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In project planning, traditionally, the project managers first schedule the project activities, and then plan for materials ordering and supplier selection. This disintegrated approach lacks in planning co-ordination and causes a loss in expected profit for the organization. In this study, a concurrent model is proposed for resource constraint project scheduling with materials ordering and suppliers selection problems. The proposed model aims to maximize overall net present value for the organization considering ordering cost, procurement cost, materials holding cost, and the project’s deadline penalty cost. The resource constraint project scheduling with materials ordering and suppliers selection is an NP-hard problem. Thus, a memetic algorithm hybridizing the genetic algorithm with a forward-backward improvement based local search is proposed to solve the proposed model. The proposed algorithm is tested on self-generated 120 instances varied from 30 to 60 activity projects with 4 to 8 suppliers. Experimental results show that the proposed genetic algorithm-based memetic algorithm approach generates better solutions than the standalone genetic algorithm. The concurrent project scheduling with materials ordering and supplier selection approach and solution methods have a significant implication for the managers to complete the project in an economic and timely manner.

Resource constrained project scheduling problems (RCPSPs) are complex optimization problems that aim to minimize project completion time after considering limited resources and precedence-related activities with known durations. However, due to the dynamic nature of real-world applications, activity durations are vulnerable to change. In addition, resource demands along with resource availability at any stage of a project can also vary due to disruptions, which compel practitioners to re-think existing scheduling methodologies. Consequently, to mitigate the conjoint effect of those uncertainties and/or disruptions, this paper proposes a real-time reactive scheduling approach. To deal with uncertain activity durations, a chance constrained based approach is followed, which is later solved by an advanced meta-heuristic based approaches called IGFBIS and IGFBID. After solving an exhaustive list of stochastic RCPSP instances, this paper proves the efficacy of the proposed approaches, which is proven to be more useful to mitigate uncertainty or disruption while executing real-life projects.

Purpose Optimizing material handling within the factory is one of the key problems of modern assembly line systems. The purpose of this paper is to focus on simultaneously balancing a robotic assembly line and the scheduling of material handling required for the operation of such a system, a topic that has received limited attention in academia. Manufacturing industries focus on full autonomy because of the rapid advancements in different elements of Industry 4.0 such as the internet of things, big data and cloud computing. In smart assembly systems, this autonomy aims at the integration of automated material handling equipment such as automated guided vehicles (AGVs) to robotic assembly line systems to ensure a reliable and flexible production system. Design/methodology/approach This paper tackles the problem of designing a balanced robotic assembly line and the scheduling of AGVs to feed materials to these lines such that the cycle time and total tardiness of the assembly system are minimized. Because of the combination of two well-known complex problems such as line balancing and material handling and a heuristic- and metaheuristic-based integrated decision approach is proposed. Findings A detailed computational study demonstrates how an integrated decision approach can serve as an efficient managerial tool in designing/redesigning assembly line systems and support automated transportation infrastructure. Originality/value This study is beneficial for production managers in understanding the main decisional steps involved in the designing/redesigning of smart assembly systems and providing guidelines in decision-making. Moreover, this study explores the material distribution scheduling problems in assembly systems, which is not yet comprehensively explored in the literature.

This paper provides a methodological overview of service supply chain research through a comprehensive review of published literature, enabling us to describe the service supply chain from a knowle...

Text-type detection of a document is an essential pre-processing step in the implementations of many document-processing solutions, such as optical character recognition (OCR) and machine translation. Specifically, text-type detection research for Bangla is very rare, with only a handful of solutions ever reported in the literature. In this paper, we present a lightweight, small footprint convolutional neural network, which detects handwritten and printed types of content directly from scanned mixed-type document images. The proposed model achieves 99.82% recognition accuracy for this specific two-type classification problem.

Font detection is an essential pre-processing step for printed character recognition. In this era of computerization and automation, computer composed documents such as official documents, bank checks, loan applications, visiting cards, invitation cards, educational materials are used everywhere. Beyond just editing and processing documents, converting documents from one format to another, such as an invitation card, billboards is another major application area where a designer has to recognize the font details from the images. There is a lot of re-search on automatic font detection published for high-resource languages such as English. Still, not much has been reported for a low resource language such as Bangla. Bangla has a complex structure because of the use of diacritics, compound characters and graphemes. Furthermore, because of the popularity of digital, online publications, there has been a recent surge of fonts in Bangla. Font detection can also help analysts to detect changes in font choices based on sociopolitical divides: for example, consider that fonts common in Bangladesh may not be as popular among Bangla publications in India. In this paper, we present a convolutional neural network (CNN) approach for detecting Bangla fonts, using a space adjustment method dependent on a stacked convolutional auto-encoder (SCAE). As part of the work, we built a large corpus of printed documents consisting of 12,187 images in 7 different Bangla fonts, forming a total of 77,728 samples by augmentations to train and validate our model. Our proposed model achieves 98.73% average font recognition accuracy in the validation set.

