Parameter Tuned Machine Learning Based Emotion Recognition on Arabic Twitter Data

Arabic is one of the most spoken languages across the globe. However, there are fewer studies concerning Sentiment Analysis (SA) in Arabic. In recent years, the detected sentiments and emotions expressed in tweets have received significant interest. The substantial role played by the Arab region in international politics and the global economy has urged the need to examine the sentiments and emotions in the Arabic language. Two common models are available: Machine Learning and lexicon-based approaches to address emotion classification problems. With this motivation, the current research article develops a Teaching and Learning Optimization with Machine Learning Based Emotion Recognition and Classification (TLBOML-ERC) model for Sentiment Analysis on tweets made in the Arabic language. The presented TLBOML-ERC model focuses on recognising emotions and sentiments expressed in Arabic tweets. To attain this, the proposed TLBOML-ERC model initially carries out data pre-processing and a Continuous Bag Of Words (CBOW)-based word embedding process. In addition, Denoising Autoencoder (DAE) model is also exploited to categorise different emotions expressed in Arabic tweets. To improve the efficacy of the DAE model, the Teaching and Learning-based Optimization (TLBO) algorithm is utilized to optimize the parameters. The proposed TLBOML-ERC method was experimentally validated with the help of an Arabic tweets dataset. The obtained results show the promising performance of the proposed TLBOML-ERC model on Arabic emotion classification.     

Modified Garden Balsan Optimization Based Machine Learning for Intrusion Detection

The Internet of Things (IoT) environment plays a crucial role in the design of smart environments. Security and privacy are the major challenging problems that exist in the design of IoT-enabled real-time environments. Security susceptibilities in IoT-based systems pose security threats which affect smart environment applications. Intrusion detection systems (IDS) can be used for IoT environments to mitigate IoT-related security attacks which use few security vulnerabilities. This paper introduces a modified garden balsan optimization-based machine learning model for intrusion detection (MGBO-MLID) in the IoT cloud environment. The presented MGBO-MLID technique focuses on the identification and classification of intrusions in the IoT cloud atmosphere. Initially, the presented MGBO-MLID model applies min-max normalization that can be utilized for scaling the features in a uniform format. In addition, the MGBO-MLID model exploits the MGBO algorithm to choose the optimal subset of features. Moreover, the attention-based bidirectional long short-term (ABiLSTM) method can be utilized for the detection and classification of intrusions. At the final level, the Aquila optimization (AO) algorithm is applied as a hyperparameter optimizer to fine-tune the ABiLSTM methods. The experimental validation of the MGBO-MLID method is tested using a benchmark dataset. The extensive comparative study reported the betterment of the MGBO-MLID algorithm over recent approaches.     

Modelling worker fatigue and recovery in dual-resource constrained systems

The operational benefits that dual-resource constrained (DRC) job shop systems bring have captured the attention of researchers for some time. Although several studies that investigate DRCs are available in the literature, none has investigated a DRC system for the effects of human fatigue and recovery, which poses important parameters to avoiding overload and injury to employees. The purpose of this paper is to address this limitation by presenting a mixed-integer linear programming (MILP) model that describes fatigue and recovery in a DRC system with one worker performing n tasks (flexibility level) within m cycles. Later, the complexity of the MILP problem was reduced to four practical cases. These cases were investigated to evaluate several research questions. The results obtained from the MILP model and the four practical cases suggest that short rest breaks after each task, short cycle times and faster recovery rates improve the system’s performance and that reduced force levels in the work tasks will reduce recovery needs and further increase performance. Further research is still needed to identify or to develop better models of physiological and mental fatigue that can be integrated to the modelling framework presented here.     

A joint model for cash and inventory management for a retailer under delay in payments

As retail companies continue to navigate through the economy downturn, it becomes critical to find innovative cost reduction methods. Cash management is a cost-intensive process for retailers, who are currently focusing on effective cash management, such as deciding on the maximum cash level to keep in their business accounts and how much to borrow to finance inventories and pay suppliers. In this paper, we consider the problem of finding the optimal operational (how much to order and when to pay the supplier) and financial decisions (maximum cash level and loan amount) by integrating the cash management and inventory lot sizing problems. We consider a supplier offering a retailer an interest-free credit period for settling the payment. Beyond this period, the supplier charges interest on the outstanding balance. Whenever the cash exceeds a certain limit, it will be invested in purchasing financial securities. At the time when the retailer pays the supplier for the received order, cash is withdrawn from the account, incuring various financial costs. If the cash level becomes zero or not sufficient, the retailer obtains an asset-based loan at interest. We model this problem as a nonlinear program and propose a solution procedure for finding the optimal solution. We perform a numerical study to analyze the impact of optimal cash management on the inventory decisions. The results indicate that the optimal order quantity decreases as the retailer’s return on cash increases. We compare our model to a model that ignores financial considerations of cash management, and show numerically that our model lowers the retailer’s cost. Also, we illustrate the effect of changing various model parameters on the optimal solution and obtain managerial insights.     

