2,127 research outputs found
ISSUES OF ETHICS AND ETHICS IN THE CHILDHOOD OF HAZRAT SHAIKH MUHAMMAD SADIQ MUHAMMAD YUSUF
No full textThis article discusses the issues of etiquette and morality in the upbringing of children of Sheikh Muhammad Sadiq Muhammad Yusuf and the responsibility of parents to the child. 
Magnetic induction framework synthesis: a general route to the controlled growth of metal-organic frameworks
No full textAbstract not availableHaiqing Li, Muhammad Munir Sadiq, Kiyonori Suzuki, Paolo Falcaro, Anita J. Hill∥ and Matthew R. Hil
Contemporary Issues and style of Khatam-ul-Nabieen Hazrat Muhammad (Peace be upon him)
No full textThe style of Khatam-ul-Nabieen Hazrat Muhammad (Peace be upon him) in addressing contemporary issues is both profound and multifaceted. His approach can be summarized as (Universal principles, Emphasis on Dialogue, Moral and Ethical Leadership, Inclusivity and Diversity, Community Welfare, Adaptability to Change).The teachings of the Prophet Muhammad(PBUH) are rooted in universal principles of justice, compassion and respect for human dignity. These principles resonate with contemporary challenges such as inequality, discrimination and social injustice. He emphasized open dialogue and consolation as essential tools for resolving conflict. This approach is crucial today, as it promotes understanding and cooperation among diverse communities. His exemplary character served as a model for ethical leadership, highlighting the importance of integrity and accountability in leadership role, relevant to modern governance. The Prophet ability to adapt his message to the context of his time illustrates the importance of contextual understanding in addressing modern issue effectively. His interactions with various tribe and faiths underscore the significance of inclusivity. In today globalized world, this fosters social cohesion and mutual respect among different cultures and religions. The style of Hazrat Muhammad (SAW) in dealing with contemporary issue offers valuable insights for today spciety.His teaching encourage a holistic approach to problem –solving, emphasizing compassion, dialogue and ethical leadership, which are essential for fostering peace and harmony in a diverse world
امام جعفر صادق ؑکی طبّی خدمات : Medical Services Of Imam Jafar Sadiq
No full textThis paper is about Imam Jafar Sadiq. Imam Jafar Sadiq had the honor of spending almost 12 years of his 65-year life under the shadow of his grandfather Hazrat Imam Zain al-Abidin (peace be upon him) and 19 years under the shadow of his father Majid Imam Muhammad Baqir (peace be upon him). After that, 34 years of his Imamate were available, during which time he benefited the world with his knowledge because during this time, the Umayyads were spending their days of decline, so they were worried about saving their power. And Bani Abbas were in their heyday, so they were busy consolidating their power.
Therefore, due to the conflict between these two families, Imam Jafar Sadiq got some freedom, he took advantage of this opportunity to organize the publication of the school of Ahl al-Bayt (peace be upon him), so the Prophet’s Mosque in Madinah and Kufa. I turned the mosque of Kufa into a place of learning, in which scholars from all over the world came to quench their thirst for knowledge, so about four thousand students, including non-Muslims, were blessed with the highest position of imam.
