A sustainable inventory model is developed for a single item with price-dependent demand and time-dependent holding cost. In any business, deterioration is imminent with time, but the chances of deterioration in the initial stage are much higher in electronic and semiconductor devices. This study integrates a two-parameter Weibull deterioration model to research such businesses. Investment in preservation technology is made to reduce the effect of deterioration on the model. Shortages are permitted in this paper and assumed to be partially backlogged. The suggested model is solved analytically to minimize the total cost as well as the optimal order quantity. Numerical experimentation is performed to authenticate the proposed model. Sensitivity and graphical analysis are carried out for the total cost function and order quantity to validate the proposed model. Managerial insights of this paper are discussed for better implementation.

Cybercrime is one of the fastest-growing crimes worldwide.It is observed that every seven seconds, cyber attackers penetrate cyber systems.While detecting an anomaly or attack, the log system is one of the crucial components of any system storing and managing all the events.It has always been challenging to detect an anomaly in logs.This is because of continuous and ever-changing log events and their mutability property.In this paper, we develop a machine learning-based artificial intelligence approach to address this issue of log analysis by proposing two modules.The first one is anomaly detection using different machine learning models.The second one is a distributed immutable storage system for securely storing the logs.In addition, we present a descriptive and user-friendly web application by integrating all modules using HTML, CSS, and Flask Framework on the Heroku cloud environment.The results demonstrate that the proposed hybrid machine learning models are capable of achieving 99.7% accuracy in detecting network anomalies.

Optimizing Quality of Service (QoS) in network traffic management is essential for efficient data flow within a network. Fuzzy logic is a powerful tool for enhancing QoS, enabling intelligent decisions based on imprecise data. This paper outlines steps for QoS optimization using fuzzy logic, emphasizing the importance of QoS in network management. Fuzzy logic's role in addressing challenges is explored. It reviews relevant research, including applications in vehicular networks, software-defined networking, and more. The paper details a procedure for implementing fuzzy logic-based QoS optimization, estimation of network parameters, and mathematical equations for latency, throughput, and packet delivery ratio. It also discusses fuzzy logic-based control strategies for latency, throughput, and PDR enhancement.

The swift advancement of artificial intelligence (AI) technologies has brought about a profound shift in the realm of cybersecurity threat prediction. This comprehensive literature review delves into the advancements and challenges within the realm of AI-driven cybersecurity threat prediction. By comprehensively amalgamating various scholarly sources, this analysis elucidates the present situation and anticipates the future developments of artificial intelligence (AI) in the realm of predicting cybersecurity threats. The discussion delves into pivotal topics such as machine learning algorithms, deep learning techniques, natural language processing, and anomaly detection methodologies. Additionally, ethical considerations, data privacy issues, and the integration of AI with traditional cybersecurity practices are critically examined. The review concludes by highlighting emergent trends and suggesting avenues for future research, thereby providing valuable insights for both academics and practitioners in the field of cybersecurity.

This paper introduces cooperative continuous static games (CCSG) with parameters in the cost functions of the players and in the right-hand side of the constraints. The CCSG is converted into the corresponding multi-objective nonlinear programming problem. The resulted nonlinear programming problem is converted into the single objective nonlinear programming problem through the use of the weighted sum method. A solution method for obtaining the stability set of the second kind without differentiability for the CCSG is presented using Karush-Kuhn-Tucker conditions. A numerical example is given for the illustration.

In real-life scenarios, there are many mathematical tools to handle incomplete and imprecise data. One of them is the fuzzy approach. The main issue with addressing nonlinear interval programming (NIP) problems is that the optimal solution to the problem is a decision made under uncertainty that has a risk of not satisfying the feasibility and optimality criteria. Some strategies handle this kind of problem using classical terminology such as optimal solution and feasible solution. These strategies are insufficient for efficient analysis since the properties of the solution in an uncertain environment are ignored. Therefore, in the proposed approach, more suitable terminologies were suggested for the analysis process. In addition, it combines parametric treatment and interactive methodology. This article aims to contribute to the literature of fuzzy multi-objective dynamic programming (MODP) issues involving the fuzzy objective functions. The piecewise quadratic fuzzy numbers characterize these fuzzy parameters. Some basic notions in the problem under the α-pareto optimal solution concept is redefined and analyzed to study the stability of the problem. Furthermore, a technique, named the decomposition approach (DP), is presented for achieving a subset for the parametric space that contains the same α-pareto optimal solution. For a better understanding of the suggested concept, a numerical example is provided.

