67 research outputs found
گورنمنٹ کالج یونیورسٹی لاہور کے دو اہل قلم فکشن نگار: اکرام اللہ، محمد عاصم بٹ
No full textIkramullah is a well-known Urdu fiction writer known for his unique style. One of his characteristics is his deep observation of daily life and its small details, which examples are rarely found in Urdu fiction. His writing feels alive and full of energy, whether he's depicting someone's destiny, exploring their personality, or telling stories of love. Be they urban scenarios or escalating violence, images of despair or instances of magical realism, or plain realism. He never hesitates to have the last word in his narrative, especially at crucial moments where others act reluctantly, he walks with great confidence and courage. Overall, Ikramullah's work leaves a strong impression on readers with its vivid portrayal of life's intricacies. After Ikramullah, we will talk about Muhammad Asim Butt. Asim Butt is considered among the writers of modern thought. In his fiction, he has tried to see the existential problems of modern man in the background of urban life. Contemporary consciousness is evident in his art and his stories are a reflection of modern human problems. Most of his characters are forced to live in a fantasy world away from reality where they satisfy their unsatisfied desires. We can say that Asim Buttamp;#39;s stories deal with the problems faced by individuals in the modern era such as the helplessness of the individual, loneliness, his retreat at the hands of destiny, economic and social oppression, monotony of life, meaninglessness, fear, illusion and indolence. We can say that the fictions of Asim Butt have an existential impression on which the influence of the German writer Franz Kafka can be clearly seen
An Efficient Automated Machine Learning Framework for Genomics and Proteomics Sequence Analysis
No full textGenomics and Proteomics sequence analyses are the scientific studies of understanding the language of Deoxyribonucleic Acid (DNA), Ribonucleic Acid (RNA) and protein biomolecules with an objective of controlling the production of proteins and understanding their core functionalities. It helps to detect chronic diseases in early stages, root causes of clinical changes, key genetic targets for pharmaceutical development and optimization of therapeutics for various age groups. Most Genomics and Proteomics sequence analysis work is performed using typical wet lab experimental approaches that make use of different genetic diagnostic technologies. However, these approaches are costly, time consuming, skill and labor intensive. Hence, these approaches slow down the process of developing an efficient and economical sequence analysis landscape essential to demystify a variety of cellular processes and functioning of biomolecules in living organisms. To empower manual wet lab experiment driven research, many machine learning based approaches have been developed in recent years. However, these approaches cannot be used in practical environment due to their limited performance. Considering the sensitive and inherently demanding nature of Genomics and Proteomics sequence
analysis which can have very far-reaching as well as serious repercussions on account of misdiagnosis, the main
objective of this research is to develop an efficient automated computational framework for Genomics and Proteomics sequence analysis using the predictive and prescriptive analytical powers of Artificial Intelligence (AI) to significantly improve healthcare operations.
The proposed framework is comprised of 3 main components namely sequence encoding, feature engineering and
discrete or continuous value predictor. The sequence encoding module is equipped with a variety of existing and newly developed sequence encoding algorithms that are capable of generating a rich statistical representation of DNA, RNA and protein raw sequences. The feature engineering module has diverse types of feature selection and dimensionality reduction approaches which can be used to generate the most effective feature space. Furthermore, the discrete and/or continuous value predictor module of the proposed framework contains a wide range of existing machine learning and newly developed deep learning regressors and classifiers. To evaluate the integrity and generalizability of the proposed framework, we have performed a large-scale experimentation over diverse types of Genomics and Proteomics sequence analysis tasks (i.e., DNA, RNA and proteins).
