VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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A Study of Brain Tumor detection using MRI images
Get PDFThis study investigates the advantages of an algorithm for detecting brain tumors using magnetic resonance imaging. The thematic analysis demonstrates how the algorithm can be understood and changed through narrative descriptions. The findings highlight areas for improvement, which aids in the direction of future research. Based on unexpected results, the algorithm was improved over time. Even though the study had some restrictions and limitations, this makes the algorithm a versatile tool for detecting brain tumors. This study is an important step toward better understanding algorithmic applications and demonstrates the significance of qualitative insights in shaping the future of brain tumor detection methods
AdaptorPro:A Deep Learning Approach for Accurate Identification of Adaptor Proteins
Get PDFAdaptor proteins, pivotal in signal transduction ,consist of diverse modular domains, each exhibiting unique binding activities, forming complexes with intracellular signaling molecules. Implications of adaptor proteins in various human diseases underscore the need for accurate predictive models. In addressing this, we compiled a dataset featuring 2,484 positive (G0:0060090) and 15,495 negative (G0:0140110) results. Removal of highly similar sequences using the bio-conda CDHIT API yielded 1429 non-redundant clustered Adaptor proteins for G0:0060090. Similarly, G0:0140110 resulted in 8076 non-redundant clustered Adaptor proteins. Employing a 5-step rule predictor based on statistical moments and PseAAC for feature extraction, we split the dataset into 80% training and 20% testing. Our approach, currently employing known neutral models, advances bioinformatics efforts in anticipating the actions of adaptor proteins, holding promise for unravelingintricate cellular signaling mechanisms
A Taxonomy for Supporting Industry- Academia Communication in Software Quality
No full textSoftware quality is one of the most important components of software development. Software quality attracts the user\u27s attention and produces reliable and error-free software. Poor approach strategies lead to less effective outcomes and a higher likelihood of failure. The gap between software quality research and practice is one of the main problems with software quality. The difference between the way software quality research is communicated and the way industry concerns related to software quality is the actual gap.software industries are unaware about rapid technological change, complexities of Modern software systems, security concerns, skill shortage impact on software quality, to overcome theses problems and gaps. Taxonomy is proposed to enhance industry-academia collaboration by facilitating better communication between software quality research and practice. Some of the best quality standards are outlined and authorized in this paper that yields the greatest out-comes. A methodical and goal-oriented strategy is used to develop the proposed taxonomy, with the help of literature review, blogs, articles, and interviews with researchers and practitioners. Twenty distinct questions regarding team co-ordination, dispersion, culture, behaviors and attitudes, experience, and other topics are included in the questionnaire. The taxonomy is evaluated through online survey by implementing it in an industry-academia collaboration project. Researchers and practitioners could use the proposed taxonomy to classify and identify quality concerns or obstacles
A three step seventh order iterative method for solution nonlinear equation using Lagrange interpolation technique
Get PDFThis research paper comprehensively presents the derivation of a seventh-order iteration scheme designed to obtain simple roots of nonlinear equations through the utilization of Lagrange interpolation technique. The scheme is characterized by the requirement for three function evaluations and one evaluation of the first derivative in each iteration. A detailed convergence analysis is also carried out to assess the efficacy of the proposed method. Additionally, the paper includes comprehensive numerical experiments aimed at confirming the theoretical results and illustrating the competitive performance of the derived iteration scheme
Comparative Analysis of the Portrayal of Islamic Feminist Ideals in A Woman is No Man and The Henna Artist
Get PDFThis study aims at employing content analysis with qualitative approach for undertaking a comparative analysis of the portrayal of Islamic feminist ideals in two contemporary literary works: A Woman is No Man by Etaf Rum and The Henna Artist by Alka Joshi. Since both novels provides narratives of the lives of women in Muslim societies, therefore, this study explores issues such as women’s struggle for agency, autonomy, and empowerment in the context of male dominated norms and cultural expectations. Utilizing textual evidence, this research explores the authors’ portrayals of Islamic feminist ideals specifically, gender equality, female education, and women’s rights. Also, whether these ideals are negotiated and contested through a critical engagement of the cultural and social realities contained within the Muslim communities portrayed by the authors. In sum, this study contributes to a comprehensive understanding of the multiple voices and perspectives that make up the Islamic feminist movement of today as well as to the complexities of gender relations in current Muslim societies
A Novel Relaxed Dynamic Local Ternary Pattern Texture Descriptor for Face Anti-Spoofing
Get PDFNow a day’s biometric authorization is utilized for security applications like identification and recognition. There are many biometric traits like fingerprint, face, iris, voice etc. The motivation behind the use of biometric systems is that remembering pin and password is quite difficult for a person and also it can be easily stolen. Unlike pin and password based system, biometric system cannot easily be stolen and forgotten because of its unique characteristics. In Biometric systems, the face based biometric system is mostly used for security purpose to identify the users according to the uniqueness of their features and easy availability. There are still many challenges that faced by face biometric security systems, such as some culprits make fake faces made from different material e.g. paper, digital electronic devices or 3D masks. The system can be easily be spoofed and give access to unauthorized persons. Due to this security breach an identification system should be designed that verifies whether the users are genuine or fake. In this paper, a novel Texture descriptor is proposed named as Relaxed Dynamic Local Ternary Pattern (RDLTP) for texture analysis in the field of anti-spoofing. The proposed texture descriptor is analyzed on publically available datasets to differentiate that either the input is from genuine face or spoofed face via Support Vector Machine (SVM) Classifier. The obtained outcomes from the proposed RDLTP Descriptor showed improved accuracy and performed well as compared to the most popular texture descriptor from the literature i.e. Local Binary Pattern (LBP) and Local Ternary Pattern (LTP).