Depression is a common illness worldwide with doubtless severe implications. Due to the absence of early identification and treatment for depression, millions of individuals worldwide suffer from mental illnesses. It might be difficult to identify those who are experiencing mental health illnesses and to provide them with the early help that they need. Additionally, depression may be associated with thoughts of suicide. Currently, there are no clinically specific diagnostic biomarkers that can identify the severity and type of depression. In this research paper, the novel particle swarm-cuckoo search (PS-CS) optimization algorithm is proposed instead of the traditional backpropagation algorithm for training deep neural networks. The backpropagation algorithm is widely used for supervised learning in deep neural networks, but it has limitations in terms of convergence speed and the possibility of getting trapped in local optima. These problems were addressed by using a deep neural network architecture for depression detection tasks along with the PS-CS optimization technique. The PS-CS algorithm combines the strengths of both particle swarm optimization and cuckoo search algorithms, which allows for a more efficient and effective optimization of the network parameters. We also evaluated how well the suggested methods performed against the most widely used classification models, including (K-nearest neighbor) KNN, (support vector regression) SVR, and decision trees, as well as the most widely used deep learning models, including residual neural network (ResNet), visual geometry group (VGG), and simple neural network (LeNet). The findings show that the suggested method, PS-CS, in conjunction with the CNN model, outperformed all other models, achieving the maximum accuracy of 99.5%. Other models, such as the KNN, decision trees, and logistic regression, achieved lower accuracies ranging from 69% to 97%.

In the era of healthcare and its related research fields, the dimensionality problem of high-dimensional data is a massive challenge as it is crucial to identify significant genes while conducting research on diseases like cancer. As a result, studying new Machine Learning (ML) techniques for raw gene expression biomedical data is an important field of research. Disease detection, sample classification, and early disease prediction are all important analyses of high-dimensional biomedical data in the field of bioinformatics. Recently, machine-learning techniques have dramatically improved the analysis of high-dimension biomedical data sets. Nonetheless, researchers' studies on biomedical data faced the challenge of vast dimensions, i.e., the vast features (genes) with a very low sample space. In this paper, two-dimensionality reduction methods, feature selection, and feature extraction are introduced with a systematic comparison of several dimension reduction techniques for the analysis of high-dimensional gene expression biomedical data. We presented a systematic review of some of the most popular nature-inspired algorithms and analyzed them. The paper is mainly focused on the original principles behind each of the algorithms and their applications for cancer classification and prediction from gene expression data. Lastly, the advantages and disadvantages of nature-inspired algorithms for biomedical data are evaluated. This review paper may guide researchers to choose the most effective algorithm for cancer classification and prediction for the satisfactory analysis of high-dimensional biomedical data.

In this article, mathematical modeling for the evaluation of reliability is studied using two methods. One of the methods, is developed based on possibility theory. The performance of the reliability of the system is of prime concern. In view of this, the outcomes for the failure are required to evaluate with utmost care. In possibility theory, the reliability information data determined from decision-making experts are subjective. The samemethod is also related to the survival possibilities as against the survival probabilities. The other method is the one that is developed using the concept of approximation of closed interval including the piecewise quadratic fuzzy numbers. In this method, a decision-making expert is not sure of his/her estimates of the reliability parameters. Numerical experiments are performed to illustrate the efficiency of the suggested methods in this research. In the end, the paper is concluded with some future research directions to be explored for the proposed approach.

Recently, the Ethereum smart contracts have seen a surge in interest from the scientific community and new commercial uses. However, as online trade expands, other fraudulent practices-including phishing, bribery, and money laundering-emerge as significant challenges to trade security. This study is useful for reliably detecting fraudulent transactions; this work developed a deep learning model using a unique metaheuristic optimization strategy. The new optimization method to overcome the challenges, Optimized Genetic Algorithm-Cuckoo Search (GA-CS), is combined with deep learning. In this research, a Genetic Algorithm (GA) is used in the phase of exploration in the Cuckoo Search (CS) technique to address a deficiency in CS. A comprehensive experiment was conducted to appraise the efficiency and performance of the suggested strategies compared with those of various popular techniques, such as k-nearest neighbors (KNN), logistic regression (LR), multi-layer perceptron (MLP), XGBoost, light gradient boosting machine (LGBM), random forest (RF), and support vector classification (SVC), in terms of restricted features and we compared their performance and efficiency metrics to the suggested approach in detecting fraudulent behavior on Ethereum. The suggested technique and SVC models outperform the rest of the models, with the highest accuracy, while deep learning with the proposed optimization strategy outperforms the RF model, with slightly higher performance of 99.71% versus 98.33%.

