Miracle-dz/UniFAR-FTUs huggingface dataset:
Analysis and Comparison of various Methods for Text Detection from Images using MSER Algorithm

— In this paper analysis and comparison of various methods for text detection is carried by using canny edge detection algorithm and MSER based method along with the image enhancement which results in the improved performance in terms of text detection. In addition, we improve current MSERs by developing a contrast enhancement mechanism that enhances region stability of text patterns to remove the blurring caused during the capture of image Lucy Richardson de blurring Algorithm is used
An Assessment of Non-Compliance with Procurement Proceedings in Procurement of Works in Nigeria

As part of public finance reform in Nigeria, procurement in the public sector was strengthened through enactment of Public Procurement Act, 2007, which details principles process and procedure to undertaken. However, experience has shown that inherent principles of transparency, efficiency and value for money have been a challenge orchestrated by non-compliance with procedures. This research therefore assessed areas/stages and reasons for non-compliance in the procurement proceedings of procurement of works in Nigeria. Using extensive review of related literature as well as eliciting of primary data through structured questionnaires administered to stratified and randomly selected 100 stakeholders’ respondents, mean score ranking was used to establish areas/stage and reasons noncompliance significant are bid open/evaluation and reporting, procurement procedure, and political party or authority influence of decisions are significant while correlation analysis instituted the strength and direction of positive linear relationship between the variables/ perceptions of public and private sector respondents, and Analysis of variance (ANOVA) indicated there is statistical significant difference areas/stage and reasons among variables and compared groups respectively . The study concluded by recommending strengthening transparent and objective competitive bidding process, establish structure that will exclude political influences and complexities in managing the procurement processes.
Deep Learning Techniques: A Review

Deep Learning models are effective due to their automatic learning capability. This review paper highlights latest studies regarding the implementation of deep learning models such as deep neural networks, convolutional neural networks and many more for solving different problems of sentiment analysis such as sentiment classification, cross lingual problems, textual and visual analysis and product review analysis.
The Influence of Perceived Insider Status on New Employee Engagement

This study from the perspective of new employees' perceived insider status, study on the influence mechanism of perceived insider status on engagement, and introduce psychological capital as intermediary variable. It focuses on the subject of new employees who begin to work within 3 years. Finally, 278 valid questionnaires were collected. Through empirical analysis, the research hypothesis is well verified, and the conclusion is drawn. The conclusions of this study include: (1) perceived insider status can affect new employees’ engagement; (2) psychological capital can affect new employees’ engagement; (3) perceived insider status can affect new employees’ psychological capital; (4) psychological capital plays an intermediary role in the relationship between perceived insider status and new employees’ engagement. At last, we propose the suggestion to strengthen new employees’ engagement, making the research can be effectively applied to the practice of enterprise management.
INFORMATION-THEORETIC OUTLIER DETECTION FOR LARGE-SCALE CATEGORICAL DATA

— Outlier detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specially developed for certain application domains. The outlier detection techniques have been proposed for the removal of unwanted data in order to get meaningful information based on classification, clustering, frequent patterns and statistics. The detection of outliers from unsupervised datasets is more complicated work since there is no inherent measure between the objects. We have proposed a novel method with effectively and efficiently detect outliers in unsupervised datasets using holoentropy. We have proposed ITB-SP outlier detection algorithm which do not require any user-defined parameter. This algorithm just takes the dataset as input and provides the dataset with outliers removed as the output. We have also used the concept of weighted holoentropy. The proposed method removes outliers more effectively and efficiently than other existing methods.
Performance and Evaluation of Three Phase Bridge Module Type Diode Clamped Multilevel Inverter

This work proposes three phase five level Bridge module type Diode Clamped Multilevel Inverter (DCMLI) using various modulating techniques for induction motor load. These Pulse Width Modulating (PWM) techniques include Carrier Overlapping (CO) strategy, Variable Frequency (VF) strategy, Phase Shift (PS) strategy and Sub-Harmonic Pulse Width Modulation (SHPWM) i.e. Phase Disposition (PD) strategy, Phase Opposition Disposition (POD) strategy and Alternate Phase Opposition Disposition (APOD) strategy. The Total Harmonic Distortion (THD), VRMS (fundamental), crest factor, form factor and distortion factor are evaluated for various modulation indices. Simulation is performed using MATLAB-SIMULINK. It is observed that PODPWM/PSPWM methods provide output with relatively low distortion for R Load. It is also observed that PSPWM method provides output with relatively low distortion for IM load. COPWM is also found to perform better since it provides relatively higher fundamental RMS output voltage for Induction Motor (IM) load and R load. Keywords— DCMLI, PWM, THD, Vrms, CF
Cerebral hemodynamics in stroke and traumatic brain injury