We present a balanced and 100% machine annotated Bangla OCR corpus of nearly eight and a half million characters. This is a “first-of-its-kind” effort for the Bangla language. Although Bangla is a top five most frequently used language in the world, it is still considered to be a resource-poor language since even the most basic language processing tools are not available. This corpus goes a long way in mitigating this shortcoming. Also, it is important to mention that this is a continuation of our previous work in building a synthetic corpus for training Bangla OCRs and is an intermediate step in our effort to build a true gold-standard corpus for Bangla OCRs—our ultimate goal. The paper not only discusses the corpus, but also the entire processing pipeline—from scanning pages to identifying lines to segmenting words and finally characters—which are then annotated using a homegrown OCR. We also present a comprehensive corpus characteristics and specification review—demonstrating that we have built a corpus which is very nearly balanced—a very desirable characteristic of any corpus design.

Latin has historically led the state-of-the-art in handwritten optical character recognition (OCR) research. Adapting existing systems from Latin to alpha-syllabary languages is particularly challenging due to a sharp contrast between their orthographies. Due to a cursive writing system and frequent use of diacritics, the segmentation and/or alignment of graphical constituents with corresponding characters becomes significantly convoluted. We propose a labeling scheme based on graphemes (linguistic segments of word formation) that makes segmentation inside alpha-syllabary words linear and present the first dataset of Bengali handwritten graphemes that are commonly used in everyday context. The dataset contains 411k curated samples of \( 1295 \) unique commonly used Bengali graphemes. Additionally, the test set contains \(900 \) uncommon Bengali graphemes for out of dictionary performance evaluation. The dataset is open-sourced as a part of a public Handwritten Grapheme Classification Challenge on Kaggle to benchmark vision algorithms for multi-target grapheme classification. The unique graphemes present in this dataset are selected based on commonality in the Google Bengali ASR corpus. From competition proceedings, we see that deep learning methods can generalize to a large span of out of dictionary graphemes which are absent during training (Kaggle Competition kaggle.com/c/bengaliai-cv19, Supplementary materials and Appendix https://github.com/AhmedImtiazPrio/ICDAR2021supplementary).

The coronavirus disease 2019 (COVID-19) pandemic has revealed deep gaps in our understanding of the clinical nuances of this extremely infectious viral pathogen. In order for public health, care delivery systems, clinicians, and other stakeholders to be better prepared for the next wave of SARS-CoV-2 infections, which, at this point, seems inevitable, we need to better understand this disease-not only from a clinical diagnosis and treatment perspective-but also from a forecasting, planning, and advanced preparedness point of view. To predict the onset and outcomes of a next wave, we first need to understand the pathologic mechanisms and features of COVID-19 from the point of view of the intricacies of clinical presentation, to the nuances of response to therapy. Here, we present a novel approach to model COVID-19, utilizing patient data from related diseases, combining clinical understanding with artificial intelligence modeling. Our process will serve as a methodology for analysis of the data being collected in the ASAIO database and other data sources worldwide.

This paper presents a targeted, machine learning based solution to model the phenomenon known as the ‘cytokine storm,’ which is suspected to play a major role in explaining the highly variable severity of COVID-19 among patients. It describes how a Natural Language Processing (NLP) approach, augmented by biomedical knowledge databases, can extract pre-existing conditions and relevant clinical markers from Electronic Health Records (EHRs). These extracted variables can be modeled to demonstrate correlation with the severity of infection outcomes, the building blocks of a comprehensive risk assessment and stratification strategy to predict which patients have higher or lower risks in terms of the disease severity and likelihood of hospitalization, exclusively from insights taken from the natural language data. The model has been applied to a cohort of patients from a large database of real, anonymized patients and has displayed demonstrable results.

Bangla is one of the world’s most widely-spoken languages, but few languages (or "script") automation solutions have been reported for it. To build an OCR system, it is very important to detect the language and type of printing style to run specific character recognition and segmentation modules. This paper presents a novel solution to automatically detect the language (Bangla vs English in terms of the script), and printing style (printed vs handwritten) from any given bilingual scanned document using multiple deep learning models.

