23 research outputs found
URDU–TAFSEER AL-SIRAJ-UL-MUNEER (ALLMA KHATEEB SHARBEENI 977) CRITICAL VIEW FOR CHARACTERISTICS, PATTERN AND METHOD
No full textThis article describes the methodology and characteristics of tafsir “Al_ Siraj ul Munir”.This are one of finest work of Allma Khateeb al Sharbini a 10th century prominent interpreter. Several editions of this Tafsir have been published. However, the edition of” Maktbea Bolaq Alamireeya, Alqahera (publication year: 1285 A.H)" published in four volumes is selected for this study. This interpretation is based on conventional narrations, authentic quotations from the Islamic scholars and lingual and grammatical discussions. As a witness, causes of verses(Asbab-e-Nuzul), Makki and Madani Surah’s(chapters),the abrogating and abrogated verses (Alnasikh Walmansukh) and Islamic jurisprudence have been discussed in it where needed. The quality of this translation which is admirable is that mostly authentic Ahadith from original sources and references to well-known basic books.
The application of Shari’ah and international human rights law in Saudi Arabia
No full textThis thesis was submitted for the degree of Doctor of Philosophy and was awarded by Brunel UniversityThe present dissertation provides an analytical and comparative study of the application of Islamic law (Shari’ah) and international human rights law in the Kingdom of Saudi Arabia. It provides an analysis of the sources of Islamic law as well as the sources of international law to set the background for analysis and defines the nature of both laws. It also tackles the subject of the domestic application of international human treaties in Saudi Arabia.
In addition, it examines some reservations Saudi Arabia has entered to some of the international human rights treaties it has ratified, specifically the Convention on the Elimination of All Forms of Discrimination against Women (CEDAW) and the Convention on the Rights of the Child (CRC). It also sheds some light on the political, cultural and religious obstacles to the realisation of norms protected by international human rights treaties in the country, and in other countries for that matter, clearly stating the impossibility of implementing the provisions of the international human rights treaties in their entirety. This is due to the various political and legal developments towards the internationalization of the concept of human rights. It observes that despite the existence of the international human rights treaties, which aim at reinforcing a universal realisation of international human rights, these rights cannot be possibly realised by all countries.
To stress the importance Saudi Arabia attaches to the issue of human rights, the dissertation discusses some rights of women before Saudi courts in family matters, an issue which has been criticised by some international human rights treaties, and examines to what extent the country has managed to tackle the issue of domestic violence, particularly violence against women. It provides an overview of the major causes of domestic violence against women in Saudi Arabia, presents some cases of domestic violence before Saudi courts and sheds some light on the measures taken by the Saudi government to combat domestic violence against women. It also tackles this issue both in the international and domestic legal frameworks, clearly stating the Islamic standpoint on the issue, namely that Islamic law, and Saudi Arabia for that matter, whose laws are essentially derived from the two main sources of Shari’ah. It also discusses the common forms of violence against women in Saudi Arabia and suggests a number of recommendations towards more effective protection of women against violence in the country.
