Recent Advances in Computer Science and Communications - Volume 17, Issue 1, 2024

The special issue contains research papers elaborating advancements in computational intelligence. Computational intelligence mimics the extraordinary capacity of the human intellect to assert and understand in an environment of uncertainty and imprecision. Computational intelligence is new-age multidisciplinary artificial intelligence. The main goal of computational intelligence is to develop intelligent systems to solve real-world problems that are not modelled or too hard to model mathematically.

There has been an exponential increase in discussions about bias in Artificial Intelligence (AI) systems. Bias in AI has typically been defined as a divergence from standard statistical patterns in the output of an AI model, which could be due to a biased dataset or biased assumptions. While the bias in artificially taught models is attributed able to bias in the dataset provided by humans, there is still room for advancement in terms of bias mitigation in AI models. The failure to detect bias in datasets or models stems from the "black box" problem or a lack of understanding of algorithmic outcomes. This paper provides a comprehensive review of the analysis of the approaches provided by researchers and scholars to mitigate AI bias and investigate the several methods of employing a responsible AI model for decision-making processes. We clarify what bias means to different people, as well as provide the actual definition of bias in AI systems. In addition, the paper discussed the causes of bias in AI systems thereby permitting researchers to focus their efforts on minimising the causes and mitigating bias. Finally, we recommend the best direction for future research to ensure the discovery of the most accurate method for reducing bias in algorithms. We hope that this study will help researchers to think from different perspectives while developing unbiased systems.

Chatbots use artificial intelligence (AI) and natural language processing (NLP) algorithms to construct a clever system. By copying human connections in the most helpful way possible, chatbots emulate individuals and serve as virtual assistants. They easily interface and respond to customers' requests. In the modern technical environment, these conversation agents or chatbots are considered the next-generation invention. Chatbot has become more popular in the business field right now as it can reduce customer service cost and handle multiple users at a time. There are many techniques used to involve such intelligent experts in daily business. A comprehensive analysis of the methods is needed to determine the viability of the different strategies. This paper tracks the progress of this invention and further clarifies the influence of chatbots on numerous businesses. Besides, a survey of the multiple chatbot methodologies suggested by various researchers is provided. Along with the survey, a chatbot e-commerce customer service is designed to provide an efficient and accurate answer for any query based on the dataset of frequently asked questions. This chatbot can reduce customer service costs and can handle multiple customers at the same time.

Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media networks. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a marginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Naïve Bayes, Multinomial Naïve Bayes, Multinomial Naïve Bayes with Bagging, Logistic Regression and a similar enhancement is observed with Ada- Boost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.

Aim: Load forecasting for efficient power system management. Background: Short-term energy load forecasting (STELF) is a valuable tool for utility companies and energy providers because it allows them to predict and plan for changes in energy. Method: 1D CNN BI-LSTM model incorporating convolutional layers. Result: The results provide the Root Mean Square Error of 0.952. The results shows that the proposed model outperforms the existing CNN based model with improved accuracy, hourly prediction, load forecasting. Conclusion: The proposed model has several applications, including optimal energy allocation and demand-side management, which are essential for smart grid operation and control. The model’s ability to accurately management forecast electricity load will enable power utilities to optimize their generation.

Introduction: Crack is the main bridge disease. The monitoring of the crack width is the key for determining whether the bridge needs to be maintained. The systematic and automatic detection of bridge cracks can be realized using the crack images, which are captured using unmanned aerial vehicles (UAV). Methods: Cracks in the image with a complex background and low contrast ratio are difficult to detect. In order to detect the tiny cracks, the image is preprocessed by homomorphic filtering to enhance the contrast ratio. It is a necessary step that makes the color clustering be used in the detection. An adaptive color clustering method is proposed to detect cracks without additional initialization. Morphological method is also used to obtain clean edges and skeletons. Results: The proposed method can accurately detect the crack areas with an actual width greater than 0.13 mm, and the absolute error is only 0.0013 mm. The relative error for all test images are smaller than 15.6%. Cracks over 0.2 mm need to be filled. Therefore, this error is completely acceptable in practice. Discussion: The proposed method is practical and reproducible for bridge disease automatic inspection based on UAV. In order to verify its advantage, the proposed method is compared with a state-of-the-art method, which is published on Sensors. The proposed method is proven to be better for images with water stains in its complex background. Conclusion: The proposed method can calculate the width of tiny cracks accurately, even if the width is below 0.2 mm.

Background: Data quality is crucial to the success of big data analytics. However, the presence of outliers affects data quality and data analysis. Employing effective outlier detection techniques to eliminate dirty data can improve data quality and garner more accurate analytical insights. Data uncertainty presents a significant challenge for outlier detection methods and warrants further refinement in the era of big data. Objective: The unsupervised outlier detection based on the integration of clustering and outlier scoring scheme is the current research hotspot. However, hard clustering fails when dealing with abnormal patterns with uncertain and unexpected behavior. Rough boundaries help identify more accurate cluster structures. Therefore, this article uses uncertainty soft clustering based on rough set theory to extend the clustering technology and designs appropriate scoring schemes to capture abnormal instances. This solves the problem of outlier detection in uncertain and nonlinear complex data. Methods: This paper proposes the flow of an outlier detection algorithm based on Kernel Rough Clustering and then compares the detection accuracy with five existing popular methods using synthetic and real-world datasets. The results show that the proposed method has higher detection accuracy. Results: The detection precision and recall of the proposed method were improved. For the detection accuracy, it is superior to popular methods, indicating that the proposed method has a good detection effect in identifying outlier. Conclusion: Compared with popular methods, the proposed method has a slight advantage in detection accuracy and is one of the effective algorithms that can be selected for outlier detection.

Background: The healthcare sector faces numerous issues, such as insurance fraud, electronic medical record management, interoperability, insecure dissemination of information, etc. The novel Blockchain technology holds tremendous potential to transform the healthcare sector by addressing these rising challenges in the industry. It provides a secure platform for storing, disseminating, and retrieving sensitive patient data and health records while preserving the ethical principles of the healthcare sector. Method: In this study, we systematically reviewed the literature on blockchain technology in healthcare using PRISMA and highlighted how blockchain technology might promote innovation and deliver major improvements in the healthcare sector. Result: Our goal is to examine the present status of this discipline, focusing on limits and potential advances. Queries were used to gather Scopus, PubMed, SpringerLink, IEEE Xplore, and Web of Science publications that met the criteria for the selection of papers. Conclusion: This article, thus, analyses the potential for blockchain in the healthcare industry and outlines blockchain-based products in healthcare. Our study enhances and complements prior healthcare blockchain research.

One of the most promising fields of research in recent years is Mobile Ad Hoc Networks (MANET). The well-known advantages of the internet for specific types of applications lead to the fact that it is a wireless ad-hoc network. As a result, such networks can be utilized in circumstances where no other wireless communication infrastructure is present. A MANET is a network of wireless devices without any centralized control. A device can directly communicate with other devices using a wireless connection. For nodes that are located far from other nodes, multi-hop routing is employed. The functionality of route-finding is performed by routing protocols. The mobility model creates the movement pattern for nodes. This article discusses early research to address concerns about performance indicators for MANET routing protocols under the Column Mobility Model (CMM). Moreover, we discuss concerns regarding the designs of the related work, followed by the designed CMM model on the behavior of routing protocols.