Tracking sentiments is a critical task in many natural language processing applications. A lot of work has been done on many leading languages in the world, such as English. However, in many languages such as Bangla, sentiment analysis is still in early development. Most of the research on this topic suffers from three key issues: (a) the lack of standardized publicly available datasets, (b) the subjectivity of the reported results, which generally manifests as a lack of agreement on core sentiment categorizations, and finally, (c) the lack of an established framework where these efforts can be compared to a formal benchmark. Thus, this seems to be an opportune moment to establish a benchmark for sentiment analysis in Bangla. With that goal in mind, this paper presents benchmark results of ten different sentiment analysis solutions on three publicly available Bangla sentiment analysis corpora. As part of the benchmarking process, we have optimized these algorithms for the task at hand. Finally, we establish and present sixteen different evaluation matrices for benchmarking these algorithms. We hope that this paper will jumpstart an open and transparent benchmarking process, one that we plan to update every two years, to help validating newer and novel algorithms that will be reported in this area in future.

Bangla is the fifth most-spoken native language in the world. Despite having such a large number of speakers, the resources related to development of language processing solutions are very limited. To realize the full potential of Machine Learning (ML) and Artificial Intelligence (AI) solutions for computer vision and Natural Language Processing (NLP), a complete and standardized fully-annotated corpus is an essential prerequisite. Specifically, development of Optical Character Recognition systems (OCRs) for printed characters, an important resource for language automatic and digitization, requires a large corpus with high coverage and variability of fonts, representing the nuances of the language usage, which does not exist for Bangla. In this paper, we present a novel synthetic corpus of over 5 million printed Bangla characters containing 60 alphanumeric characters, 10 vowel modifiers, 159 compound characters, which corresponds to 229 different classes of both Unicode and ASCII encodings. This is entirely novel work, since there exists no such corpus currently for the Bangla language.

Handwriting recognition is still not a solved problem. With the advancements in artificial intelligence and machine learning, the construction of Optical Character Recognition systems (OCRs) has become more effective. However, there is still no serious commercially available OCRs for many low-resource languages, such as Bangla. Bangla presents additional challenges, since oftentimes, the vowels and consonants in the middle of the words are abbreviated and replaced with notations called diacritics, and multiple letters can be combined to build shorthand representations, called compound characters. Furthermore, the compound characters can have diacritics as well, making the recognition task extremely complex. This means that a successful commercial OCR should not only model individual characters but also model these diacritics and combined characters, leading us to propose a grapheme-based holistic recognition approach. Borno is the first multiclass convolutional neural network-based deep learning model that can recognize Bangla handwritten characters with graphemes. The proposed model has been trained on a dataset of 1,069,132 images, with 50 basic characters, 10 numerals, 146 compound characters, 10 modifiers, and 6 consonant diacritics classes. The trained Borno model achieves a 92.61% average character recognition accuracy in the validation set.

During a pandemic, such as COVID-19, the scientific community must optimize collaboration, as part of the race against time to identify and repurpose existing treatments. Today, Artificial Intelligence (AI) offers us a significant opportunity to generate insights and provide predictive models that could substantially improve the opportunities for understanding the core metrics that characterize the epidemic. A principal barrier for effective AI models in a collaborative environment, especially in the medical and pharmaceutical industries, is dealing with datasets that are distributed across multiple organizations, as traditional AI models rely on the datasets being in one location. In the status quo, organizations must slog through a costly and time-consuming process of extract-transform-loading to build a dataset in a singular location. This paper addresses how Federated Learning may be applied to facilitate flexible AI models that have been trained on biopharma and clinical unstructured data, with a special focus on extracting actionable intelligence from existing research and communications via Natural Language Processing (NLP).

The purpose of this study is to provide empirical evidence of the effect of non profit sharing finance, profit sharing finance and credit risk on financial performance with the integration of intellectual capital as a moderating variable. The population is all Islamic banking registered in OJK for the period 2015-2018 with a total of 192 observations. These are analyzed using hierarchical regression techniques, multiple linear regression tests and moderated regression analysis (MRA) tests, with e-views 9 software. The results show that non profit sharing financing, profit sharing financing and intellectual capital have a positive and significant effect to financial performance on Islamic banking. While credit risk has a significant negative effect to financial performance of Islamic banking. Furthermore showed that intellectual capital significantly moderates the relationship of non profit sharing finance and profit sharing finance to financial performance Islamic banks. However intellectual capital can’t be moderates the relationship of credit risk and financial performance.

