VFAST - Virtual Foundation for Advancement of Science and Technology (Pakistan)
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New Results for Riemann Solution of the Cargo-LeRoux Model by the Application of Flux-Limiter Schemes
Get PDFThe Rienamm solution of the Cargo-LeRoux model has been recently introduced in [1] in which authors have found the exact solutions to the initial value problem. This work is the first attempt to apply numerical methods for the Cargo-LeRoux model. The higher-order flux limiter method applied in this paper holds the total variation diminishing property and gives smooth solutions in steep gradient regions. Various limiter functions that lead to different accuracy in numerical results are tested for the Riemann problem. The numerical investigations presented in this work can be used to review limiter-based TVD schemes extensively and to construct a class of highly efficient finite volume/ finite difference methods for such models.
Collection and Compilation of Qur’anic Verses Related to various Animals
No full textThis research paper aims to examine the significance of various animals mentioned in the Holy Qur\u27an, which are depicted for multiple important purposes. As a divine scripture intended to guide humanity on the right path, the Qur\u27an incorporates descriptions of animals not only to demonstrate the Creator\u27s power but also to use them as parables that impart lessons to humans. Additionally, these depictions emphasize that animals were created for the benefit of humans, addressing one of the perpetual needs of mankind. Understanding the Quranic perspective on animals is crucial, as it contains essential knowledge that aids believers in adhering to Allah\u27s instructions. This paper is divided into three sections: the first provides a definition of "animal," the second discusses land animals, and the third explores other creatures, each accompanied by relevant verses from the Surahs. The study adheres strictly to established research principles.
Fine-Tuning Mistral 7b Large Language Model For Python Query Response And Code Generation: A Parameter Efficient Approach
Get PDFThe research delves into the concept of fine-tuning and its unique application to Python queries and code generation. This process involves adjusting the model\u27s parameters to make it more proficient in responding to Python-only queries and generating corresponding code. It underscores the untapped potential of fine-tuning large language models and the significance of combining Parameter-Efficient fine-tuning with quantization to reduce memory usage. This was achieved by the pre-trained language model’s fine-tuning and meticulously evaluating and contrasting it with its base model. Notably, the model was fine-tuned to proficiently respond to Python-only queries and generate corresponding code, a novel and intriguing application of fine-tuning. We utilized Mistral7B-Instruct version 0.2 as our base large language model for fine-tuning. The dataset, sourced from Kaggle, was a collection of Python Question-Answer pairs. Before fine-tuning, we meticulously cleaned and organized the dataset, ensuring its quality by arranging it in descending order based on their rankings. This rigorous and thorough approach instills confidence in the reliability of our results. Our research shows that the fine-tuned model outperformed the base Mistral7B Instruct version 0.2 model in BERTScore and demonstrated a significant performance boost when compared with the HumanEval metric. This clear and substantial improvement further affirms the effectiveness of our approach. Our research is a step forward in the realm of large language models, specifically in the coding sphere. It showcases a substantial improvement in understanding Python queries and generating code snippets. This has profound implications for the current trajectory of Natural Language Generation and Generative AI fields. Our findings act as a pivotal catalyst for further progress
Symbolic Collapse and Psychological Descent in King Lear: A Psychoanalytic Exploration of Transformation and Abjection
Get PDFThis research paper delves into King Lear through psychoanalytic lenses, examining themes of transformation, abjection, and the tension between symbolic and semiotic forces. Central to the analysis is Lear\u27s quest for unconditional love, revealing his internal conflicts and repressed fears. The dynamic between Lear’s desire for flattery from Goneril and Regan, and Cordelia’s restrained yet genuine affection, illustrates his struggle with authority and vulnerability. Key psychoanalytic theories, including Pauncz\u27s "Lear Complex," Freud\u27s Elektra Complex, and Kristeva\u27s theory of the abject, are applied to analyze Lear\u27s psychological descent. The storm symbolizes the collapse of Lear\u27s symbolic identity, while Cordelia’s return offers temporary solace but also underscores the cost of Lear’s flawed pursuit of love. This study illuminates how Shakespeare masterfully explores the complexities of human psychology, deepening our understanding of his characters\u27 internal struggles
A Hybrid Approach for Simultaneous Effective Automobile Navigation with DE and PSO
Get PDFAutomobile Navigation is fundamentally an optimization challenge focused on transportation logistics between a depot and various clients. In this paper, we specifically address the complex variant of Automobile Navigation that involves simultaneous pickup and delivery tasks, which must be executed concurrently at clients\u27 locations. This dual requirement introduces significant complexity, as traditional exact approaches struggle to rapidly identify near-optimal solutions due to the problem\u27s NP-hardness. Therefore, the objective of this research is to develop a novel hybrid algorithm that integrates Differential Evolution (DE) and Particle Swarm Optimization (PSO) to effectively solve the Automobile Navigation problem with simultaneous pickup and delivery. The proposed method uses the nearest neighbor heuristic to initially produce results. It is based on the iterated local search paradigm. Variable neighborhood descent is used to improve the search process by adding random sequences to the neighborhood structures for improved search space intensification. Furthermore, exploration across various sections of the search space is made possible by the perturbation process. This method solves the problem of different truck loads on every client visit because it does the pickup and delivery at the same time, which makes the Navigation strategy more effective