Na een acuut herseninfarct worden patienten opgenomen op een speciale ziekenhuisafdeling om stoornissen in de bloedsomloop en de zuurstofvoorziening te monitoren en te behandelen. Snel rechtop verplegen of uit bed halen is mogelijk gunstig voor het herstel na het herseninfarct. Er zijn echter ook aanwijzingen dat dit nadelig zou kunnen zijn voor bloeddoorstroming in de hersenen. UMCG-promovendus Marcel Aries ontdekte dat tijdens rechtop zitten en staan de bloeddruk gemiddeld toeneemt bij deze patienten. Dit fenomeen werd voornamelijk gezien op de eerste dag na het herseninfarct. Opmerkelijk hierbij was dat patienten met een forse toename van de bloeddruk bij staan (>20%), uiteindelijk een beter herstel na drie maanden leken te hebben. Nader onderzoek (continue bloeddruk- en ultrageluidmetingen van de grote hersenvaten en infraroodmetingen van de voorste hersengebieden) liet geen verschillen zien tussen patienten met een herseninfarct en gezonde proefpersonen. De promovendus vond geen aanwijzingen dat de zittende positie bij licht tot matig aangedane patienten met een acuut herseninfarct onveilig is tijdens de eerste drie dagen. Wiskundige analyse van de bloeddoorstroming van de hersenen laat zien dat intensief monitoren ook belangrijk is voor patienten met fors gezwollen of gekneusd hersenweefsel na een ernstig ongeval (ernstig traumatisch schedelhersenletsel).
Comparative Analysis Of Task Scheduling Algorithms In Cloud Environment In Term Of Their Future Prospective And Risk

: In the present scenario, due to covid19 pandemic dependents on the computer have increased at rapid speed, which can access from growth level gained by IT companies. For the processing of different applications, there are two types of environments Centralized or Distributed. Distributed Environments originate in reality in various avatars in the field of computing for example Grid Computing, Utility computing, Cloud Computing. Cloud computing is an evolving technology that is a virtual pool of resources and is provided to users on a pay-per-use basis without the burden of managing the infrastructure. For the best utilization of resources, efficient task scheduling is a major issue from the last eras, and amply task scheduling algorithms have been proposed for this purpose by well-known researchers. The problem of discovering an optimal feasible allocation is known to be NP-Hard and Heuristic techniques have found the optimal Solutions so this paper gives the extensive study of various heuristic task scheduling algorithms especially in terms of future perspectives and risks associated with them.
Update on the Neurobiological Aspects of Postpartum Depression

Postpartum depression is the most frequent psychiatric complication during the postnatal period and, given its potential impact on the well-being of the mother and the baby, its correct management should be a public health priority. Regarding its etiology, there is more evidence concerning the psychosocial factors involved in the process, but considering the remarkable hormonal variability during pregnancy and postpartum, differences could be expected between women with this pathology, or there could also exist some causal relationship. However, the literature has not been consistent with these findings or with the influence of other neurobiological changes. Recent researchs, including the evaluation of cytokines and neurotrophins (e.g. Brain-derived neurotrophic factor), has opened up new points of interest.
Data Protection for Clients in Private Network through Virus-Protection as a Service

— Cloud computing is seen as the next wave of Technology which will accelerate the pace of the IT industry. Cloud services will increase the computing power without investing much on infrastructure. The cloud offers service includes SPI – Software, Platform and Infrastructure. Cloud services enable users to remotely utilize the services in the pay-per use model. As the services types evolve, the need of the security shoots up. The aim of this paper discusses how to provide Security as a Service (SECaaS). Conventional virus protection applications are standalone and needs individual updates . The idea of providing Virus Protection as a Service(VPaaS) is discussed as a part of SecaaS . This centralized virus protection in cloud will serve as a one point Anti-virus application which will secure the clients in the network against virus attacks. This model can be seen as a successful business model for the organization and also reduce the cost of individual license for the applications.
Bimodal Emotion Recognition: A Comparative Study of Rule Based System Vs Classification Algorithms

Emotions can be understood in a face to face interaction immediately while in a human computer interaction (HCI) this response is limited. Research studies have been undertaken to investigate and develop various approaches and technology to incorporate emotions in HCI. One major concern of HCI now is the need to improve the interactions between humans and computers through justifications and explanations. Now a day's HCI is experimenting with alternate input mechanisms through speech, gesture, posture and facial expression. Endowing computers with this kind of intelligence is a complex task. It becomes more complex with the fact that the interaction of humans with their environment (including other humans) is naturally multimodal. In reality, one uses a combination of modalities and they are not entirely independent. As compared to unimodal approaches, various problems arise in case of multimodal emotion recognition especially concerning fusion architecture of multimodal information. We introduce a rule based hybrid approach to combine the information from different sources like facial expression and speech for recognizing emotions. The results presented in this paper shows that it is feasible to recognize human affective states with a reasonable accuracy by combining the modalities together using rule based system. The performances of our hybrid model i.e. rule based approach is compared with the traditional model i.e. feature based fusion model.
A Novel Improvised Anonymization with Map Reduce Framework for Big Data Privacy Preservation in Cloud