The rise of artificial intelligence technology along with machine and deep learning are opening up almost limitless possibilities. In recent years, application-based researchers in machine learning and deep learning have started developing solutions for many practical problems. Handwriting recognition is one such area of interest. Bangla, being the seventh most spoken language in the world, is not an exception. However, unlike English, there have not been concerted formal attempts in building a benchmark in comparing the different approaches reported in the literature, mainly because of the lack of openly and freely available datasets and diversity of the approaches without formal comparative studies. In this research paper, we seek to rectify this gap. We have focused on benchmarking five robust algorithms: K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), on all publicly available Bangla handwriting digits datasets, including Ekush, NumtaDB, CMARTdb, and BDRW. NumtaDB itself is a collection of five handwriting datasets. We have worked on fine-tuning these algorithms by finding the best possible hyper-parameters of these algorithms. It is our hope that Sankhya will work as a beginning point of an open and verifiable benchmarking process that we plan to repeat every two years for now on and set a standard for testing and validating newer and novel algorithms that will be reported in this area in the future. In addition, we have extensively compared our research with other states of the art research and our versions of these algorithms are now outperforming every reported result on these datasets.All the datasets we used are open-sourced. In addition, we are making the.csv version of these datasets available in public GitHub. Of all the models we tested, the Sankhya CNN model performed the best for all these datasets, which we fine-tuned specifically for Bangla character recognition. We are making this CNN model available in public GitHub.

This work focuses on creating a smart Electronic Health Record (EHR) platform to collect and analyze doctor-patient interactions. It is an often cited fact that doctors are spending less and less time with patients, but two recent surveys have quantified what these numbers really are and the results are disturbing. In one survey [1], it was found that during the office day, physicians spent 27.0% of their total time on direct clinical face time with patients and 49.2% of their time on EHR and deskwork. In another survey [2], results “suggest that the physicians logged an average of 3.08 hours on office visits and 3.17 hours on desktop medicine each day,… Over time, log records from physicians showed a decline in the time allocated to face-to-face visits, accompanied by an increase in time allocated to desktop medicine It is precisely this problem that the University of Arizona’s “Wired Room” project seeks to address.

This paper presents a Bangla corpus specifically targeted for sentiment analysis and made available to researchers under an open-source licensing scheme1. We have collected and manually annotated over 10,000 sentences with sentiment polarity. We then moved to the Word domain and annotated over 15,000 words derived from these sentences with sentiment polarity. Each entry is the corpus has been cross-annotated by at least two and sometimes three annotators for ensuring quality. Also as a pre-requisite of creating a high quality sentiment analysis corpus, we had to build a secondary corpus for Bangla word stemming, which is also been cross-validated by at least two and sometimes three annotators for ensuring quality.

Project scheduling (PS) and supply chain management (SCM) are two broad areas that have attracted considerable research attention in recent years. However, the integration of SCM and PS has so far attracted little attention, which motivates this research to include supplier selection strategy in the integrated PS network. Primarily, the project attributes are considered as analogous to the very well-known resource constrained project scheduling problem (RCPSP), which are later integrated with simulated supplier data. After designing mathematical formulations for that integrated model, a genetic algorithm based memetic algorithm (MA) approach is proposed with the objective of minimizing supply chain backward (from supplier to project completion) cost. The proposed algorithm incorporates Nawaz, Enscore, and Ham (NEH) heuristic-based initialization with carefully designed crossover and local search techniques. An online case study model with simulated supplier attributes is considered and solved efficiently to justify the proposed approach. Finally, validation of the proposed MA has been carried out through comparison with a selected state-of-the-art algorithm.

The resource constraint project scheduling problem (RCPSP) is one of the complex combinatorial optimization problems. Several approaches have been proposed over the decades to solve RCPSPs optimally within reasonable computational times. In this paper, a genetic algorithm based memetic algorithm (MA) is proposed to solve RCPSP with makespan minimization as the objective. The proposed algorithm incorporates Nawaz, Enscore, and Ham (NEH) heuristic-based initialization with carefully designed crossover and local search techniques. The performance of the proposed approach is evaluated by solving 1560 problem instances from the popular project scheduling library (PSPLIB) and the results for different datasets have been compared with selected state-of-the-art algorithms. The comparison demonstrates the satisfactory performance of the proposed approach. Extensive experiments reveal that the proposed algorithm is very simple to implement and highly effective when compared to state-of-the-art methods.

