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- Abdur Rasool was born in Rahim Yar Khan, Punjab, Pakistan, in 1993. He received a BS (Hons.) degree in software engin... moreAbdur Rasool was born in Rahim Yar Khan, Punjab, Pakistan, in 1993. He received a BS (Hons.) degree in software engineering from Government College University, Faisalabad, Pakistan, in 2015 and a master's degree in Computer Science and Technology from Donghua University, Shanghai, China, in 2020, and a Ph.D. degree at Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS), China. He has authored over 18 publications in international journals and academic conferences. He is presently an IEEE member. He has received several awards, including the Shenzhen Universiade International Scholarship Foundation (SUISF) in 2022, and won first prize in the IEEE R10 Research paper contest in 2021. He received the 2022 Excellent International Student award from the University of Chinese Academy of Sciences, Beijing, China. His interests are in multidisciplinary computer science and technology, mainly in DNA Data storage, Deep Learning, Data Mining, Machine Learning, and Sentiment Analysis.edit
The exponential increase of big data volumes demands a large capacity and high-density storage. Deoxyribonucleic acid (DNA) has recently emerged as a new research trend for data storage in various studies due to its high capacity and... more
The exponential increase of big data volumes demands a large capacity and high-density storage. Deoxyribonucleic acid (DNA) has recently emerged as a new research trend for data storage in various studies due to its high capacity and durability, where primers and address sequences played a vital role. However, it is a critical biocomputing task to design DNA strands without errors. In the DNA synthesis and sequencing process, each nucleotide is repeated, which is prone to errors during the hybridization reactions. It decreases the lower bounds of DNA coding sets which causes the data storage stability. This study proposes a metaheuristic algorithm to improve the lower bounds of DNA data storage. The proposed algorithm is inspired by a moth-flame optimizer (MFO), which has exploration and exploitation capability in one dimension, and it is enhanced by opposition-based learning (OBL) strategy with three-dimension search space for the optimal solution; hereafter, it is MFOL algorithm. This algorithm is programmed to construct the DNA storage codes by reducing the error rates of DNA coding sets with GC-content, Hamming distance, and No-runlength constraints. In experiments, 13 benchmark functions and Wilcoxon rank-sum test are implemented, and performances are compared with the original MFO and three other algorithms. The generated DNA codewords by MFOL are compared with a state-of-the-art Altruistic algorithm and KMVO algorithm. The proposed algorithm improved 30% DNA coding rates with shorter sequences, reducing errors during DNA synthesis and sequencing.
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Artificial Intelligence (AI) is a research-focused technology in which Natural Language Processing (NLP) is a core technology in AI. Sentiment Analysis (SA) aims to extract and classify the people's opinions by NLP. The Machine Learning... more
Artificial Intelligence (AI) is a research-focused technology in which Natural Language Processing (NLP) is a core technology in AI. Sentiment Analysis (SA) aims to extract and classify the people's opinions by NLP. The Machine Learning (ML) and lexicon dictionaries have limited competency to efficiently analyze massive live media data. Recently, deep learning methods significantly enrich the accuracy of recent sentiment models. However, the existing methods provide the aspect-based extraction that reduces individual word accuracy if a sentence does not follow the aspect information in real-time. Therefore, this paper proposes a novel word embedding method for the real-time sentiment (WRS) for word representation. The WRS's novelty is a novel word embedding method, namely, Word-to-Word Graph (W2WG) embedding that utilizes the Word2Vec approach. The WRS method assembles the different lexicon resources to employ the W2WG embedding method to achieve the word feature vector. Robust neural networks leverage these features by integrating LSTM and CNN to improve sentiment classification performance. LSTM is utilized to store the word sequence information for the effective real-time SA, and CNN is applied to extract the leading text features for sentiment classification. The experiments are conducted on Twitter and IMDB datasets. The results demonstrate our proposed method's effectiveness for real-time sentiment classification.
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Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources. Several different techniques, i.e., simple rule-based to lexicon-based... more
Sentiment analysis or opinion mining is the key to natural language processing for the extraction of useful information from the text documents of numerous sources. Several different techniques, i.e., simple rule-based to lexicon-based and more sophisticated machine learning algorithms, have been widely used with different classifiers to get the factual analysis of sentiment. However, lexicon-based sentiment classification is still suffering from low accuracies, mainly due to the deficiency of domain-oriented competitive dictionaries. Similarly, machine learning-based sentiment is also tackling the accuracy constraints because of feature ambiguity from social data. One of the best ways to deal with the accuracy issue is to select the best feature-set and reduce the volume of the feature. This paper proposes a method (namely, GAWA) for feature selection by utilizing the Wrapper Approaches (WA) to select the premier features and the Genetic Algorithm (GA) to reduce the size of the premier features. The novelty of this work is the modified fitness function of heuristic GA to compute the optimal features by reducing the redundancy for better accuracy. This work aims to present a comprehensive model of hybrid sentiment by using the proposed method, GAWA. It will be valued in developing a new approach for the selection of feature-set with a better accuracy level. The experiments revealed that these techniques could reduce the feature-set up-to 61.95% without negotiating the accuracy level. The new optimal feature sets enhanced the efficiency of the Naïve Bayes algorithm up to 92%. This work is compared with the conventional method of feature selection and concluded the 11% better accuracy than PCA and 8% better than PSO. Furthermore, the results are compared with the literature work and found that the proposed method outperformed the previous research.