: Cloud computing provides promising scalable dynamic infrastructure to maintain various processing of a variety of big data real time applications in sectors such as healthcare and business and other area. Data sets like electronic health records in such applications often contain privacy-sensitive information, which brings about privacy concerns potentially shared to third-parties in cloud. Privacy-preserving data mining (PPDM) refers seeks to preserve sensitive information from unsolicited or unsanctioned disclosure. Protecting the individual privacy within the sensitive data is becoming a prominent research focus data in big data environment. Local-Recoding Anonymization plays major role in privacy preservation. Data anonymization refers to hiding identity of sensitive data so that the privacy of an individual is effectively preserved. Most of the existing privacy model fails to maintain effective privacy Preservation due to their insufficiency or poor scalability. The system proposes an innovative framework to effectively preserved privacy of an individual and to keep published data secret. The proposed system aims at achieve highly scalable privacy. The proposed system aims at achieve highly scalable privacy through the combination of Improvised k-anonymity along with novel SBDT model. It preserves better data utility than generalization. This aims provide better anonymize along with Minimizing the information loss.
Decision Tree Algorithms for Diagnosis of Cardiac Disease Treatment

: Data mining is the process of finding the previously unknown and potentially interesting patterns and relation in database. Decision tree learning algorithm has been successfully used in expert systems in finding the knowledge. The primary work is to performed in these frameworks is utilizing inductive strategies to the given estimations of characteristics of an obscure protest decide suitable grouping as per choice tree rules. This paper suggests several procedures and methods for building decision tree, such as ID3, C4.5, and CART. Good choice for decision making tree methods. Decision tree learning method is also one of the methods that are used for classification or diagnosis. Decision tree learning method is used in Medical science for diagnosis purpose. This paper suggests that decision tree construction with ID3 algorithm for Diabetic patient database. For this database I have choose Iterative Dichotomizer algorithm. This algorithm based on the homogenous mixture Entropy, Information Gain for the best split. Remote resources such as computers, databases, files etc. along with people like analysts, professionals, end users are often involved in the complex process of analysis of data. This investigation is in a ubiquitous way and is extremely import insect for applications which bargain in back, process control, safeguard and numerous more spaces. The ability to analyse large data amount is the demand of these applications. Decision tree a data mining technique which are CART, ID3 and C4.5 as are scalable and fast and are for data streams monitoring from omnipresent devices such as computers, palmtops etc.
Semantic Metadata for Creating and Describing Educational Resources

Nowadays, web contains a multitude of information sources and knowledge which often are represented as HTML files. These files can be used as educational resources (ERs) or may contain inside (piece of text, image …) important ERs. In this paper we propose an approach that allows us to extract and annotate these resources in order to store them into Learning
PRELIMINARY PHYTOCHEMICAL STUDY OF NYCTANTHES ARBOR-TRISTIS L. AND SANTALUM ALBUM L. FROM WESTERN GHATS OF KARNATAKA

Plants have an almost endless variety of metabolites which is very useful to human beings. The traditional medicine involves the use of different plant extracts or the bioactive constituents. Primary and secondary metabolites present in plants have been linked with the healing properties of plants. Hence, the present study was carried out to screen the phytochemical constituents in the leaf extracts of two medicinal plants viz., Nyctanthes arbor-tristis L. and Santalum album L. Different solvents viz., aqueous, ethanol, chloroform and hexane were used to extract the leaf of selected medicinal plants. Phytochemical constituents such as carbohydrates, amino acids, proteins, steroids, alkaloids, flavanoids, glycosides, cardiac glycosides, saponins, tannins, terpenoids, triterpenes, phlobatinnins, fatty acids, anthocyanins, leucoanthocyanins, coumarins, phenols, quinines and emodins were analysed in both the medicinal plants. The study showed that the presence of primary and secondary metabolites in all the four solvent extracts of selected medicinal plants. But Santalum album L. showed the highest level of phytochemical constituents compared to Nyctanthes arbor-tristis L.The results suggest that the phytochemical properties of these plants may serves for curing various ailments and possess potential antioxidant which leads to the isolation of new and novel compounds.
Genome-wide screening of genes required for Listeria monocytogenes biofilm formation