Integration of project scheduling (PS) with materials ordering has received greater attention in the last three decades as an approach to ensure the profitability of a project. The fundamental concern of the material ordering integrated PS is to select the right supplier of the right material by placing an order at the right time so that the ordering, purchasing, and holding cost of the materials are minimized which finally maximizes the project’s profitability. This study proposes a mathematical model and solution approach for a resource constrained project scheduling and material ordering problem with discounted cash flows (RCPS-MOP-DC). The mathematical model for this proposed RCPS-MOP-DC considers decisions regarding materials ordering, supplier selection, transportation and inventory of the raw materials. A mixed integer programming (MIP) model has been proposed for this RCPS-MOP-DC with the objective to maximize the project’s net present value (NPV). A meta-heuristic approach by hybridizing genetic algorithm (GA) and immune algorithm (IA) is proposed as a potential solution approach for this RCPS-MOP-DC model. Performance of this hybridized GA and IA (IGA) approach is compared and contrasted with its constituent algorithms (GA and IA) to validate the effectiveness of this hybridization. Performance of the IGA is further improved by applying a forward–backward improvement (FBI) based local search technique. A restart mechanism is also incorporated in the algorithms which ensures diversity and helps to avoid becoming trapped in local optima. The Taguchi Design of Experiment (DOE) is used to investigate the impact of various parameters and to determine the appropriate parameter sets for the proposed algorithms. The performance of this proposed solution approach has been tested on varied self-generated RCPS-MOP-DC instances ranging from 30 to 120 activities. The results show that the hybrid IGA outperforms GA and IA in terms of the project’s NPV.

The Resource Constrained Project Scheduling Problem (RCPSP) is a combinatorial optimization problem which is non-deterministic polynomial-time (NP)-hard in nature. Due to the diversified applications of RCPSPs, they have been commonly used as scheduling procedures in real-world problems. Since, in practice, project data are prone to changes or disruptions, this paper introduces a mathematical model for a reactive scheduling approach, called the Event Based Reactive Approach (EBRA). This proposed EBRA approach is employed to examine its recovery performance under both a single disruption and a series of independent resource disruptions. Several simulated disruption data are hypothesized to represent real-world disruption scenarios and, without loss of generality, the proposed reactive approach is proved to be efficient in reducing the number of variables and computational complexity and also to be resilient in realistic changes, such as duration inflation and dynamic resource usages. Along with employing an exact method by LINGO software, this paper also proposes an enhanced iterated greedy (EnIG) approach to meta-heuristically solve larger and computationally expensive benchmark instances taken from the Project Scheduling Library (PSPLIB).

The resource constrained project scheduling problem (RCPSP) has a wide variety of practical applications in construction, manufacturing, project planning, and other areas. Since the 1960s many optimization algorithms have been proposed to solve this NP-hard problem, and their performances are evaluated in well-known test problems with different complexities. Although it is desirable to find an algorithm which can provide promising solutions with reasonable computational efforts for any problem under consideration, no single algorithm can meet that condition. To deal with this challenge, we present a genetic algorithm based memetic algorithm (MA) for solving RCPSP. The algorithm is initiated by a critical path-based heuristic and a variant of the Nawaz, Enscore, and Ham (NEH) heuristic. The algorithm involves a similar block order crossover and a variable insertion based local search. An automatic restart scheme is also presented which assists the algorithm to escape from local optima. In addition, a design-of-experiment (DOE) method is used to determine the set of suitable parameters for the proposed MA. Numerical results, statistical analysis and comparisons with state-of-the-art algorithms demonstrate the effectiveness of the proposed approach.

The objective of this literature study is to explore factors affecting voluntary Public Accounting Firm (PAF) switching according to contract theory associated and agency theory of Watts & Zimmerman (1986). PAF switching may occur due to regulations that require a company to do PAF switching, called as mandatory replacement, and due to the company’s own wishes, called as voluntary PAF switching. The result of this study shows that 23.9% of companies voluntarily change their PAF because of managerial changes, financial distress, PAF Size, changes on Return on Assets, client size, and auditor’s opinion. Contracting theory may explain researches in the field of auditing practices, such as auditor or PAF, related to auditor’s reputation, professionalism, auditor environment, PAF size, and industry specialization. Large PAF has a brand image of having a better reputation than small PAF [36]. This is also supported by De Angelo (1981), who stated that large PAFs are preferred by clients because they are considered to be more independent than small PAFs. Agency Theory is often used in research in the field of auditing because information asymmetry is information gap between the interests of agents and principals. Therefore, an independent third party is needed, and the role of the auditor is only as the monitoring party. Keywords: contracting theory, agency theory, Public Accounting Firm switching

The development of power generation technology enables the location of the plant which is no longer centralized but becomes scattered in several distribution channel locations close to the load. The interconnection of PV into the distribution network may result in potential disruption of operating conditions and result in problems in power quality. The hosting capacity is defined as the number of new production or consumption that can be interconnected with the network without any adverse impact on the reliability or quality of the voltage on the consumer. The hosting capacity also ensures that there are no broken parameters, i.e., the current carrying capacity distribution network and power flow direction. An iteration approach with voltage constraints, power flow, and current conductivity capability is used to determine the amount of PV power capacity that can be injected into the network. The impact of PV interconnection into the network, especially the voltage and power flow is studied to determine the capacity of PV hosting. The results show that the PV hosting capacity of the distribution network in Yogyakarta through GI Kentungan is equal to the current load on the distribution network.