Keywords:
Imam Zain al-Abidin, Medical Services, publication of the school, position of imam
Design and implementation of a hybrid RFID-GPS human tracking system / Muhammad Sadiq Rohei
No full textIn pervasive computing research, object tracking and specification is an emerging trend. Researchers have been working on ways to allow people to work safely and freely within certain environments. With the use of RFID technology, objects can become part of real-world communication indoors while GPS tracking accommodates outdoor environments. The RFID-GPS hybrid system uses features from both technologies to provide a robust location tracking system that covers a wide variety of applications, including the tracking of animals, vehicles or people. This can be used to improve the safety of important or vulnerable members of society, particularly in developing countries where persecution and kidnapping are rampant. For example, the rate at which medical practitioners are being kidnapped has hit 41%, which makes it imperative for this study to develop an application that facilitates a safer commute and possible monitoring. To this end, an approach based on comprehensive studies is proposed using simulation to evaluate the suitability of epidermal RFID tags (implanted under the skin of participants) in a hybrid RFID-GPS tracking system operating under the IoT paradigm. The study's model aims at improving the safety of doctors by assigning them IDs using unique UHF passive RFID tags that operate at a frequency of 868 MHz, which is regionally altered. The RFID tag was designed and simulated through technical methodology using Matlab, the Simulink environment and relevant system middleware prototyped through the Microsoft ASP MVC platform. Using these technologies, the GPS coordinates of the research-based area and the virtual floor plan of the unified centre are included in the system. This was to allow end users to visualise system performance on Google Maps and within the building parameters. The results showed that the simulated tag had a high transmission rate with a corresponding factor of γ =0.6, almost 1.57 dBi power gain, and a reading distance of nearly 4 metres. The system's middleware contains outstanding features such as innovative, fast and accurate real-time location tracking. It has been proven to guarantee the safety of doctors in hospitals/fieldwork, and is ready for actual establishment at a unified centre
Data prediction and recalculation of missing data in soft set / Muhammad Sadiq Khan
No full textUncertain data cannot be processed by using the regular tools and techniques of clear data. Special techniques like fuzzy set, rough set, and soft set need to be utilized when dealing with uncertain data, and each special technique comes with its own advantages and snags. Soft set is considered as the most appropriate of these techniques. A soft set application represents uncertain data in tabular form where all values are represented by 0 or 1. Researchers use soft set representation in a number of applications involving decision making, parameter reduction, medical diagnosis, and conflict analysis. Soft set binary data may be missing due to communicational errors or viral attacks etc. Soft sets with incomplete data cannot be used in applications. Few researchers have worked on data filling and recalculating incomplete soft sets, and the current research focuses on predicting missing values and decision values from non-missing data or aggregates. A soft set needs to be preprocessed in order to obtain aggregates while no preprocessing is needed when aggregates are not required. Therefore, this research discusses the existing techniques in terms of preprocessed and unprocessed soft sets. The currently available approaches in the preprocessed category recalculate partial missing data from aggregates, yet are unable to use the set of aggregates for recalculating entire values. This research presents a mathematical technique capable of recalculating overall missing values from available aggregates.
Also investigated are the techniques belonging to the unprocessed category, among them being DFIS, a novel data filling approach for an incomplete soft set, which seems to be the most suitable technique in handling incomplete soft set data. The result shows that DFIS possesses a persisting accuracy problem in prediction. DFIS predicts missing values through association between parameters, yet makes no distinction between the different associations. Thus, it ignores the role of the strongest association, which in turn results in low accuracy. This research rectifies this particular DFIS issue by using a new prediction technique through strongest association (PSA). The experimental result validates the high accuracy of PSA over DFIS after implementing both techniques in MATLAB and testing for data filling using bench mark data sets. Further, this research applies PSA to online social networks (OSN) and detects a new kind of network community for those nodes that are associated with each other. The new network community is named ‗virtual community‘ and the inter-associated nodes are named ‗prime nodes‘. Researchers have found that the unavailability of complete OSN nodes results in a low accuracy of ranking algorithms. Therefore, this research predicts new links in two OSNs (Facebook and Twitter) data sets through association between prime nodes using PSA. By completing OSNs through association between prime nodes using PSA, this study demonstrates that the performance of famous ranking algorithms (k-Core and PageRank) can be significantly improved
COVID-19: Automatic Detection of the Novel Coronavirus Disease from CT Images Using an Optimized Convolutional Neural Network
