of the OBNN-DSSMND technique is to detect the mitotic and non-mitotic cells on the HIs. In the initial phase, the OBNN-DSSMND technique undergoes the bilateral filtering (BF) technique to preprocess the input images. Next, the OBNN-DSSMND technique involves a feature fusion process encompassing SqueezeNet, DenseNet, and VGG-19 models. Meanwhile, the hyperparameter selection of the DL models is performed by using the Archimedes Optimization algorithm (AOA). For mitotic nuclei detection, the OBNN-DSSMND technique applies a BNN classifier, which recognizes the presence of mitotic and non-mitotic cells on the HIs. The experimental assessment of the OBNN-DSSMND approach was examined utilizing a benchmark image dataset. The widespread simulation analysis reported that the OBNN-DSSMND technique achieves better results than other techniques.

groups

Ali Allouf mail

link https://doi.org/10.54216/IJAACI.070101

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2025

Integration of Business Process Web Services Using BPEL and QoS Optimization for Effective Composition

The importance of business procedures and web services in facilitating effective and dynamic company operations is highlighted in this section as it delves into their construction and integration. Web services are defined by their reuse and seamless integration, and they communicate and integrate using standard like XML, WSDL, UDDI, and SOAP. The importance of web service composing is emphasized throughout the section. This technique involves combining many services to handle complicated tasks and improve performance. Static (design-time), dynamic (runtime) composing approaches, together with orchestrating, and the choreography, are the main categories in the field. Using state-of-the-art methods such as BPEL (Business Process Execution Language), Petri nets, and AI-based methods, the method of composition entails three critical phases: identifying services, selection, and scheduling. To demonstrate how to deal with dependency issues, mistakes, and optimizing, this section also discusses scheduling difficulties by combining Hierarchical Task Networks (HTN) with Partial Order Planning (POP). Being compliant with QoS (Quality of Service) standards is supported by dynamically services selection, which also facilitates strong, automatic business processes. Web services have the ability to streamline Business-to-Business (B2B) interactions, improve agility, and save costs, as highlighted in this section. Companies may improve the quality of products, speed delivery, and provide individualized services by automating workflows and using dynamically composition. The study suggests cutting-edge mathematical techniques to boost performance and shows how to put them to use in practical situations. Comparing the two methods at one service, the Proposed Method completes the work in 0.16 seconds, which is 98.67% quicker than the Conventional Method's 0.3 seconds are. Because it yields quicker responses without sacrificing efficiency, the Proposed Method is more accurate. With an increase in time for execution accuracy, the suggested technique is more effective and faster at one service.

groups

Ramazan Yasar mail -

Sergey Drominko mail

link https://doi.org/10.54216/IJAACI.070102

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2025

Knowledge-Based Decision Support System for Selecting Optimal Web Services Based on QoS Attributes for Business Process Composition

Web services are a crucial part of large-scale software development and cross-organizational collaboration. This chapter discusses the challenges of selecting the finest internet services among the vast array of possibilities available, with an emphasis on quality of service (QoS) features. Web services must fulfil every requirement needed to provide optimal user experience and the efficient execution of corporate operations. In order to find the best services, we look at important quality of service characteristics including response speed, reliability, accessibility, and efficiency. In what follows, you will find a detailed method for selecting services. The approach consists of three steps: finding services, improving them according to QoS constraints, and grading those using weighted normalized techniques. At each stage, methods are provided to ensure an accurate and successful selection that meets the customer's needs. The proposed method seems to work, according to the results of the trials. The rating of services for several customers with varying limits, achieved using real-life data sets, demonstrates the approach of filtering and assessing to acquire optimal results. This method boosts the efficiency and usefulness of the selected services by combining functional and non-functional aspects. Finally, this part concludes by stressing the importance of quality of service in guaranteeing customer satisfaction and optimizing the delivery of services in competitive and fast-changing environments. Service 3 has the highest accuracy rate at 96.5%. Due to their low reaction times and high availability, Services 2 and 6 are in close second place. Services 4 and 7 have good availability ratings; however, they take longer to respond. Services 1 and 8 have moderate availability and high response times; hence, they get the lowest scores. When it comes to reliability and accuracy, Service 3 remains your most effective choice.