This research purpose is to test empirically and analyze the influence of zakat, qardh, murabahah, mudharabah, musyarakah, and corporate social responsibility (CSR) towards financial performance (ROA) Commercial Bank Syariah in Indonesia in 2012-2016 period of time. The analyzing technique used double linear regression. Research population is Syariah Commercial Bank listed in Financial Service Authority (in Indonesia called OJK) year 2012-2016. The sample taken by using purposive sampling, within the Syariah Commercial Bank criteria that showed the financial report in 2012-2016 period. The data taken based on publication that conducted by the enlisted Banks. Thus, the number of taken samples is 10 out of 13 Syariah Commercial Bank in Indonesia. The research result showed that zakat, qardh, and murabahah does not affect the Syariah Commercial Bank financial performance. Mudharabah and corporate social responsibility (CSR) showed significant negative influence towards Syariah Commercial Bank financial performance, while musyarakah affects significantly positive toward Syariah Commercial Bank financial performance in Indonesia.

Purpose – The purpose of this study is to examine the association between the financial expertise of the chief financial officer (CFO) and concerns about corporate governance. Design/methodology/approach – Consistent with prior research, the authors used four variables, including certified public accountant (CPA) certification, Master of Business Administration degree, age of CFO and length of CFO tenure, to measure CFO’s financial expertise. The authors hypothesize a negative association between CFO expertise and concerns about corporate governance. Findings – Regression analysis revealed that the CPA certification is negatively associated with governance concerns at a significant level. The results suggest that stakeholders show less concerns about a company’s corporate governance mechanism when the CFO has a CPA certification. In particular, the results support the recommendation by the American Institute of Certified Public Accountants that a CFO of a public firm should have a CPA certification. Originality/value – The study is important in the following ways. First, the study delivers new evidence on the link between CFO financial expertise and corporate governance. This contributes to the CFO financial expertise literature and the corporate governance literature. Second, according to Standard and Poor’s, equity index investing has grown more popular over the past 30 years. The study delivers useful information to index investors who invest in S & P SmallCap 600 Index. Third, regulators have put a large amount of resources to discover ways to strengthen firms’ corporate governance. Thus, the results should be of interest to policy makers who design and implement guidelines on corporate governance mechanisms.

This paper aims to identify an efficient priority rule (PR) for resource constrained project scheduling problems under dynamic environments. Here, the dynamicity comes from the unstable resource availability, time-varying resource demands and stochastic activity durations for each of the project activities. This extended dynamic project scheduling problem is solved by using a proposed Variable Neighbourhood Search Based Local Search Heuristic (VNSLSH). Six PRs are then integrated with the proposed heuristic to identify the best rule. Standard sets of 5760 and 7200 generated test instances with 30 and 120 activities are considered for experimentation. The influence of the variation of activity durations and risk or uncertainty levels on the algorithm’s performance is analysed and discussed. After analysing several PR-based heuristics, this study concludes that no one of the taken PR is dominant over others under this dynamic project scheduling environment.

DOI: http://dx.doi.org/10.3329/bmj.v41i3.18963Bangladesh Medical Journal 2012 Vol.41(3): 59-63

Herein we present a novel big-data framework for healthcare applications. Healthcare data is well suited for bigdata processing and analytics because of the variety, veracity and volume of these types of data. In recent times, many areas within healthcare have been identified that can directly benefit from such treatment. However, setting up these types of architecture is not trivial. We present a novel approach of building a big-data framework that can be adapted to various healthcare applications with relative use, making this a one-stop “Big-Data-Healthcare-in-a-Box”.

Photovoltaic cell technology has been developed to meet the target of 17% Renewable Energy in 2025 accordance with Indonesia Government Regulation No. 5 2006. Photovoltaic cells are made of semiconductor materials, namely silicon or germanium (p-n junction). These cells need the light that comes from solar irradiation which brings energy photons to convert light energy into electrical energy. It is different from the solar heater that requires heat energy or thermal of sunlight that is normally used for drying or heating water. Photovoltaic cells requires energy photons to perform the energy conversion process, the photon energy can be derived from sunlight. Energy photon is taken from the sun light along with the advent of heat due to black-body radiation, which can lead to temperature increments of photovoltaic cells. Increment of 1°C can decreased photovoltaic cell voltage of up to 2.3 mV per cell. In this research, it will be discuss the analysis of the effect of rising temperatures and variations of irradiation on the type monocrystalline photovoltaic. Those variation are analyzed, simulated and experiment by using a module of experiment. The test results show that increment temperature from 25° C to 80° C at cell of photovoltaic decrease the output voltage of the photovoltaic cell at 4.21 V, and it also affect the power output of the cell which decreases up to 0.7523 Watt. In addition, the bigger the value of irradiation received by cell at amount of 1000 W / m2, produce more output power cells at the same temperature.

