Miracle-dz/UniFAR-FTUs huggingface dataset:
Sigmoid Oesophagus: Case Report and Review of Literature

It is the severe form of achalasia that is characterised by massive dilatation and tortuosity of the oesophagus. It is seen in long standing achalasia where the dilated upper segment of the oesophagus sags down below the level of the narrowed lower segment causing mechanical obstruction. Whereas Heller’s cardiomyotomy is considered to be the operation of choice in simple achalasia, it is regarded as inadequate for the treatment of this form of achalasia. While oesophagectomy with substitution is regarded as the treatment of choice in this group of patient, newer options have emerged and preliminary results appear to be promising. We present a case of end-stage achalasia with sigmoid oesophagus that had modified Heller’s procedure and reviewed relevant literature.
Oocyte freezing: basics, current status and potential applications in reproductive biology

Over the past several decades, considerable effort has been expended toward the successful cryopreservation of various cells. While attempts at cryopreservation have been directed at different tissue types, one of the most vigorously pursued targets has been reproductive tissue. Historically, cryopreservation of sperm has existed for several decades. The earliest reports of pregnancies (Trounson et al., 1983) and births (Zeilmaker et al., 1984) from the cryopreservation of embryos occurred in the early 1980s. Presently, the freezing and storage of embryos following in vitro fertilization (IVF) is standard practice at most fertility clinics. In 2003, the CDC Assisted Reproductive Technology success rates report stated that 4,246 live births occurred out of 17,517 non-donor frozen embryo cycles. Because the egg is a relatively voluminous cell with abundant cytoplasm, crystallization at the time of freezing may result in damage to the organelles. Secondly, a mature metaphase II oocyte contains a fragile spindle apparatus involved in cleavage. The purpose of this research study is to evaluate a method of freezing and thawing oocytes. This evaluation will be made by comparing the survival rates and rates of fertilization, cleavage and embryo quality of fresh oocytes and frozen-thawed oocytes which will be inseminated during the IVF (in vitro fertilization) treatment cycle. In addition, the same comparisons will be made between frozen oocytes from infertile women and those of egg donors. You are being asked to be in this study because you are currently undergoing in vitro fertilization
IDENTIFICATION OF FACTORS AFFECTING PEDESTRIAN LEVEL OF SERVICE OF CROSSWALKS AT ROUNDABOUTS

Walking is the most basic mode of commute and is an essential part of transportation. Every trip essentially starts or ends with a walk trip. Over the years, it has been realized that in order to encourage walking and non-motorized transport and reduce the use of personal vehicles, pedestrian facilities need to be provided keeping in mind the requirements of the users and also improving the serviceability of the facilities. Roundabouts form an integral part of the road network and offer many advantages over conventional signalized and unsignalized intersections. Crosswalks are a critical element of pedestrian infrastructure at roundabouts. For improving the level of service of crosswalk facilities and enhancing the level of safety and comfort perceived by the users, identification of factors which significantly affect pedestrian level of service at crosswalks is very important. Only after the identification of these factors, measures can be taken to improve the facility. In this paper, review of existing literature on pedestrian level of service of crosswalks has been done and significant factors are identified.
Stereo Matching with Gaussian Correlation Approach

Constructing photorealistic 3D models of object or scene from images is still a very difficult task in 3D image modeling research area. Matching problem is a challenging task in various application fields of computer vision such as recognition, identification, motion analysis, 3D structure reconstruction and so on. A robust method is represented for matching of the most interesting image points (feature points) among the multiple images. a detection algorithm of the corresponding corner points of an object is developed based on the Gaussian correlation based on the singular value decomposition approach. All of the corresponding image pairs between two images are computed by the transformation matrix. And then depth of the images are computed by Stereo Approach. Given its tremendous performance /complexity figure, the method is particularly suitable for research purposes where an off the shelf but reliable feature correspondence is needed.
IRIS Recognition and Authentication System for Enhancing Data Security

— IRIS recognition is a method of biometric authentication that uses pattern recognition techniques based on high-resolution images of the IRIS es of an individual’s eyes. Not to be confused with another less prevalent ocular-based technology, retina scanning, IRIS recognition uses camera technology, and subtle IR illumination to reduce specular-reflection from the convex cornea to create images of the detail-rich, intricate structures of the IRIS . These unique structures converted into digital templates, provide mathematical representations of the IRIS that yield unambiguous positive identification of an individual. IRIS recognition efficacy is rarely impeded by glasses or contact lenses. IRIS technology has the smallest outlier (those who cannot use/enroll) group of all biometric technologies. The only biometric authentication technology designed for use in a one-to many search environment, a key advantage of IRIS recognition is its stability, or template longevity as, barring trauma, a single enrolment can last a lifetime.
FPGA Realization of FLSE Classifier Based Multiple Feature Extraction Technique for Face Recognition