Power quality issues are handled very well by filter technologies. In recent years, the advancement of hybrid active power filters (HAPF) has been enhanced due to ease of control and flexibility as compared to other filter technologies. These filters are a beneficial asset for a power producer that requires a smooth filtered output of power. However, the design of these filters is a daunting task to perform. Often, metaheuristic algorithms are employed for dealing with this nonlinear optimization problem. In this work, a new hybrid metaheuristic algorithm (Marine Predator Algorithm and Sine Cosine Algorithm) has been proposed for selecting the best parameters for HAPF. The comparison of different algorithms for obtaining the HAPF parameters is also performed to show case efficacy of the proposed hybrid algorithm. It can be concluded that the proposed algorithm produces robust results and can be a potential tool for estimating the HAPF parameters. The confirmation of the performance of the proposed algorithm is conducted with the results of fitness statistical results, boxplots, and different numerical analyses.

The rapid growth of social media sites in recent times has introduced a special environment for researching human actions. Micro blogging website (Twitter) which also allows users to access and communicate their opinions on various of topics, occurrences, brands, and service providers. Tweets have been classified into different subgroups relating to the subject of the research. Various machine learning algorithms, such as baseline, Naive Bayes classifier, support vector machine (SVM), and many others, are presently used this to categorize posts on Twitter into favourable and unfavourable groups based on their views and opinions. This paper describes a solution to improve comparison between Twitter accounts besides trying to implement Naive Bayes using perception vibrant data for training from of the Twitter database. SentiWordNet, in combined effect with Naïve Bayes classifier, can help increase tweet accuracy rate by providing hope and optimism, hatefulness, and integrity ratings for phrases in Twitter messages. Tweepy, which is a Python package and Python-Twitter APIs are used in the actual introduction of the new system. The main work of this paper is to perform real-time streaming, collect the data set while performing real time and make comparison between them using Naïve Bayes.

This article presents a fuzzy multi-objective linear fractional programming (FMOLFP) problem. The goal programming (GP) approach is used to solve the proposed problem. The $$LR$$ (Left and Right) possibilistic variables are addressed to the suggested the fuzzy multi-objective linear fractional programming (FMOLFP) model to deal the uncertainty of the model parameters. An auxiliary model in which objective function is the distance between the $$p -$$ ary $$\alpha -$$ optimal value restriction and $$p -$$ ary fuzzy objective function is proposed. In the last, one solved example is given to illustrate and to support the validity of the suggested approach.

Ethereum smart contracts have recently received new commercial applications and a lot of attention from the scientific community. Ethereum eliminates the requirement for a trusted third party by allowing untrusted parties to expose contract details in computer code. Nonetheless, as online commerce grows, plenty of fraudulent activities, such as money laundering, bribery, and phishing, emerge as major threats to trade security. For correctly recognizing fraudulent transactions, this paper developed a Light Gradient Boosting Machine (LGBM) technique-based model. The modified LGBM model optimized the parameters of Light GBM using the Euclidean distant structured estimation approach. This paper also examines the performance of different popular models such as Random Forest (RF), Multi-Layer Perceptron (MLP), Logistic Regression, k-Nearest Neighbors (KNN), XGBoost, Support Vector Classification (SVC), and ADAboost with limited features and compares their performance metrics with the proposed model for Ethereum fraudulent activity classification. A comparative performance evaluation matrices scores of different popular models along with the proposed model demonstrated the applicability of the proposed approach. The modified LGBM algorithms and RF models demonstrate the best performance compared to other models with the highest accuracies, while the modified LGBM algorithm has a slightly superior performance of 99.17 percent compared to the RF model's 98.26 percent.