The dissertation concludes by presenting a number of obstacles in the way of executing judicial decisions in the Kingdom as well as the obstacles which negatively affect the performance of the new code of law practice. It also presents some recommendations concerning personal status law obstacles and hindrances to progress and attempts to answer the research questions it has posed
Comparative Evaluation of Machine Learning and Deep Learning Models for Real Estate Price Prediction
No full textAccurate real estate price prediction plays a vital role in informed decision-making for investors, policymakers, and stakeholders. This study evaluates various machine learning and deep learning models for predicting real estate prices using the House Prices 2023 dataset which contains 168,000 entries of Pakistani property data. In our proposed methodology we performed data preprocessing and features engineering to standardize the data. We performed extensive experiments by using machine learning (ML) and deep learning (DL) models on our preprocessed data. The model’s performance was evaluated based on the R-squared (R²) score and Mean Squared Error (MSE) metrics. Based on the provided metrics, the Decision Tree achieved the highest performance with an R² of 0.9968 and an MSE of 0.0021, followed by Random Forest with an R² of 0.990 and MSE of 0.0007. Similarly, other ML models like Gradient Boosting and XG Boost also outperformed by achieving (R² 0.9959, MSE 0.0028 R² 0.9747, and MSE 0.0170) respectively. In contrast, models like AdaBoost, Neural Network, and Convolutional Neural Network (CNN) showed comparatively lower performance due to the nature of the data. The study emphasizes that ensemble-based models like Decision Trees and Random Forests are highly effective at identifying patterns in real estate prices. Additionally, applying optimization techniques improves the models ability to generalize and perform well on unseen data
Comparative Evaluation of Machine Learning and Deep Learning Models for Real Estate Price Prediction
No full textAccurate real estate price prediction plays a vital role in informed decision-making for investors, policymakers, and stakeholders. This study evaluates various machine learning and deep learning models for predicting real estate prices using the House Prices 2023 dataset which contains 168,000 entries of Pakistani property data. In our proposed methodology we performed data preprocessing and features engineering to standardize the data. We performed extensive experiments by using machine learning (ML) and deep learning (DL) models on our preprocessed data. The model’s performance was evaluated based on the R-squared (R²) score and Mean Squared Error (MSE) metrics. Based on the provided metrics, the Decision Tree achieved the highest performance with an R² of 0.9968 and an MSE of 0.0021, followed by Random Forest with an R² of 0.990 and MSE of 0.0007. Similarly, other ML models like Gradient Boosting and XG Boost also outperformed by achieving (R² 0.9959, MSE 0.0028 R² 0.9747, and MSE 0.0170) respectively. In contrast, models like AdaBoost, Neural Network, and Convolutional Neural Network (CNN) showed comparatively lower performance due to the nature of the data. The study emphasizes that ensemble-based models like Decision Trees and Random Forests are highly effective at identifying patterns in real estate prices. Additionally, applying optimization techniques improves the models ability to generalize and perform well on unseen data
Experimental Characterisation and Modelling of Atmospheric Fog and Turbulence in FSO
No full textFree space optical (FSO) communication uses visible or infrared (IR) wavelengths to broadcast high-speed data wirelessly through the atmospheric channel. The performance of FSO communications is mainly dependent on the unpredictable atmospheric channel such as fog, smoke and temperature dependent turbulence. However, as the real outdoor atmosphere (ROA) is time varying and heterogeneous in nature as well as depending on the magnitude and intensity of different weather conditions, carrying out a proper link assessment under specific weather conditions becomes a challenging task. Investigation and modelling the ROA under diverse atmospheric conditions is still a great challenge in FSO communications. Hence a dedicated indoor atmospheric chamber is designed and built to produce controlled atmosphere as necessary to mimic the ROA as closely as possible. The experimental results indicate that the fog attenuation is wavelength dependent for all visibility V ranges, which contradicts the Kim model for V < 0.5 km. The obtained result validates that Kim model needs to be revised for V < 0.5 km in order to correctly predict the wavelength dependent fog attenuation. Also, there are no experimental data and empirical model available for FSO links in diverse smoke conditions, which are common in urban areas. Therefore, a new empirical model is proposed to evaluate the wavelength dependent fog and smoke attenuation by reconsidering the q value as a function of wavelength rather than visibility. The BER performance of an FSO system is theoretically and experimentally evaluated for OOK- NRZ, OOK-RZ and 4-PPM formats for Ethernet line data-rates from light to dense fog conditions. A BER of 10-6 (Q-factor ≈ 4.7) is achieved at dense fog (transmittance, T = 0.33) condition using 4-PPM than OOK-NRZ and OOK-RZ modulation schemes due to its high peak-to-average power ratio albeit at the expense of doubling the bandwidth. The effects of fog on OOK-NRZ, 4-PAM and BPSK are also experimentally investigated. In comparison to 4-PAM and OOK-NRZ signals, the BPSK