Listeria monocytogenes is a ubiquitous Gram-positive food-borne pathogen, which can cause serious infections in immunocompromised individuals due to the intake of L. monocytogenes contaminated food. In addition to planktonic growth, this pathogen can also grow as biofilms. Biofilm bacteria are highly resistant to sanitizers and are thus difficult to eradicate, posing a big challenge to food processing industries and food service departments. Here, we report the results from a systematic genome-wide study of genes involved in biofilm formation. Using transposon mutagenesis in combination with microtiter plate assays, we surveyed 10,000 transposon mutants. 17 mutants were isolated that showed more than 50% decrease in biofilm formation. The insertions were localized within 13 genes (11 operons) and two intergenic regions, which affect various cellular pathways such as wall and lipoteichoic acid biosynthesis and modification, cell wall turnover, flagella synthesis, ATP synthesis, and transcription regulation. Of these genes or regions, only the two flagella-related genes were previously known to be crucial for L. monocytogenes biofilm formation. All other genes are newly identified from our library screen. Further investigations on these genes will provide a better understanding of the molecular basis of L. monocytogenes biofilm formation, which will undoubtedly reveal important insights on effective measures to prevent and control contaminations in the food industry.
Cancer Awareness amongst Students of Southeast University Bangladesh

Cancer is a well discussed word in medical community as well as the public in the world. The occurrence of cancer is increasing in Bangladesh. Therefore, it is crucial to identify the risk factors that influence cancer occurrence relating to cancer awareness in health. The purpose of this study was to show the relationship of risk factors with cancer awareness amongst students of Southeast University and identify the influential factors responsible for body grading. University students were selected for completing a cancer awareness questionnaire form. Most of the students are within the age range of 18-29 years. All 181 students participating at a cancer awareness seminar at Southeast University Bangladesh were requested to fill up a Health History Questionnaire form of the students before the cancer awareness seminar. The study used bivariate analysis considering four risk factors as covariates. Frequency of departments/discipline with gender was calculated. Relationship was explored for awareness of cancer symptoms, smoking status, number of meals per day, and body structure. Also, distribution of respondents by awareness of cancer symptoms and few possible variables (sex, living place, cancer sufferer family members) were conducted. Bivariate analysis was performed for distribution of the respondents by their graded body structure. Physical exercise, medical checkup, living place, smoking status was taken as influential factors. This analysis calculated that about 9.9% students were entirely aware of cancer symptoms, the largest part, 60.2% were concerned slightly with its symptoms and the rest part didn’t have any idea regarding its symptoms. Also bivariate analyses of aware of cancer symptoms and other potential factors were performed. An attempt was taken to identify the significant variables responsible for their body structure, resulted in no significant correlation between any of the variables for cancer awareness. The results of this study showed the overall cancer awareness within the student community in undergraduate settings and developed the sense of cancer symptoms at very preliminary level. It also presented the importance of keeping body fit with a suggestion of avoiding carcinogenic substances.
Sensors Lifetime Enhancement Techniques in Outdoor Industrial WSN Applications

– The main challenge during designing a new node is reducing the power consumption as possible to maximize the lifetime of wireless sensor network (WSN) since most nodes are powered from a finite source of energy, in general non-rechargeable battery. In this paper we propose a solution to minimize the power consumed by the microcontroller (MCU), which is the main processing component in the node. Our energy power optimization solution is based on two techniques: firstly enhance dynamic power management (DPM) policies by reducing MCU consumption during Standby mode, secondly, focus on temperature impact on low power consumption. In this context a microcontroller dynamic power management (MDPM) algorithm is proposed to improve DPM scheme. This algorithm is deployed on a measurement circuit able to calculate the consumption during the different low power modes in real environments conditions then selects the better one. These two techniques are combined in a novel way to provide an efficient energy solution for wireless sensor networks nodes. Our solution is validated and qualified with STM32F401RE MCU.
IJPRBS 391

Repetitive sequences of DNA have explored a large amount of genetic data within the living organism. By the help of these repetitive sequences of DNA it is easy to study the genetic diversity within the organisms. Molecular Markers are the powerful tools for analysis of genetic biodiversity. Microsatellites are Simple Sequence Repeats of 1 Microsatellites are abundant throughout the genome They are found to have a great potential to provide genetic information within an organism, diagnosis of disease of genetic origin and in summarize the every aspect of microsatellites including features, application and drawbacks.
Rumination on Scaffold Pattern Language for Multi-Tenant SaaS Application Development

Internet has sophisticated the life of a populist. The cloud is an internet based technology which is location independent. It provides “on demand” service to the customer on pay-per-use model. The multi-tenancy is an essential characteristic in cloud, which has motivated many researchers to contribute their work. Scaffold is a platform, where, programmers can specify and integrate application and database. The pattern is a reusable component used to develop applications. NoSQL document data model has an advantage of storing the values in a tiered storage. In this contribution, a pattern language for the scaffold is proposed, which will be useful for the researchers to integrate and develop multi-tenant aware application.