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Stipan Podobnic mail -

Barbara Charchekhandra mail

link https://doi.org/10.54216/IJAACI.070103

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of Advances in Applied Computational Intelligence | 2025

Automated Insect Detection and Classification using Pelican Optimization Algorithm with Deep Learning on Internet of Enabled Agricultural Sector

Recently, the combination of Deep Learning (DL) methods within the Internet of Things (IoTs) has developed in the agricultural field, especially in the domain of pest management. This study considers the implementation and development of an innovative method for Insect Detection and Classification using DL within the environment of the IoTs in agriculture. The developed system advantages advanced DL approaches for analysing images captured by IoT-enabled devices, enabling real-time identification and categorization of insect pests. By continuously incorporating these technologies, these research goals to increase the efficiency and precision of pest monitoring, finally providing to sustainable agricultural technologies and increased crop yield. This study presents an Automated Insect Detection and Classification using Pelican Optimization Algorithm with Deep Learning (AIDC-POADL) technique on Internet of Enabled Agricultural Sector. The main objective of the AIDC-POADL system is to identify and categorize various types of insects exist in the agricultural field. In the primary stage, the AIDC-POADL technique involves DenseNet-121 model to learn complex features in the input images. Also, the hyperparameter choice of the DenseNet-121 algorithm developed by the POA. At last, multilayer perceptron (MLP) model can be applied to discriminate the insects into various classes. To validate the enhanced performance of the AIDC-POADL algorithm, a series of simulations are involved. The experimental outcomes stated that the AIDC-POADL technique offers enhanced recognition results over other approaches.

groups

Karla Zayood mail -

Rama Asad Nadweh mail

link https://doi.org/10.54216/IJAACI.070104

Volume & Issue

Vol. Volume 7 / Iss. Issue 1

Details open_in_new

International Journal of Neutrosophic Science | 2025

Optimize Decision-Making in the Industrial Sector under Uncertainty: A Neutrosophic Inverse Exponential Distribution Approach

The most widely used distribution for risk management data for modeling longevity is the one-parameter inverse exponential distribution. Among alternative models, we suggest the neutrosophic inverse exponential (NIE) model, which generalizes the extended inverse exponential distributions and the classical structure. For the suggested model, we derive explicit formulations for the quantile functions, median, mode, cumulative distribution function, and probability density function. Data generating process of the proposed model under neutrosophic environment is discussed. To estimate the model parameters, we use the maximum likelihood approach. Using the proposed model, we run the simulation setup for randomly generated data. A genuine data set is also used to support the proposed model applicability.

groups

Mansour F. Yassen mail

link https://doi.org/10.54216/IJNS.250425

Volume & Issue

Vol. Volume 25 / Iss. Issue 4

Details open_in_new

Metaheuristic Optimization Review | 2025

Optimization Algorithms for Deep Learning Prediction of Liver cirrhosis: A Survey

Today, new Artificial Intelligence (AI) techniques are utilized to help doctors forecast the occurrence of diseases because of the necessity of sustaining public health and early disease diagnosis. One significant kind of liver damage is liver cirrhosis, which typically results from long-term liver damage brought on by a variety of liver conditions and diseases, including hepatitis, persistent alcoholism, or heredity. We created this review to provide an overview of liver cirrhosis since it is essential to identify it early and prevent the damage from spreading throughout the liver tissues. In order to identify liver cirrhosis from biomedical markers rather than images, this study has recently conducted nine studies overlaying it with various artificial intelligence deep learning techniques. Our suggested approach used various Machine Learning (ML) models to predict the signs of cirrhosis in conjunction with other illnesses. Because this condition is so important, it is important to summarize these studies based on the methodology and findings of detection accuracy and precision.

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Aya Ebrahim mail -

Asmaa H. Rabie mail -

El-Sayed M. El-Kenawy mail -

Hossam El-Din Moustafa mail

link https://doi.org/10.54216/MOR.030101

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new

Metaheuristic Optimization Review | 2025

Artificial Intelligence in Drug Discovery: A Review of AI Approaches for Target Identification

Artificial Intelligence (AI) has become a revolutionary solution in drug discovery and development in aspects including high costs, long times, and high failure rates. This review describes the development and focuses on areas where AI has been used for target identification, lead optimization, design of new drugs from scratch and drug repurposing. Deep learning frameworks such as generative adversarial networks (GANs), variational autoencoders (VAEs), and explainable AI (XAI) approaches have been instrumental and comparative progress in enhancing the efficacy and specificity of drug discovery processes. AI has made advances in clinical trials, trial conduct, and participant selection, as well as enhanced patient-tailored therapies for personalized medicine. Issues such as data credibility, model explainability, and algorithmic biases are still present, and logical and social sciences' cooperation and code of conduct are needed. As such, this review aligns current developments with these challenges to demonstrate the possibilities of AI in revolutionizing pharma research and enhancing health solutions worldwide.

groups

Faustino D. Reyes mail

link https://doi.org/10.54216/MOR.030102

Volume & Issue

Vol. Volume 3 / Iss. Issue 1

Details open_in_new