In Genomics analysis, Epigenetic modification detection is one of the key component. It helps clinical researchers and practitioners to distinguish normal cellular activities from malfunctioned ones, which can lead to diverse genetic disorders such as metabolic disorders, cancers, etc. To support this analysis, the proposed framework is used to solve the problem of DNA and Histone modification prediction where it has achieved state-of-the-art performance on 27 publicly available benchmark datasets of 17 different species with best accuracy of 97%. RNA sequence analysis is another vital component of Genomics sequence analysis where the identification of different coding and non-coding RNAs as well as their subcellular localization patterns help to demystify the functions of diverse RNAs, root causes of clinical changes, develop precision medicine and optimize therapeutics. To support this analysis, the proposed framework is utilized for non-coding RNA classification and multi-compartment RNA subcellular localization prediction. Where it achieved state-of-the-art performance on 10 publicly available benchmark datasets of Homo sapiens and Mus Musculus species with best accuracy of 98%.
Proteomics sequence analysis is essential to demystify the virus pathogenesis, host immunity responses, the way
proteins affect or are affected by the cell processes, their structure and core functionalities. To support this analysis, the proposed framework is used for host protein-protein and virus-host protein-protein interaction prediction. It has achieved state-of-the-art performance on 2 publicly available protein protein interaction datasets of Homo Sapiens and Mus Musculus species with best accuracy of 96% and 7 viral host protein protein interaction datasets of multiple hosts and viruses with best accuracy of 94%. Considering the performance and practical significance of proposed framework, we believe proposed framework will help researchers in developing cutting-edge practical applications for diverse Genomic and Proteomic sequence analyses tasks (i.e., DNA, RNA and proteins)
Interdiscursivity in Pakistani Drama Serials: A Study of Critical Discourse Analysis
No full textThe purpose of this study is to analyse the interdiscursivity of Pakistani dramas by applying critical discourse analysis to them. This article explores the nature, function, and use of interdiscursivity in four different Pakistani dramas. Baandi by Asma Nabeel, Cheekh by Zanjabeel Asim Shah, Baghi by Shazia Khan, Ab daikh Khuda Kia Karta hai by Syed Amer Ali Shah Husaini. This paper also deals with the strategies of language and how language is being practised by different characters in different dramas to show their cultural background through the lens of media. Fairclaugh's dimensional model has been used to analyse the data. The study also takes into account how interdiscursivity explores the hidden agenda behind these ideologies. This study aims to dig out scenes and characters from these four dramas. Discursive practises have been shown through the perspective of violence and hegemony created by the continuous practise of patriarchy in our society.
 
ReqNet: an LLM-driven computational framework for automated requirements extraction from unstructured documents
No full textAbstract Within software development life-cycle, requirements guide the entire development process from inception to completion by ensuring alignment between stakeholder expectations and the final product. Requirements extraction from miscellaneous information is a challenging and complex task. Manual extraction of requirements is not only prone to human error but also contributes to increased project costs and delayed project timelines. To automate the requirement extraction process, researchers have investigated the potential of deep learning architectures, large language models (LLM) and generative language models such as ChatGPT and Gemini. However, the performance of requirements extraction could be further enhanced through the development of predictive pipelines by utilizing the combined potential of language models and deep learning architectures. To develop a powerful AI application for requirements extraction by utilizing the combined potential of LLMs and DL architectures, this study presents ReqNet framework. The framework encompasses 7 most widely used LLMs variants (small, large, Xlarge, XXlarge) and 2 DL architectures (LSTM, GRU). The framework facilitates the development of three distinct types predictive pipelines, namely standalone LLMs, LLMs + external classifiers and an ensemble of multiple LLMs representation + external classifiers. Extensive experimentation of 48 predictive pipelines across 2 public core datasets and 1 independent test set, demonstrates that predictive pipelines made up from LLMs and DL architectures generally exhibited superior performance compared to pipelines solely reliant on LLMs. In addition, a ensemble of three distinct LLMs (ALBERT, BERT and XLNet) and LSTM classifier achieved a 3% improvement in F1-score over state-of-the-art predictors on the PURE dataset, a 10% improvement on the Dronology dataset and a 3% improvement on the RFI independent test set