Marrying a Robot, A Critical Review in a Religious Context
Get PDFWe have unbalanced opinion about the sex. If we study the history of civilizations, we found that there are two theories about the sex. One theory is avid the sex and spend the life in forest alone. On the other hand, they started the worship of sex and made the sex industry. These two unbalanced theories are also existed in current era. Especially when we heard about the person who marry with the robot. In this situation question is raised, what is the teaching of Islam about the robotic marriage? To answer the question, this paper will be highlighting the basic concept of sex in Islam and Sharia status of robotic marriage
Transforming data from the image to the text domain: benign versus malignant micro-calcification classification
Get PDFIn this paper we present a new approach for the classification of benign and malignant micro-calcification clusters by transforming data from the image to the text domain. A string representation is extracted from binary micro-calcification segmentation images. We extracted two different features from the strings and combined different machine learning techniques towards benign versus malignant classification. We evaluated our proposed method on the DDSM database and experimental results indicates a Classification Accuracy equal to 92%.
Machine Learning Approches for Prediction of Mental Health Issues in Adolescents: A Comparative Survey
Get PDFMental health is recognized as a non-communicable disease that impairs human lives, sometimes beyond recovery. While everyone is at risk of developing a mental illness, adolescents are more prone to it due to various factors like hormonal changes, study pressure, social pressure, etc. If mental health goes ignored at this stage, it can cause serious, even fatal problems later on in life, which not only impacts a family but also the young workforce of a country. Hence, constant efforts are being made for the early detection of mental disorders so they can be treated better. Early prediction of mental health issues is a classic machine learning problem relying on patient history and data. In this survey, we discuss a total of 22 previous research papers based on machine learning algorithms and other statistical analysis tools employed for the said task and compare their efficacy. The research papers are categorized into different mental health disorders such as 1) Methods for predicting Depression and Anxiety 2) Methods for Suidial Prevalence 3) Methods for Predicting Autism Spectrum Disorder (ASD) 4) Methods for Predicting Substance Abuse among adolescents. On the basis of accuracy, the performance of machine learning prediction models was compared. CNN models, Random Forest, and XGBoost generally performed better than other models. There is centralized research in Pakistan on mental health based on machine learning so SPSS and other tools are mostly used for data analysis. The findings suggest that Machine learning algorithms can be effective for classifying and early predicting high-risk factors among adolescent
A Survey on Security Issues and Attacks of Fog Computing
Get PDFThere is a link between the cloud and the Internet of Things (IoT). The layer that makes up the dispersed network environment is exactly what it is. Cloud computing is brought out to the edge of the network through the type of networking topology referred as fog computing. Users can benefit greatly from fog computing. Fog\u27s primary role, similar to cloud computing, is to allow people mobility. Fog computing is becoming more and more popular, whereas at the same time, security dangers are growing every day. Users\u27 identification & verification are crucial. The fact of fog computing cannot effectively utilize the security and privacy solutions provided by cloud computing must be emphasized. The risks, issues, and solutions linked to security in fog computing are outlined throughout this study. The poll then includes information on ongoing research projects as well as open security and safety concerns for fog computing
An Improved Framework for Sindh School Monitoring System Android App
Get PDFSindh government has presented a system for observing schools called the Sindh School Monitoring System (SSMS) Framework. One of the part of the system is SSMS app, which is based on Android. SSMS app is widely used in the Sindh province in order to monitor the school with major focus on attendance. The SSMS app has increased the system performance in terms of attendance, however several flaw are present in its current framework. This paper identifies the key issues in the current framework such as identification and verification of Monitoring assistant (MA), school search options and SMC, teacher performance evaluation, reporting, curriculum, student performance evaluation and census, school building details, SNE, school amenities, GR register, NADRA verification, rights of MAs and online reporting options in the app. The changes are proposed in the existing framework, for the said key issues, which could improve the overall system performance. In order to validate the key findings and proposed changes in the existing framework a questionnaire has been prepared and evaluated from the SSMS app users. All the app users validated the proposed changes in the framework