The effect of worker learning and forgetting on storage reassignment decisions in order picking systems

Order picking is a time-intensive and costly logistics activity as it involves a high amount of manual work. Prior research has mostly neglected the influence of human factors on the efficiency of order picking systems. This paper develops a mathematical model that investigates the impact of learning and forgetting of a heterogeneous workforce on order picking time and, consequently, on storage assignment decisions. In particular, the paper investigates when to change a storage assignment and when to keep it if learning and forgetting occur among the members of an order picking workforce. The results show that learning and forgetting should be considered in order to achieve a proper planning of storage assignment strategies.     

Numerical solution of an integral equations system of the first kind by using an operational matrix with block pulse functions

This article proposes a simple efficient method for solving a Volterra integral equations system of the first kind. By using block pulse functions and their operational matrix of integration, a first kind integral equations system can be reduced to a linear system of algebraic equations. The coefficient matrix of this system is a block matrix with lower triangular blocks. Numerical examples show that the approximate solutions have a good degree of accuracy.     

IoT-Cloud Assisted Botnet Detection Using Rat Swarm Optimizer withDeepLearning

Nowadays, Internet of Things (IoT) has penetrated all facets of human life while on the other hand, IoT devices are heavily prone to cyberattacks. It has become important to develop an accurate system that can detect malicious attacks on IoT environments in order to mitigate security risks. Botnet is one of the dreadful malicious entities that has affected many users for the past few decades. It is challenging to recognize Botnet since it has excellent carrying and hidden capacities. Various approaches have been employed to identify the source of Botnet at earlier stages. Machine Learning (ML) and Deep Learning (DL) techniques are developed based on heavy influence from Botnet detection methodology. In spite of this, it is still a challenging task to detect Botnet at early stages due to low number of features accessible from Botnet dataset. The current study devises IoT with Cloud Assisted Botnet Detection and Classification utilizing Rat Swarm Optimizer with Deep Learning (BDC-RSODL) model. The presented BDC-RSODL model includes a series of processes like pre-processing, feature subset selection, classification, and parameter tuning. Initially, the network data is pre-processed to make it compatible for further processing. Besides, RSO algorithm is exploited for effective selection of subset of features. Additionally, Long Short Term Memory (LSTM) algorithm is utilized for both identification and classification of botnets. Finally, Sine Cosine Algorithm (SCA) is executed for fine-tuning the hyperparameters related to LSTM model. In order to validate the promising performance of BDC-RSODL system, a comprehensive comparison analysis was conducted. The obtained results confirmed the supremacy of BDC-RSODL model over recent approaches.     

Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition

The recognition of the Arabic characters is a crucial task in computer vision and Natural Language Processing fields. Some major complications in recognizing handwritten texts include distortion and pattern variabilities. So, the feature extraction process is a significant task in NLP models. If the features are automatically selected, it might result in the unavailability of adequate data for accurately forecasting the character classes. But, many features usually create difficulties due to high dimensionality issues. Against this background, the current study develops a Sailfish Optimizer with Deep Transfer Learning-Enabled Arabic Handwriting Character Recognition (SFODTL-AHCR) model. The projected SFODTL-AHCR model primarily focuses on identifying the handwritten Arabic characters in the input image. The proposed SFODTL-AHCR model pre-processes the input image by following the Histogram Equalization approach to attain this objective. The Inception with ResNet-v2 model examines the pre-processed image to produce the feature vectors. The Deep Wavelet Neural Network (DWNN) model is utilized to recognize the handwritten Arabic characters. At last, the SFO algorithm is utilized for fine-tuning the parameters involved in the DWNN model to attain better performance. The performance of the proposed SFODTL-AHCR model was validated using a series of images. Extensive comparative analyses were conducted. The proposed method achieved a maximum accuracy of 99.73%. The outcomes inferred the supremacy of the proposed SFODTL-AHCR model over other approaches.     

Using a real coded PSO algorithm in the design of a multi-component countercurrent cascade

Parameter optimization of a multi-objective cascade in the multi-component mixture like maximizing of separation work relative to the number of centrifuges has too economical advantages in the centrifuge-based separation. In this paper, a cascade simulator program (CASIM) is developed to design an optimum cascade. Through CASIM, a realistic function for separation factors, α_i, in relation to θ, cut, and feed flow rate is achieved. A real coded particle swarm optimization (RCPSO) program is implemented and developed in the CASIM to design an optimum cascade. It has been shown that the application of RCPSO to this problem guarantees finding the optimum solution. It is found that all the objective functions are achieved.     

Polymerization of olefins through heterogeneous catalysis. XII. The influence of hydrogen in the solution copolymerization of ethylene

The copolymerization of ethylene with highly active TiCl₄/MgCl₂-supported catalysts in solution reactors at 185°C and 400 Psig pressure is presented. The performance of these supported catalysts at these conditions is characterized by a high initial rate that decays rapidly within the 10 min polymerization period. In the presence of hydrogen and a comonomer, catalyst yields up to about 300 kg/g (Ti) are achieved. The kinetic data indicate rate enhancement when hydrogen is added in moderate concentrations. However, a high concentration of hydrogen results in a decreasing rate of ethylene consumption. Increasing the H₂/C₂ molar ratio in the range 0–10.66 ? 10^?3 leads to a reduction in the M_n values from 31,600 to 17,400. © 1993 John Wiley & Sons, Inc.