This cross sectional study was carried out on 101 cases of vesico-vaginal fistula who were admitted and operated in Kumudini Women's Medical College Hospital. The aim of the study was to find out the outcome of vesicovaginal fistula repair. A detailed interview was taken from the patients and attendants by using a questionnaire. Necessary information were collected from the patient's record sheets . Small, medium and large sized fistulae were 59.41%, 26.73% and 11.88% respectively. The result of the study showed that success of fistula repair was 73.27%, though seven cases developed stress incontinence. Totally 27 patients were unsuccessfully repaired. Of which 19 (70.37%) were dropped out, 5(18.52%) were referred and 3(11.11%) had operative failure. Post-operative recovery was mostly (87.13%) uneventful. Urethral leakage (6.93%), blocked catheter ( 2.97%) and UTI (2.97%) were the leading complications. To improve the outcome, more training and skill of surgeons, improvised per-operative and post-operative care is necessary. Approaches should be designed to make the incidence of drop out to zero. DOI: http://dx.doi.org/10.3329/bmj.v40i3.18652 Bangladesh Medical Journal 2011 Vol.40(3):18-20

Big-Data has opened up enormous opportunities for a variety of industries. Our ability to process large amounts of data, particularly a plethora of types of data, within a unified processing platform has been extremely useful in pushing the boundaries of what can be achieved from our computational models and how we can apply that to solve real life problems. This paper enumerates the main areas where Big-Data has been applied so far and points out areas within healthcare analytics where the same level of success can be gained through adoption of this technology.

This research is aimed to: (1) Examine the effect of environmental uncertainty and decentralization on management accounting system (MAS) information characteristic of broad scope and aggregation, (2) Examine the effect of availability MAS information characteristic of broad scope and aggregation on managerial performance.The respondents are branch managers from 16 national banking which has spread in 21 big cities in Indonesia. Through mail survey, it is acquired 93 respondents which is put into data tabulation. The data analysis is done by using structural equation modeling with AMOS 4.0 programThe result of research shows 1hat availability of MAS information characteristic of broad scope and aggregation is needed in environmental uncertainty condition and decentralization organizalion struclure . The availability of information characteristic is also concluded that it can increase managerial performance.

This paper introduces a novel networking strategy for RAM-based neurons which significantly improves the training and recognition performance of such networks whilst maintaining the generalization capabilities achieved in previous network configurations. A number of different architectures are introduced, each using the same underlying principles. Initially, features which are common to all architectures are described, illustrating the basis of the underlying paradigm. Three architectures are then introduced illustrating different techniques of employing the paradigm to meet differing performance specifications. The architectures are described in terms of the structure of the neurons they employ. Details of the various training and recognition algorithms employed by the architectures are supplied in order to present a complete description of the operation of this class of artificial neural network.

Zakah is one of the religious orders that must be complied by every Muslim who had the capacity to do so. The level of such complience was affected by the faith factor. However, if payment of zakah was obliged through the formal institution of zakah collector, as that was applied in several islamic countries, then the zakah complience is now becomes the obedience against the formal institution of zakah collector. Therefore, the management of zakah institution, collecting efficiency and several other factors that were connected with the services given by zakah institution influenced the level of zakah complience. The study aimed at forming the zakah complience model of the formal institution of zakah collector. The model was formed based on the tax compliance model by adding several important factors that influenced the obedience of zakah payment. The availability of zakah complience model will benefit to facilitate research concerning the empirically test of zakah complience and evasion to the formal institution of zakah collector.

This paper introduces a document clustering method within a commercial document repository, FileShare(R). FileShare(R) is a commercial collaborative digital library offering facilities for sharing and accessing documents over a simple Internet browser (e.g. Microsoft(R) Internet Explorer(R), Netscape(R) or Opera(R)) within groups of people working on common projects. As the number of documents increases within a digital library, displaying these documents in this environment poses a huge challenge. This paper proposes a document clustering method that uses a modified version of the traditional K-Means algorithm to categorize documents by their themes using lexical chaining within the FileShare(R) repository. The proposed algorithm is unsupervised, and has shown very high accuracy in a typical experimental setup.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