 
SEO: TIPS to Minimize Bounce Rate of Website User
Get PDFDue to extensive use of the Internet, the WEB holds an immeasurable amount of data, and Search Engines (SE) are essential tools for finding, sorting, and ranking the value of that data on the web. The potential of SEs is very significant because a major portion of web traffic is driven by SEs, such as Google, Bing, Baidu, Yahoo, etc., and their results route end-users to specific websites. Due to the vital role of SEs, search results are becoming decisive for the website owners to compete with their rivals. Search Engine Optimization (SEO) is a key process for getting better online visibility on search results from search engines. The objective of this study is to technically justify the importance of search engines and SEO. More specifically, the main emphasis is to quantify the importance of bounce rate and load time of retaining users on the website. Data from the web development blog “MLT” has been extracted to demonstrate the impact of SEO on website performance, bounce rate, and loading time. Google Analytics and Page Speed Insight have been employed to get the impact of SEO. Finally, the addition of SEO elements on an experimental project and the positive impact on websites are explained. Results attained from the experimental work demonstrate the significance of key SEO factors in minimizing the Bounce rate
Detection of Questions from Text Data Using LSTM-Deep Learning Model
Get PDFThis paper discusses the importance of detecting questions in textual data for various applications in natural language processing (NLP), such as question answering and chatbot creation. The proposed approach employs long short-term memory (LSTM) models to accurately identify questions by leveraging the sequential nature of language.The paper highlights that LSTM models address challenges like ambiguous language and varying sentence structures. They allow the model to learn from sequential patterns, crucial for understanding the intent behind the text. The preprocessing steps, including tokenization, embedding, and padding, are detailed to prepare the data for training and testing. The study investigates the impact of hyperparameters like hidden layers, hidden states, and optimizer choice on the LSTM algorithm’s performance. In experiments on benchmark datasets, the proposed LSTM-based approach consistently outperforms conventional machine learning models, achieving a remarkable accuracy of 99.25% on the test dataset. The paper concludes by suggesting future directions, including applyingthe approach to other NLP tasks like named entity recognition, sentiment analysis, and text classification. Further optimization for specific datasets or domains is also encouraged. Overall, this research contributes to robust question detection models in NLP, with potential applications in various fields.
Hybrid FNN-DNN Approach for Early Detection of Cardiac Arrhythmia: A Novel Framework for Enhanced Diagnosis
Get PDFThis work introduces a relatively new hybrid approach to detect arrhythmias noninvasively at the early stages by combining Feedforward Neural Networks (FNN) with Deep Neural Networks (DNN). It is oriented towards the critical area of detecting minute anomalies in the heart rhythm impetuses which are essential to enable prompt management and better results. The foreseen framework tries to solve the gaps in the existing diagnostic methods by using deep learning techniques especially in understanding sequential patterns in medical information. Detection at a speed and on a proper level is a lifesaving key because cardiac arrhythmias can cause serious problems like the stroke and the heart failure. We should make sure that there are more sensitive techniques (other than the traditional ECGs) as the conventional methods (such as ECGs) suffer from some restrictions which may be subjective and also can achieve low accuracy. The distinct attributes of subtle pattern uncovering and making the easy yet precise diagnoses of the Hybrid FNN-DNN model places it a SMART choice for the future. The results demonstrate the need to detect heart rhythm disorders in the early stage as they can have a great influence in terms of patient health and the health sector spending on serious arrhythmia consequences like heart failure or stroke. The effectiveness of the hybrid model in separation between persons with heart disease and the rest is measured by a composite assessment that employs many measures e.g., accuracy, precision, recall, F1-score and AUC-ROC curve analysis. The empirical result highlights that the hybrid model has achieved the same accuracy as both FNN model with 84.8% and DNN model which are 84.8% as well. Another point that the article alludes to is that in the medical environment, deep learning models should be interpretable and provide the therapeutic information needed. Therefore, in order to recognize that the created model coincides with clinical practices and strives to improve patients\u27 care, collaborative efforts with domain experts are carried out. However, the hybrid FNN-DNN strategy is just the beginning for the developing cardiovascular management and treatment field as it provides a good path to progressing with getting better detection and early diagnosis of cardiac arrhythmias. It is necessary to carry out more research and proof-of-concept validation of the proposed tool for a wider population
A Novel Approach to Improve Question Answering System
Get PDFQuestion Answering (QA) systems are automated solutions that provide accurate responses to queries presented in natural language. This research proposes an optimized model aimed at enhancing the performance of QA systems by integrating logical reasoning with traditional statistical methods. The study focuses on improving the interaction and accuracy of these systems. Evaluation of the proposed model shows notable improvements in response accuracy and reliability. The fine-tuned QA model demonstrated its capability to generate precise answers, such as correctly identifying alternative names for the Amazon rainforest based on the provided context. These findings underscore the potential of the proposed approach to advance the effectiveness of QA systems significantly