modulation signalling format is more robust against the effects of fog. Moreover, the effects of using different average transmitted optical communication powers Popton the T and the received Q-factor using the OOK-NRZ modulation scheme are also investigated for light and dense fog conditions. The results show that for an FSO system operating at a Q-factor of 4.7 (for BER = 10-6), the required Q-factor is achieved at T of 48% under the thick fog condition by increasing Popt to 1.07 dBm, whereas the values of T are 55% and ~70% for the transmit power of 0.56 dBm and -0.7 dBm, respectively. The experimental characterisation and investigation of the atmospheric turbulence effect on the Ethernet and Fast-Ethernet FSO link is reported using different modulation schemes. The experiment is carried out in a controlled laboratory environment where turbulence is generated in a dedicated indoor atmospheric chamber. The atmospheric chamber is calibrated to mimic an outdoor turbulence conditions and the measured data are verified against the theoretical predictions. The experiment also demonstrates methods to control the turbulence levels and determine the equivalence between the indoor and outdoor FSO links. The results show that the connectivity of Ethernet and Fast-Ethernet links are highly sensitive to atmospheric turbulence. The results also show that the BPSK and OOK-NRZ modulation signalling formats are more robust against the weak atmospheric turbulence conditions than PAM signal
Comparison of Machine Learning Algorithms for Sepsis Detection
No full textSepsis is a very fatal disease, causing a lot of causalities all over the world, about 2, 70,000 die of Sepsis annually, thus early detection of Sepsis disease would be a remedy to prevent this disease and it would be a big relief to the family of sepsis patients. Different researchers have worked on sepsis disease detection and its prediction but still the need to have an improved model for Sepsis detection remains. We compared various machine learning algorithms for Sepsis detection and used the dataset publicly available for all the researchers at Physionet.org, the dataset contains many empty or Null values, we applied backward filling and forward filling techniques, and we calculated missing values of MAP using equation (1) which gives more precise results, we divided the 40,336 files of datasets A and B into 80% training set and 20% testing set. We applied the algorithms twice one time using vital signs and clinical values of patients and the second time using only vital signs of the patients; using vital signs only the training accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.992, 0.999, 0.981, 0.981, and 0.981 respectively, while the testing accuracy of KNN, Logistic Regression, Random Forest, MLP, and Decision Trees was 0.987, 0.980, 0.983, 0.981, and 0.981 respectively, for Sepsis Label 0, the value of precision for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 0.99, 0.98, 0.98, 0.98, and 0.98 respectively, while the value of recall for KNN, Random Forest, Decision Trees, Logistic Regression, and MLP was 1.00, 1.00, 1.00, 1.00, and 1.00 respectively; the comparison of all the above-mentioned algorithms showed that KNN leads over all the competitors regarding the accuracy, precision, and recall.
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Uso del suelo y modificación de la cobertura terrestre y su impacto en la biodiversidad y los servicios de los ecosistemas en el distrito de Kurram, Pakistán
No full textModifications of land use and vegetation cover are proceeding faster than ever before in human history, with a considerable reduction in forest cover in biodiversity hotspots. We investigated the land use and vegetation cover changes, their impact on biodiversity in the Kurram District, Pakistan, for 27 years (1988 to 2015). Temporal satellite imagery was processed using a supervised maximum likelihood classification algorithm in ARCGIS 10.1 to elucidate information regarding land use/land cover changes, with conducted structured interviews to obtain the inhabitants' perspectives on their dependence on ecosystems in Kurram, and how their environment is changing. We found that the land under forest cover and rangeland showed a remarkable decrease over the study period. This decline in rangeland and forest cover was a result of the increased of farmland, barren land. The study area is part of a biodiversity, with important medicinal, rare and unique plant species.Las modificaciones del uso de la tierra y la cobertura vegetal están avanzando más rápido que nunca en la historia de la humanidad, con una reducción considerable de la cobertura forestal en los puntos críticos de biodiversidad. Investigamos el uso de la tierra y los cambios en la cobertura vegetal, su impacto en la biodiversidad en el distrito de Kurram, Pakistán, durante 27 años (1988 a 2015). Las imágenes satelitales temporales se procesaron utilizando un algoritmo de clasificación de máxima verosimilitud supervisada en ARCGIS 10.1 para dilucidar información sobre los cambios en el uso del suelo / cobertura del suelo, con entrevistas estructuradas realizadas para obtener las perspectivas de los habitantes sobre su dependencia de los ecosistemas en Kurram y cómo está cambiando su entorno. Descubrimos que la tierra cubierta por bosques y pastizales mostró una disminución notable durante el período de estudio. Esta disminución en los pastizales y la cubierta forestal fue el resultado del aumento de las tierras de cultivo, tierras estériles. El área de estudio es parte de una biodiversidad, con importantes especies de plantas medicinales, raras y únicas