This paper presents a novel automatic web publishing solution, Pageview(R). PageView(R) is a complete working solution for document processing and management. The principal aim of this tool is to allow workgroups to share, access and publish documents on-line on a regular basis. For example, assuming that a person is working on some documents. The user will, in some fashion, organize his work either in his own local directory or in a shared network drive. Now extend that concept to a workgroup. Within a workgroup, some users are working together on some documents, and they are saving them in a directory structure somewhere on a document repository. The next stage of this reasoning is that a workgroup is working on some documents, and they want to publish them routinely on-line. Now it may happen that they are using different editing tools, different software, and different graphics tools. The resultant documents may be in PDF, Microsoft Office(R), HTML, or Word Perfect format, just to name a few. In general, this process needs the documents to be processed in a fashion so that they are in the HTML format, and then a web designer needs to work on that collection to make them available on-line. PageView(R) takes care of this whole process automatically, making the document workflow clean and easy to follow. PageView(R) Server publishes documents, complete with the directory structure, for online use. The documents are automatically converted to HTML and PDF so that users can view the content without downloading the original files, or having to download browser plug-ins. Once published, other users can access the documents as if they are accessing them from their local folders. The paper will describe the complete working system and will discuss possible applications within the document management research.© (2006) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Summarization of web pages is a very interesting topicfrom both academic and commercial point of view.Academically, it is challenging to create a summary of adocument (e.g. a web page) that is highly structured andhas multi-media components in it. From the commercialpoint of view, it is advantageous to summarize web pagesto be viewed in small display devices such as PDAs andcell phones. Summarization not only makes web browsingand navigation easier, but it makes browsing faster ascomplete web pages need not be downloaded beforeviewing. In this paper, a novel combination of naturallanguage and non-natural language based summarizationtechniques have been used to automatically generate anintelligent re-authored display of web pages in real time.

The process of summarizing documents is becomingincreasingly important in the light of recent advances indocument creation/distribution technology, and theresulting influx of large numbers of documents in everyday life. This paper presents a document summarizer thatcombines document analysis, structural decomposition,XML representation and lexical chain analysis. Theproposed summarizer is compared to three commerciallyavailable summarizers and it is shown that it produceseither comparable or better summaries overall.

Digital libraries are becoming very important in our daily lives. This itself is a huge field that needs no introduction. On the other hand, document analysis and recognition is also a very well established field that deals with some distinctly different aspects of document processing. But in the broader picture of document storage and manipulation, these two communities are dealing with only part of the puzzle. We present a commercial digital library that allows storage and manipulation of electronic documents. The novelty is that users can upload electronic documents in many formats, and the system automatically converts them to HTML documents so that they are immediately accessible using the standard Web interface. The original layout and styles of the document is preserved in the HTML conversion process. The digital library then allows users to manipulate these documents in many ways. The Hawaii National Library has already installed the system and conducting large scale user testing.

We describe how an ’in house’ classifier can enhance the performance of a commercial ’black box’ classifier using the classic serial multiple classifier combination scheme. It is now acknowledged by the classifier combination community that parallel or hybrid decision fusion algorithms, in general, outperform serial combination schemes. However, classifier combination using techniques that use class labeling, ranking or probability estimators need access to low level information supplied by all of the participating classifiers. Unfortunately, in many commercial applications the classifier is often a’black box’, which implies that it is not possible to manipulate the low level information regarding classification for these classifiers. In many such cases, a serial classifier combination model provides the only practical method to improve classification. In this paper, we present such an application in speech recognition.

We propose a generic multiple classifier system based solely on pairwise classifiers to classify web pages. Web page classification is getting huge attention now because of its use in enhancing the accuracy of search engines and in summarizing web content for small-screen handheld devices. We have used a Support Vector Machine (SVM) as our core pair-wise classifier. The proposed system has produced very encouraging results on the problem web page classification. The proposed solution is totally generic and should be applicable in solving a wide range of multiple class pattern recognition problems.

Optimizing a multiple classi$er configuration to a particular problem domain is alwoys a demanding task, especially when there are multiple variable paramelers that need to be optimized simultaneously. In this paper, a genetic algorithm is used for optimizing a decision fiion framavork. It is demonstrated tho! !he adoprion of !he proposed approach can result in a system, which is completely seljlconfigurable when adapting itself to a newproblem domain.

Automatic detection and recognition offaces is now becoming an important tool in securiQ and suweillance. One of the most promising techniques for recognizing faces is the use of Support Vector Machines (SVMs). However, SVMs are essentially tw-class classifiers or dichotomizers, and applying them IO recognize multiple classes of faces requires post- recogni:ion decision fusion to arrive at the final overall decision. In this paper, we propose such a decision fusion framewrk, and show chat such a pair-nise classification framework can be totally generic irrespective of the chosen classifier. Application o/ this framework to the face recognition problem has resulted in encouraging perl/ormance.