No full textIt is widely known that a quick disclosure of the COVID-19 can help to reduce its spread dramatically. Transcriptase polymerase chain reaction could be a more
useful, rapid, and trustworthy technique for the evaluation and classification of the COVID-19 disease. Currently, a computerized method for classifying computed tomography (CT) images of chests can be crucial for speeding up the detection while the COVID-19 epidemic is rapidly spreading. In this article, the authors have proposed an optimized convolutional neural network model (ADECOCNN) to divide infected and not infected patients. Furthermore, the ADECO-CNN approach is compared with pretrained convolutional neural network (CNN)-based VGG19, GoogleNet, and ResNet models. Extensive analysis proved that the ADECO-CNN-optimized CNN model can classify CT images with 99.99% accuracy, 99.96% sensitivity, 99.92%
precision, and 99.97% specificity
Improving the Prediction of Heart Failure Patients’ Survival Using SMOTE and Effective Data Mining Techniques
No full textCardiovascular disease is a substantial cause of mortality and morbidity in the world. In clinical data analytics, it is a great challenge to predict heart disease survivor. Data mining transforms huge amounts of raw data generated by the health industry into useful information that can help in making informed decisions. Various studies proved that significant features play a key role in improving performance of machine learning models. This study analyzes the heart failure survivors from the dataset of 299 patients admitted in hospital. The aim is to find significant features and effective data mining techniques that can boost the accuracy of cardiovascular patient’s survivor prediction. To predict patient’s survival, this study employs nine classification models: Decision Tree (DT), Adaptive boosting classifier (AdaBoost), Logistic Regression (LR), Stochastic Gradient classifier (SGD), Random Forest (RF), Gradient Boosting classifier (GBM), Extra Tree Classifier (ETC), Gaussian Naive Bayes classifier (G-NB) and Support Vector Machine (SVM). The imbalance class problem is handled by Synthetic Minority Oversampling Technique (SMOTE). Furthermore, machine learning models are trained on the highest ranked features selected by RF. The results are compared with those provided by machine learning algorithms using full set of features. Experimental results demonstrate that ETC outperforms other models and achieves 0.9262 accuracy value with SMOTE in prediction of heart patient’s survival.Full Tex
Discrepancy detection between actual user reviews and numeric ratings of Google App store using deep learning
No full textNowadays online reviews play a significant role in influencing the decision of consumers. Consumers show their experience and information about product quality in their reviews. Product Reviews from Amazon to Restaurant Reviews from Yelp are facing problems with fake reviews and fake numeric ratings. Online reviews typically consist of qualitative (text format) and quantitative (rating) formats. In the case of Google Play store fake numeric ratings can play a big role in the success of apps. People tend to believe that a high-star rating may be significantly attached with a good review. However, user star level rating information does not usually match with text format of review. Despite many efforts to resolve this issue, Apple App Store and Google Play Store are still facing this problem. This study proposes a novel Google App numeric reviews & ratings contradiction prediction framework using Deep Learning approaches. The framework consists of two phases. In the first phase, the polarity of reviews are predicted using sentiment analysis tool to build ground truth. In the second phase, star ratings are predicted from text format of reviews after training deep learning models on ground truth obtained in the first phase. Experimental results demonstrate that based on actual user reviews the proposed framework significantly predicts unbiased star rating of app.No Full Tex
Comparative influence of active PLA and PP films on the quality of minimally processed cherry tomatoes
No full textMinimally processed fruits and vegetables (F&V) are highly prone to oxidative deterioration and despite many efforts, no tangible solution has been found. Thus, this study was designed to evaluate the influence of antioxidant-releasing PLA (polylactic acid) and PP (polypropylene) films incorporated with orange peel extract (OPE) on the quality of cherry tomatoes during storage. Films were characterized based on color parameters, barrier properties and potential migration of volatile compounds from packaging into the food systems. The success of OPE encapsulation and molecular interactions between extract and polymeric chains was confirmed by FT-IR. The release analysis was performed in terms of DPPH radical scavenging activity and through GC-MS analysis (through liquid injection and SPME). Finally, the influence of the packaging material on the quality of cherry tomatoes was ascertained through oxidative enzyme activity and the production of volatile organic compounds. The effect of the extract on the oxygen permeability depends by the film. There was a significant difference (p < 0.05) in compounds that migrated from the control and active PLA films as observed through GC-MS. Finally, cherry tomatoes packed with active PLA films displayed more total polyphenolic content (TPC) retention and reduced volatile compounds (i.e., hexanal) at the end of storage as compared to PP films. Thus, active PLA films have the potential to be used as a replacement packaging material to PP for cherry tomatoes
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