: Face Recognition is one of the biometric validation techniques and it has been extensively utilized for the real world applications. Although lots of techniques to recognize the face in real world applications, video based recognition techniques gives many challenge related utilization in most of places. In this paper, a novel classifier namely Fuzzy based Least Score Elimination (FLSE) classifier is introduced for efficient face recognition. This fuzzy based classifier gives important to the each frequency interval in an image; hence the accuracy of the recognition will improve. This process will be done on VHDL design language for FPGA realization. --------------------------------------------------------------------------------------------------------------------------------------------
A Cloud Based Electronic Office Framework for Ethiopian Public Universities (CBEOFEPU)

An effective cloud-based electronic office management system plays a crucial role in delivering services in a better way. The main goal of this research is to create a smart environment, a digital workplace solution, good communication, and collaboration between employers and Ethiopian higher education institutions and to add some cloud computing capabilities to the electronic office. The proposed system is intended to better drive the effective development of Smart society and to advance a step into an era of the paperless, replace the previous manual handling of documents with an efficient electronic system. The study uses questionnaire, Interview, and observation data collection techniques and the developed prototype and proposed framework evaluate by different users, experts in software engineering and related fields. This study proposes a private cloud platform architecture for electronic office in Ethiopian public universities Software as a Service. The Cloud-Based Electronic Office Framework (CBEOF) adapts the IBM Cloud Reference Architecture (IBMCRA), the NIST Cloud Reference Architecture, and the Microsoft Web Application Architecture to take advantage of all the considerations and capabilities of a cloud-based software architecture.
Review of Communication System Modelling for Supervisory Control and Data Acquisition (SCADA) Network

— Communication remains a vital backbone of computer network such as Industrial Control System (ICS), Municipal Area Network (MAN), Wide Area Network (WAN), Supervisory Control and Data Acquisition (SCADA) Systems, isolated inter-company networks and the general worldwide network – the internet. We examine the evolution of communication system model from the forties to the present day. It is observed that while the model evolves, the fundamental role of communication system remains the same which is primarily that of getting the information across (transmits) from the source (sender) to the destination (receiver) with high fidelity. Communication types, transmission media and formal modeling of signal behavior as proposed by Fourier [1] are also considered. The components of the earlier model is compared with the current model and it is found out that variation arises mainly from the inclusion of binary interface that simplifies the process of digitization of source information.
FEATURE BASED IMAGE OPTIMIZATION TECHIQUE

— The motivation behind the production of high resolution images is to increase the quality and the visual presentation of a digital image. The erstwhile analogue images captured by the silver Bromide film was having infinite resolution. However the options such as editing, transmission through the media etc features warranted the production of the digital images. Started with the few thousand pixels now, the digital images are of 16 Megapixels or more resolution and tend to bridge the gap between the analogue and digital images while maintaining the advantages offered by the digital images. However, in this effort of increasing the resolution, the size increases drastically and thus compression and optimization is required to the extent possible. The proposed feature based image optimization techniques will reduce the size of the image by incorporating feature based segmentation while maintaining the high quality in the areas of interest opted by users.
Growth of apical and lateral buds of Conadria fig cultivar as affected by growth regulators

Current study aimed to maximize multiplication rate, growth parameters and producing superior plantlets which can tolerate acclimatization conditions of Conadria fig cultivar through utilizing of Thidiazuron (TDZ), isopentenyladenine (2iP) separately. Shootsthat passed establishment stagewere cultured on MS medium that contained different concentrations of TDZ and 2iP (0.5,1 and 1.5 mg/l). In addition, activated charcoal and auxin type indole-3-acetic acid (IAA) and indole-3-butyric acid (IBA) were applied in rooting stage to evaluate their influences on root formation. Data indicated that number of shoots was produced on lower concentration of 0.5 mg/l 2iP while, 0.5 mg/l TDZ improved shoot thickness. Also, supplementation of the culture medium with 1.5 mg/l TDZ raised chlorophyll content. However, the optimal combination for number of roots and root length was 1.0 mg/l IBA+0.1mg/l IAA. Meanwhile, using of 1.0 mg/l IBA+0.1 mg/l IAA or 0.1 mg/l IAA increased root percentage (100%) as compared with auxin free medium plus activated charcoal, On another hand no beneficial effect was obtained from adding 0.1 mg/l IAA and1.0mg/l IBA+0.1mg/l IAA to the culture medium when root percentage was concerned. Conclusion of current study was gathering both of 2iP and TDZ since each hormone had a positive impact on proliferation rate, plantlet growth performance and chlorophyll content of fig (Conadria cv.), thereby gathering both of them may lead to promising results. Also, more studies should be carried out to state the optimum combination of TDZ and 2iP to produce ideal proliferation rate with vigor plantlets.
Enhancing the End to End QoS of multi-media applications in mobile adhoc networks by an Integrated Cross Layer approach

