Financial performance of Islamic and conventional banks during and after US sub-prime crisis in Pakistan: a comparative study

Islamic banking system that is based on Shariah principles is considered more resilient to the financial shocks due to its interest free nature. This study is aimed to compare the financial performances and investigate whether Islamic banks are more profitable, liquid, less risky and operationally efficient compared to conventional banks during and after US Sub-prime crisis in Pakistan. The time span used for the study was from 2007 to 2012. Thirteen financial ratios composed of five Islamic and five conventional banks to measure the financial performance in terms of profitability, risk and solvency, liquidity and capital adequacy. Independent sample t-test is used to determine the significance of mean differences of selected ratios. The results of profitability measures indicate that Islamic banks remained less profitable; however, liquidity performances of Islamic banks were better than conventional banks. However, overall operational efficiency measures are not in favour of Islamic banks. The study concluded that conventional banks performed more efficiently and profitably as compared to Islamic banks. The opportunity of future empirical study is recommended at the end of this paper

PENGARUH MEDIA PEMBELAJARAN AUDIO VISUAL TERHADAP KEAKTIFAN BELAJAR SISWA DALAM PEMBELAJARAN EKONOMI (Sub Tema Koperasi Kelas X MIPA 3 di SMAN 6 Bandung)

Judul penelitian ini “Pengaruh Media Pe mbelajaran Audio-Visual Terhadap Keaktifan Belajar Siswa Dalam Pe mbelajaran Ekonomi Sub Te ma Koperasi di Kelas X MIPA 3 SMAN 6 Bandung”. Tujuan dari penelitian ini adalah untuk mengetahui p enerapan media pembelajaran audio- visual dan keaktifan belajar siswa dalam pe mbelajaran ekonomi sub tema koperasi di kelas X MIPA 3 SMAN 6 Bandung serta untuk mengetahui p engaruh media pembelajaran audio- visual terhadap keaktifan belajar siswa dalam pe mbelajaran ekonomi sub tema koperasi di kelas X MIPA 3 SMAN 6 Bandung. Metode yang digunakan dalam penelitian ini adalah asosiatif kausal. Subjek dalam penelitian ini adalah siswa kelas X MIPA 3 SMAN 6 Bandung yang berjumlah 33 siswa. Analisis data yang digunakan adalah analisis verifikatif data melalui perhitungan rata- rata (mean) skor dengan bantuan SPSS release 21.0 for Windows. Hasil penelitian rekapitulasi skor rata- rata tanggapan responden mengenai media pembelajaran audio- visual sebesar 4,13, sedangkan mengenai keaktifan belajar siswa sebesar 4,20, dengan demikian dapat disimpulkan bahwa ta nggapan responden terhadap media pembelajaran audio- visual dan keaktifan belajar “ Sangat Baik”. Berdasarkan analisis data yang telah dilakukan maka diperoleh hasil penelitian pengaruh penerapan media pembelajaran audio- vis ual yaitu koefisien determinasi R Square sebesar 0,607%. Hal ini dinyatakan variabel X mempunyai pengaruh sebesar 60,70 % terhadap variabel Y dan sisanya 40% dipengaruhi faktor lain. Faktor yang memberikan pengaruh kepada variabel Y sebanyak 60,70% disebabkan oleh indikator variabel X berupa fungsi media pembelajaran dan keunggulan media audio- visual. Kesimpulan penelitian dapat diterima, sebagai ak hir penelitian, penulis menyampaikan saran j ika memiliki siswa yang cenderung memiliki karakteristik pasif, guru sebaiknya menggunakan variasi model pembelajaran yang menarik yang dipadukan dengan media pembelajaran audio- visual yaitu pemutaran video yang berkaitan dengan materi pelajaran yang akan disampaikan karena cara ini dapat membuat siswa lebih aktif untuk belajar dan akan meningkatkan hasil belajar siswa. Kata Kunci: Media Pembelajaran Audio-Visual dan Keaktifan Belajar Siswa

Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart's rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients' acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11\%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed method.Comment: 14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journa

Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques

AbstractSentiment mining is a field of text mining to determine the attitude of people about a particular product, topic, politician in newsgroup posts, review sites, comments on facebook posts twitter, etc. There are many issues involved in opinion mining. One important issue is that opinions could be in different languages (English, Urdu, Arabic, etc.). To tackle each language according to its orientation is a challenging task. Most of the research work in sentiment mining has been done in English language. Currently, limited research is being carried out on sentiment classification of other languages like Arabic, Italian, Urdu and Hindi. In this paper, three classification models are used for text classification using Waikato Environment for Knowledge Analysis (WEKA). Opinions written in Roman-Urdu and English are extracted from a blog. These extracted opinions are documented in text files to prepare a training dataset containing 150 positive and 150 negative opinions, as labeled examples. Testing data set is supplied to three different models and the results in each case are analyzed. The results show that Naïve Bayesian outperformed Decision Tree and KNN in terms of more accuracy, precision, recall and F-measure