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This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY
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Sudden and unlooked events can be adverse to the world financial community, especially stock exchange markets. Events like the 9/11 terrorist attacks can be destructive for global and local markets. Investors need to assess the sudden... more
Sudden and unlooked events can be adverse to the world financial community, especially stock exchange markets. Events like the 9/11 terrorist attacks can be destructive for global and local markets. Investors need to assess the sudden events' effects on a minute-by-minute basis to evaluate market reactions precisely for a better investment strategy. Despite the web crawler's availability, it is challenging to mine biased data; however, we build an artificial intelligence (AI) based model to make a better investment strategy that uses unexpected incidents like terrorist attacks and other natural disasters like floods and earthquakes. For this, we used Pakistan's real-time terrorist attacks, floods, and earthquakes data and Pakistan stock market KSE100 index data to feed the model which predicts swift market changes. We study unexpected real-world incident data and stock market data from 2001 to 2020. First of all, a web crawler crawls the data from different websites using AI techniques. Then this data is fed into the information extractor module, which extracts the meaningful information based on AI. This unexpected incident data and stock market data are then used to train the AI-based LSTM to predict the market direction. Results prove that our model outperforms in measuring the influence of these shocking incidents and consequently detecting abrupt alterations in stock market directions.
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Relation extraction is an important NLP task to extract the semantic relationship between two entities. Recently, large-scale pre-training language models have achieved excellent performance in many NLP applications. Most of the existing... more
Relation extraction is an important NLP task to extract the semantic relationship between two entities. Recently, large-scale pre-training language models have achieved excellent performance in many NLP applications. Most of the existing relation extraction models mainly rely on context information, but entity information is also very important for relation extraction, especially domain knowledge of entity and the direction between entity pairs. In this paper, based on the pre-trained BERT model, we propose a multi-task joint relation extraction model incorporating knowledge representation learning(KRL). The experimental results on the SemEval 2010 task 8 dataset and the KBP37 dataset show that our proposed model outperforms most of state-of-the-art methods. The results on the larger dataset FewRel80 refined from FewRel also indicate that increasing the knowledge representation learning as an auxiliary objective is helpful for the relation extraction task.
In recent times, multi-phase induction motor drives have attracted attention where high overall device performance and a decrease in total power per phase are desired. The extra phases in conjunction with normal 3phase drive ensure that... more
In recent times, multi-phase induction motor drives have attracted attention where high overall device performance and a decrease in total power per phase are desired. The extra phases in conjunction with normal 3phase drive ensure that the mechanism continues to function without torque ripple, disturbance and vibration even under unstable circumstances. To overcome this problem, a suitable terminal sliding mode control system may achieve a ripple-less high torque in a five-phase induction motor. The current control loop offers significant benefits to enhance the stability of the drive system. With outstanding transient responses, this device can conduct strong dynamic current management. The Terminal Sliding Mode Control based on MPCC is used to optimize the motor control system. Whereas, HOSM is introduced to eliminate the chattering phenomenon.
Sudden and unlooked events can be adverse to the world financial community, especially stock exchange markets. Events like the 9/11 terrorist attacks can be destructive for global and local markets. Investors need to assess the sudden... more
Sudden and unlooked events can be adverse to the world financial community, especially stock exchange markets. Events like the 9/11 terrorist attacks can be destructive for global and local markets. Investors need to assess the sudden events' effects on a minute-by-minute basis to evaluate market reactions precisely for a better investment strategy. Despite the web crawler's availability, it is challenging to mine biased data; however, we build an artificial intelligence (AI) based model to make a better investment strategy that uses unexpected incidents like terrorist attacks and other natural disasters like floods and earthquakes. For this, we used Pakistan's real-time terrorist attacks, floods, and earthquakes data and Pakistan stock market KSE100 index data to feed the model which predicts swift market changes. We study unexpected real-world incident data and stock market data from 2001 to 2020. First of all, a web crawler crawls the data from different websites using AI techniques. Then this data is fed into the information extractor module, which extracts the meaningful information based on AI. This unexpected incident data and stock market data are then used to train the AI-based LSTM to predict the market direction. Results prove that our model outperforms in measuring the influence of these shocking incidents and consequently detecting abrupt alterations in stock market directions.
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY