INCOME STRUCTURE AND PERFORMANCE: AN EMPIRICAL ANALYSIS OF ISLAMIC AND CONVENTIONAL BANKS IN INDONESIA

Banks have tried to compensate for the decline in their profits due to increased competition by shifting their focus toward non-intermediation activities. This paper assesses the impact of these non-intermediation activities on the profitability and risk of Islamic and conventional banks in Indonesia. We use a system generalized method of moments estimator to control for the simultaneity for all the banks in our sample for the period from 2007 to 2017. Our results suggest that non-intermediation income has a positive impact on bank performance. We find no difference between Islamic and conventional banks in terms of the link between non intermediation income and performance.Banks have tried to compensate for the decline in their profits due to increased competition by shifting their focus toward non-intermediation activities. This paper assesses the impact of these non-intermediation activities on the profitability and risk of Islamic and conventional banks in Indonesia. We use a system generalized method of moments estimator to control for the simultaneity for all the banks in our sample for the period from 2007 to 2017. Our results suggest that non-intermediation income has a positive impact on bank performance. We find no difference between Islamic and conventional banks in terms of the link between non intermediation income and performance

Improving Patient Care with Machine Learning: A Game-Changer for Healthcare

Machine learning has revolutionized the field of healthcare by offering tremendous potential to improve patient care across various domains. This research study aimed to explore the impact of machine learning in healthcare and identify key findings in several areas.Machine learning algorithms demonstrated the ability to detect diseases at an early stage and facilitate accurate diagnoses by analyzing extensive medical data, including patient records, lab results, imaging scans, and genetic information. This capability holds the potential to improve patient outcomes and increase survival rates.The study highlighted that machine learning can generate personalized treatment plans by analyzing individual patient data, considering factors such as medical history, genetic information, and treatment outcomes. This personalized approach enhances treatment effectiveness, reduces adverse events, and contributes to improved patient outcomes.Predictive analytics utilizing machine learning techniques showed promise in patient monitoring by leveraging real-time data such as vital signs, physiological information, and electronic health records. By providing early warnings, healthcare providers can proactively intervene, preventing adverse events and enhancing patient safety.Machine learning played a significant role in precision medicine and drug discovery. By analyzing vast biomedical datasets, including genomics, proteomics, and clinical trial information, machine learning algorithms identified novel drug targets, predicted drug efficacy and toxicity, and optimized treatment regimens. This accelerated drug discovery process holds the potential to provide more effective and personalized treatment options.The study also emphasized the value of machine learning in pharmacovigilance and adverse event detection. By analyzing the FDA Adverse Event Reporting System (FAERS) big data, machine learning algorithms uncovered hidden associations between drugs, medical products, and adverse events, aiding in early detection and monitoring of drug-related safety issues. This finding contributes to improved patient safety and reduced occurrences of adverse events.The research demonstrated the remarkable potential of machine learning in medical imaging analysis. Deep learning algorithms trained on large datasets were able to detect abnormalities in various medical images, facilitating faster and more accurate diagnoses. This technology reduces human error and ultimately leads to improved patient outcomes.While machine learning offers immense benefits, ethical considerations such as patient privacy, algorithm bias, and transparency must be addressed for responsible implementation. Healthcare professionals should remain central to decision-making processes, utilizing machine learning as a tool to enhance their expertise rather than replace it. This study showcases the transformative potential of machine learning in revolutionizing healthcare and improving patient care

Empirical Investigation of Key Factors for SaaS Architecture Dimension

IEEE Software-as-a-Service (SaaS) has received significant attention from software providers and users as a software delivery model. Most of the existing companies are transferring their business into a SaaS model. This intensely competitive environment has imposed many challenges for SaaS developers and vendors. SaaS development is a very complex process and SaaS success depends on its architecture design and development. This paper provides a better understanding of SaaS applications architecture phase during the SaaS development process. It focuses mainly on an empirical investigation of key factors of SaaS Architecture phase identified from the systematic literature review. A quantitative survey was developed and conducted to identify key architecture factors for an improved and successful SaaS application. A developed survey was used to test the proposed hypothesis presented in this study. Empirical investigation\u27s results provide evidence that vendors and developers must consider key architecture factors for SaaS development process to stand in the current competitive environment. These key factors include customization, scalability, MTA (Multi-Tenancy Architecture), security, integration, and fault tolerance and recovery management. The main contribution of this paper is to investigate empirically the influence of identified key factors of the architecture on SaaS applications success

Design guidelines for SaaS development process

© 2018 IEEE. A novel and widespread business model in cloud computing is to provide on-demand software as a service (SaaS) over the Internet. The software runs on a server and the user access it through an Internet connection. A single application instance can be shared by multiple users which provide a cost-effective solution to SaaS providers. Varying requirements from multiple users increase complexity in SaaS application design. The success of SaaS depends on its design. SaaS is different than traditional web-based application, so traditional application design model cannot full fill many SaaS specific design requirements. This paper provides a better understanding of key design factors in SaaS development process which results in a successful SaaS product following an improved design process. This study identifies key design factors through literature review and provides guidelines for key design factors on the SaaS application development. Ultimately, it will be beneficial for SaaS developers to improve the SaaS application development process and have a positive impact on the final product

Neuroradiology in tuberculous meningitisdiagnostic significance and prognostic value.

Tuberculous meningitis (TBM) is the most common and belligerent form of CNS TB.Prompt definitive diagnosis of TBM is arduous due to tedious microbiological procedures. This study was conducted to evaluate the neuroradiological findings in patients with TBM as a modality forearly diagnoses and predicting prognosis. Materials and methods: A successive series of 100 patients diagnosed with TBM admitted to the PIMS neurology ward were studied between March 2013 and April 2014. Cranial imaging results were obtained by non-contrast enhanced CT brain (NECT) and MRI (magnetic resonance imaging) brain with contrast. MRC staging on admission and in-hospital mortality were recorded.Results: The mean age was 34.86 ± 1.76 years with a female preponderance (55%, 55 out of 100). On admission, 72% were in MRC stages II or III. The in-hospital mortality was 16%. NECT was obtained in all the patients and was abnormal in 67% of the patients with hydrocephalus (58%), edema cerebral (24%) and infarcts (5%) being the commonestfindings.CT infarct had the highest mortality rate of 60%. MRI was obtained in 61% of the patients and was abnormal in 88.5% of these cases. Hydrocephalus (61%), tuberculomas (54%), leptomeningeal involvement (46%) and infarcts (13%) were the most frequent radiological signs on MRI. Mortality was significantly associated with infarcts but not with tuberculomas.Conclusion: Neuroimaging techniques are a handy tool in the early diagnosis of TBM. MRI is particularly helpful in defining findings such as infarcts and tuberculomas and in predicting mortality and morbidity

A New Approach to Information Extraction in User-Centric E-Recruitment Systems

In modern society, people are heavily reliant on information available online through various channels, such as websites, social media, and web portals. Examples include searching for product prices, news, weather, and jobs. This paper focuses on an area of information extraction in e-recruitment, or job searching, which is increasingly used by a large population of users in across the world. Given the enormous volume of information related to job descriptions and users’ profiles, it is complicated to appropriately match a user’s profile with a job description, and vice versa. Existing information extraction techniques are unable to extract contextual entities. Thus, they fall short of extracting domain-specific information entities and consequently affect the matching of the user profile with the job description. The work presented in this paper aims to extract entities from job descriptions using a domain-specific dictionary. The extracted information entities are enriched with knowledge using Linked Open Data. Furthermore, job context information is expanded using a job description domain ontology based on the contextual and knowledge information. The proposed approach appropriately matches users’ profiles/queries and job descriptions. The proposed approach is tested using various experiments on data from real life jobs’ portals. The results show that the proposed approach enriches extracted data from job descriptions, and can help users to find more relevant jobs

COMPETITION, DIVERSIFICATION, AND STABILITY IN THE INDONESIAN BANKING SYSTEM

We examine the impact of competition and portfolio diversification on banking stability for conventional and Islamic banks in Indonesia. We find that the Islamic banking sector is less stable, when compared to the conventional banking sector. Competition in the banking sector reduces stability, while diversification enhances it. We find that competition negatively impacts the Islamic banks, but diversification has no impact on these banks. An interesting finding is that competition and diversification complement each other in enhancing the stability of the Indonesian banking sector. These findings carry an important policy implication for the banking sector of Indonesia