Financial Performance of Islamic and Conventional Banks During and After US Sub-prime Crisis in Pakistan: A Comparative Study

Islamic banking system that is based on Shariah principles is considered more resilient to the financial shocks due to its interest free nature. This study is aimed to compare the financial performances and investigate whether Islamic banks are more profitable, liquid, less risky and operationally efficient compared to conventional banks during and after US Sub-prime crisis in Pakistan. The time span used for the study was from 2007 to 2012. Thirteen financial ratios composed of five Islamic and five conventional banks to measure the financial performance in terms of profitability, risk and solvency, liquidity and capital adequacy. Independent sample t-test is used to determine the significance of mean differences of selected ratios. The results of profitability measures indicate that Islamic banks remained less profitable; however, liquidity performances of Islamic banks were better than conventional banks. However, overall operational efficiency measures are not in favour of Islamic banks. The study concluded that conventional banks performed more efficiently and profitably as compared to Islamic banks. The opportunity of future empirical study is recommended at the end of this paper

DEVELOPMENT OF A NEW HYBRID MULTI CRITERIA DECISION-MAKING METHOD FOR A CAR SELECTION SCENARIO

Increasing competition in the automobile industry has led to a vast variety of choices when buying a car thus making car selection a tedious task. The objective of this research is to develop a new hybrid multi-criteria decision-making technique, with accuracy greater than that of the already existing methods, in order to help the people in decision-making while buying a car. Hence, considering a broader spectrum, this study aims at easing the process of multi-criteria decision-making problems in different fields. To achieve the objective, seven different alternatives were evaluated with respect to the enlisted evaluation criteria, which were selected after analyzing the secondary data obtained from Pak wheels based on style, fuel economy, price, comfort and performance. These criteria were then analyzed using the proposed Full Consistency Fuzzy TOPSIS method. As the name tells, this method is a unique combination of two techniques. The Full Consistency method is used to calculate the weights of the criteria while the Fuzzy TOPSIS approach is applied to rank the alternatives according to their scores in the selected criteria. The outcomes demonstrate an increase in the consistency ratio of the weight coefficients due to which the ranking of the alternatives by the FCF-TOPSIS is more accurate than the TOPSIS and the Analytical Hierarchy Process. The novelty of the method lies in the fact that this combination has not been used for an alternative selection scenario before. In addition to this, it can be used in various industries where a choice between the available alternatives arises based on a set of evaluation criteria

Configurable data acquisition for cloud-centric IoT

© 2018 ACM. In the era of smart devices, the reliance of intelligence is the abundance of data. This data is heterogeneous in nature and generated by many small-scale sensory devices. With their taskspecific nature, these sensory devices are energy efficient resources with limited computing and storage power. The effective utilization of these devices requires collaborative execution with remote storage and computing power. Internet of Things defines one such cluster formation of these devices which are single-task specific in themselves but achieves higher goals when executed in a collective environment. Data acquisition from these devices is accumulated at scalable resources like cloud where intelligent processes constitute the foundation of smart environments. However, the current implementations of data acquisition for cloud-centric IoT are driven by the APIs of the device manufacturers, resulting in platforms which are less dynamic and configurable for data acquisition. In this paper, we propose our cloud-centric methodology with the focus on configurable data acquisition from IoT. Our methodology decouples communication of data from the strategical usage of the sensory device. Thus, making it more dynamic and applicable for evolutionary smart environments

Multiple Linear Regression-Based Energy-Aware Resource Allocation in the Fog Computing Environment

Fog computing is a promising computing paradigm for time-sensitive Internet of Things (IoT) applications. It helps to process data close to the users, in order to deliver faster processing outcomes than the Cloud; it also helps to reduce network traffic. The computation environment in the Fog computing is highly dynamic and most of the Fog devices are battery powered hence the chances of application failure is high which leads to delaying the application outcome. On the other hand, if we rerun the application in other devices after the failure it will not comply with time-sensitiveness. To solve this problem, we need to run applications in an energy-efficient manner which is a challenging task due to the dynamic nature of Fog computing environment. It is required to schedule application in such a way that the application should not fail due to the unavailability of energy. In this paper, we propose a multiple linear, regression-based resource allocation mechanism to run applications in an energy-aware manner in the Fog computing environment to minimise failures due to energy constraint. Prior works lack of energy-aware application execution considering dynamism of Fog environment. Hence, we propose A multiple linear regression-based approach which can achieve such objectives. We present a sustainable energy-aware framework and algorithm which execute applications in Fog environment in an energy-aware manner. The trade-off between energy-efficient allocation and application execution time has been investigated and shown to have a minimum negative impact on the system for energy-aware allocation. We compared our proposed method with existing approaches. Our proposed approach minimises the delay and processing by 20%, and 17% compared with the existing one. Furthermore, SLA violation decrease by 57% for the proposed energy-aware allocation.Comment: 8 Pages, 9 Figure