PassionNet: An Innovative Framework for Duplicate and Conflicting Requirements Identification
No full textEarly detection and resolution of duplicate and conflicting requirements can significantly enhance project efficiency and overall software quality. Researchers have developed various computational predictors by leveraging Artificial Intelligence (AI) potential to detect duplicate and conflicting requirements. However, these predictors lack in performance and requires more effective approaches to empower software development processes. Following the need of a unique predictor that can accurately identify duplicate and conflicting requirements, this research offers a comprehensive framework that facilitate development of 3 different types of predictive pipelines: language models based, multi-model similarity knowledge-driven and large language models (LLMs) context + multi-model similarity knowledge-driven. Within first type predictive pipelines landscape, framework facilitates conflicting/duplicate requirements identification by leveraging 8 distinct types of LLMs. In second type, framework supports development of predictive pipelines that leverage multi-scale and multi-model similarity knowledge, ranging from traditional similarity computation methods to advanced similarity vectors generated by LLMs. In the third type, the framework synthesizes predictive pipelines by integrating contextual insights from LLMs with multi-model similarity knowledge. Across 6 public benchmark datasets, extensive testing of 760 distinct predictive pipelines demonstrates that hybrid predictive pipelines consistently outperforms other two types predictive pipelines in accurately identifying duplicate and conflicting requirements. This predictive pipeline outperformed existing state-of-the-art predictors performance with an overall performance margin of 13% in terms of F1-scor
Comparison of feature selection methods in text classification on highly skewed datasets
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DNA sequence analysis landscape: a comprehensive review of DNA sequence analysis task types, databases, datasets, word embedding methods, and language models
No full textDeoxyribonucleic acid (DNA) serves as fundamental genetic blueprint that governs development, functioning, growth, and reproduction of all living organisms. DNA can be altered through germline and somatic mutations. Germline mutations underlie hereditary conditions, while somatic mutations can be induced by various factors including environmental influences, chemicals, lifestyle choices, and errors in DNA replication and repair mechanisms which can lead to cancer. DNA sequence analysis plays a pivotal role in uncovering the intricate information embedded within an organism's genetic blueprint and understanding the factors that can modify it. This analysis helps in early detection of genetic diseases and the design of targeted therapies. Traditional wet-lab experimental DNA sequence analysis through traditional wet-lab experimental methods is costly, time-consuming, and prone to errors. To accelerate large-scale DNA sequence analysis, researchers are developing AI applications that complement wet-lab experimental methods. These AI approaches can help generate hypotheses, prioritize experiments, and interpret results by identifying patterns in large genomic datasets. Effective integration of AI methods with experimental validation requires scientists to understand both fields. Considering the need of a comprehensive literature that bridges the gap between both fields, contributions of this paper are manifold: It presents diverse range of DNA sequence analysis tasks and AI methodologies. It equips AI researchers with essential biological knowledge of 44 distinct DNA sequence analysis tasks and aligns these tasks with 3 distinct AI-paradigms, namely, classification, regression, and clustering. It streamlines the integration of AI into DNA sequence analysis tasks by consolidating information of 36 diverse biological databases that can be used to develop benchmark datasets for 44 different DNA sequence analysis tasks. To ensure performance comparisons between new and existing AI predictors, it provides insights into 140 benchmark datasets related to 44 distinct DNA sequence analysis tasks. It presents word embeddings and language models applications across 44 distinct DNA sequence analysis tasks. It streamlines the development of new predictors by providing a comprehensive survey of 39 word embeddings and 67 language models based predictive pipeline performance values as well as top performing traditional sequence encoding-based predictors and their performances across 44 DNA sequence analysis tasks
ProSol-multi: Protein solubility prediction via amino acids multi-level correlation and discriminative distribution