The research problems on Object detection have been attracted with major issues in the computer vision domain. Object detection based on images from unmanned aerial vehicles (UAV) - drones, has versatile applications in both defence security, agriculture and GIS. However, real-time object detection in UAV scenarios remains quite a tedious problem due to environmental obstructions such as occlusion and view-invariant conditions despite the high number of solutions proposed to solve this task. This paper proposes an improved YOLOv3-tiny object detector by introducing a multi-dilated module between the convolution unit and the receptive field, where the problem of a small number of positive training samples is solved by a larger size of the predicted feature map thereby reducing the rate of label rewriting in YOLOv3-tiny. We find that the fusion of multi-scale receptive fields is effective in detecting even every single tiny object. We introduce a path aggregation module that merges the semantic information in a deeper layer and detailed information in an earlier layer. The analysis of the proposed solution shows that on the VisDrone2019-Det test set, our proposed model is more efficient and effective, running 2.96% times faster and increasing 4.0% AP50 than YOLOv3.

In financial planning problems, the determination of the best investment is one of the interesting optimization models. In the proposed work, an investment problem (IP) is introduced in vague environment. The vagueness in return parameter is characterized by normalized heptagonal fuzzy number (HFN). One of the suitable interval approximations, namely, an inexact rough interval of a normalized HFN is utilized. Afterward, the inexact rough interval investment problem is considered. A dynamic programming (DP) approach is developed, which is applied for optimizing the fuzzy investment problem. The ideology of “rough interval number” is suggested in the mathematical modeling framework of the proposed problem to show the rough data as an inexact rough interval of piecewise quadratic fuzzy numbers. Afterward, the DP approach is applied to solve and compute a rough interval solution. Finally, a numerical example is yielded for the utility of the approach to apply on real-world problem for the decision-maker. The obtained results consist of the total optimal return with inexact rough intervals on a $ 10 million investments is as follows: $ [[1.69, 2.08]: [1.75, 1.91]] millions.

In this paper, a mathematical model for an extended multi-objective portfolio selection (EMOPS) problem is explored with liquidity considered as another objective function besides the risk and return. The model is mathematically formulated in an uncertain environment. The concerned uncertainty is dealt with by employing the fuzzy numbers in the risk matrix and return. While the fuzzy EMOPS model is converted into the corresponding deterministic case based on the α—level sets of the fuzzy numbers, a weighted Tchebycheff method is implemented by defining relative weights and ideal targets. The merit of the suggested method is the applicability in many real-world situations. At the end, some numerical illustration is exhibited for the utility of the suggested EMOPS problem. Finally, it is concluded that the suggested method is simple to learn and to implement in real-life situations for the decision maker.

The crime rate in India is considerably increasing day by day. Consequently, the data associated with crime is also increasing, opening doors for data-driven approaches to these data to extract insightful knowledge, which can help police and other law enforcement organizations of the country in crime control and prevention. Crime prediction using machine learning algorithms on crime data can predict region-wise crime counts. In this paper, a machine learning-based soft computing regression analysis approach for Indian Crime Data Analysis (ICDA) is proposed. Different regression algorithms, namely, Simple Linear Regression (SLR), Multiple Linear Regression (MLR), Decision Tree Regression (DTR), Support Vector Regression (SVR), and Random Forest Regression (RFR) are uses to build regression models. These regression models can predict a total number of Indian Penal Code (IPC) crime counts and crime counts of different types of crime (murder, rape, kidnapping and abduction, riots, to name a few) region-wise and state-wise and all over the country for a given year. Adjusted R squared value and Mean Absolute Percentage Error (MAPE) is used to evaluate and compare proposed regression models. In the proposed approach for ICDA, district-wise spatial-temporal crime data of years 2001 to 2012 is used, collected from the official website of NCRB. For the chosen data, it is concluded that the region-wise total IPC crime prediction RFR model fits the best with an adjusted R squared value of 0.9631551 and an error of 0.2027437. Whereas for region-wise thefts crime count prediction, the RFR model fits the best with an adjusted R squared value of 0.966604 and an error of 0.16571.

This research article aims to study a multi-objective linear fractional programming (FMOLFP) problem having fuzzy random coefficients as well as fuzzy pseudorandom decision variables. Initially, the FMOLFP model is converted to a single objective fuzzy linear programming (FLP) model. Secondly, we show that a fuzzy random optimal solution of an FLP problem is resolved into a class of random optimal solution of relative pseudorandom linear programming (LP) model. As a result, some of theorems show that a fuzzy random optimal solution of a fuzzy pseudorandom LP problem is combined with a series of random optimal solutions of relative pseudorandom LP problems. As an application, the developed approach is implemented to an inventory management problem by taking the parameters as trapezoidal fuzzy numbers, ultimately resulting in a new initiative for modelling real-world problems for optimization. In the last, some numerical examples are introduced to clarify the obtained results and their applicability.