No full textProtein solubility prediction is useful for the careful selection of highly effective candidate proteins for drug development. In recombinant proteins synthesis, solubility prediction is valuable for optimizing key protein characteristics, including stability, functionality, and ease of purification. It contains valuable information about potential biomarkers or therapeutic targets and helps in early forecasting of neurodegenerative diseases, cancer, and cardiovascular disorders. Traditional wet-lab experimental protein solubility prediction approaches are error-prone, time-consuming, and costly. Researchers harnessed the competence of Artificial Intelligence approaches for replacing experimental approaches with computational predictors. These predictors inferred the solubility of proteins by analyzing amino acids distributions in raw protein sequences. There is still a lot of room for the development of robust computational predictors because existing predictors remain fail in extracting comprehensive discriminative distribution of amino acids. To more precisely discriminate soluble proteins from insoluble proteins, this paper presents ProSol-Multi predictor that makes use of a novel MLCDE encoder and Random Forest classifier. MLCDE encoder transforms protein sequences into informative statistical vectors by capturing amino acids multi-level correlation and discriminative distribution within raw protein sequences. The performance of proposed encoder is evaluated against 56 existing protein sequence encoding methods on a widely used protein solubility prediction benchmark dataset under two different experimental settings namely intrinsic and extrinsic. Intrinsic evaluation reveals that from all sequence encoders, proposed MLCDE encoder manages to generate non-overlapping clusters of soluble and insoluble classes. In extrinsic evaluation, 10 machine learning classifiers achieve better performance with proposed MLCDE encoder as compared to 56 existing protein sequence encoders. Moreover, across 4 public benchmark datasets, proposed ProSol-Multi predictor outshines 20 existing predictors by an average accuracy of 3%, MCC and AU-ROC of 2%. ProSol-Multi interactive web application is available at https://sds_genetic_analysis.opendfki.de/ProSol-Multi
Circ-LocNet: A Computational Framework for Circular RNA Sub-Cellular Localization Prediction
No full textCircular ribonucleic acids (circRNAs) are novel non-coding RNAs that emanate from alternative splicing of precursor mRNA in reversed order across exons. Despite the abundant presence of circRNAs in human genes and their involvement in diverse physiological processes, the functionality of most circRNAs remains a mystery. Like other non-coding RNAs, sub-cellular localization knowledge of circRNAs has the aptitude to demystify the influence of circRNAs on protein synthesis, degradation, destination, their association with different diseases, and potential for drug development. To date, wet experimental approaches are being used to detect sub-cellular locations of circular RNAs. These approaches help to elucidate the role of circRNAs as protein scaffolds, RNA-binding protein (RBP) sponges, micro-RNA (miRNA) sponges, parental gene expression modifiers, alternative splicing regulators, and transcription regulators. To complement wet-lab experiments, considering the progress made by machine learning approaches for the determination of sub-cellular localization of other non-coding RNAs, the paper in hand develops a computational framework, Circ-LocNet, to precisely detect circRNA sub-cellular localization. Circ-LocNet performs comprehensive extrinsic evaluation of 7 residue frequency-based, residue order and frequency-based, and physio-chemical property-based sequence descriptors using the five most widely used machine learning classifiers. Further, it explores the performance impact of K-order sequence descriptor fusion where it ensembles similar as well dissimilar genres of statistical representation learning approaches to reap the combined benefits. Considering the diversity of statistical representation learning schemes, it assesses the performance of second-order, third-order, and going all the way up to seventh-order sequence descriptor fusion. A comprehensive empirical evaluation of Circ-LocNet over a newly developed benchmark dataset using different settings reveals that standalone residue frequency-based sequence descriptors and tree-based classifiers are more suitable to predict sub-cellular localization of circular RNAs. Further, K-order heterogeneous sequence descriptors fusion in combination with tree-based classifiers most accurately predict sub-cellular localization of circular RNAs. We anticipate this study will act as a rich baseline and push the development of robust computational methodologies for the accurate sub-cellular localization determination of novel circRNAs
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