: The objective of this paper is to propose a novel method for enhancing end to end QoS of multimedia applications in mobile adhoc networks by using DiffServ approach and Multi Description Coding. The enhancement is achieved by implementing the Multi Description Coding (MDC) at application layer along with Connectionless Light Weight Protocol (UDPLite) in transport layer, Multipath routing and DIffserv at network layer. This approach achieves an increase of 36.84% in Peak Signal to Noise Ratio (PSNR) which is an improvement in PSNR and 20 % decrease in delay as compared to the conventional methods.
EMPLOYING STRUCTURAL AND STATISTICAL INFORMATION TO LEARN DICTIONARY (S) FOR SINGLE IMAGE SUPER-RESOLUTION IN SPARSE DOMAIN-A REVIEW

In this paper reviewed of an approach that explores each structural and statistical data of image patches to learn multiple dictionaries for super-resolving a picture in sparse domain. Such algorithms exploit the statistical previous that patches in a very natural image tend to recur among and across scales of a similar image. Structural data is estimated using dominant edge orientation, and suggests that value of the intensity levels of a picture patch is used to represent statistical data. This paper study of a unique computationally efficient single image SR methodology that learns multiple linear mappings to directly transform LR feature subspaces into hr subspaces. Self-similarity based super-resolution (SR) algorithms are able to produce visually pleasing.
Determinants of Entrepreneurial Intention : Selected Kenyan Universities Service Sector Perspective

Entrepreneurial intention has captured the attention of both academicians and policy makers during the last century. Unemployment poses a major challenge to developing nations Kenya inclusive where governments’ lacked sufficient resources to passing out of young graduates with immediate employment. However, many studies on entrepreneurial intention among graduates have focused mainly on developed countries and less on developing nations like Kenya perspective. Therefore, the purpose of the study was to bridge the gap by investigating the determinants of entrepreneurial intention among business undergraduate students. The specific objectives were to establish the effect of education, social network, innovativeness and self-efficacy on entrepreneurial intention. The study was guided by Ajzen’s Theory of Planned Behavior that views behavioral intent as an immediate determinant of planned behavior. Explanatory research design was adopted and the study targeted 1,649 undergraduate business students drawn from Moi, Mount Kenya and Catholic universities in Uasin Gishu County. Stratified sampling was used to select a sample size of 321. The results indicated that innovativeness among students was highly and positively correlated to entrepreneurial intention at (r=0.650). Self efficacy was the second characteristic among students to positively associated with entrepreneurial intention (r = 0.618), education had significant and positively correlated with entrepreneurial intention (r=0.564). Finally, social network showed a positive and significant relationship with entrepreneurial intention (r = 0.507). From the findings it showed that all the four variables were positively correlated to entrepreneurial intention at 0.05 level of significance level. The study findings were of have valuable assistance to develop students’ entrepreneurial skills and knowledge which equip graduates with creativity, innovation, risk-taking and ability to interpret successful entrepreneurial effect models and identification of business opportunities for the future. International Journal of Small Business and Entrepreneurship Research Vol.4, No.6, pp.41-52, December 2016 Published by European Centre for Research Training and Development UK (www.eajournals.org) 42 ISSN 2053-5821(Print), ISSN 2053-583X(Online)
A Review on MapReduce Scheduling Algorithm in Hadoop for Heterogeneous Environment

: Big data is a buzzword, or catch-phrase, meaning a massive volume of both structured and unstructured data that is so large it is not easy to process using traditional database and software techniques. Hadoop is one of the popular technologies in the big data landscape for evaluating the data through Hadoop Distributed File System and Map-Reduce. Hadoop schedulers are pluggable parts which assign resources to jobs. MapReduce is a programming standard and an associated implementation for converting and generating large datasets. It allow users to specify a map function which processes a key/value pair to create a set of in-between key/value pairs, and a reduce function that combines all the intermediate values linked with the same intermediate key. This paper presents the overall study about Big Data, Hadoop technology using Hadoop Distributed File System and MapReduce, their architectures, problem identification, problem statement and the recommended technology. Self-adaptive map reduce scheduling algorithm is used in the recommended technology.
Proximate Composition, Biochemical and Microbiological Changes Associated with Fermenting African Oil Bean (Pentaclethra macrophylla Benth) Seeds

The proximate composition, biochemical changes and microbiology of fermenting Pentaclethra macrophylla (Ugba) seeds were evaluated. Studies were carried out to screen for microorganisms associated with the natural fermentation of the oil bean seeds. Bacterial isolates obtained include species of Bacillus, Streptococcus, Salmonella, Micrococcus, Lactobacillus and Proteus. Fungal isolates include Yeast, species of Penicillium, Aspergillus, Fusarium and Rhizopus. Total aerobic counts (TAC) ranged from 1.5 x 10 to 2.5 x 10 cfu/g, while total coliform counts (TCC) ranged from 1.7 x 10 to 7.2 x 10 cfu/g. More so, total lactic acid bacterial counts ranged from 2.6 x 10 to 4.6 x 10 cfu/g. Among the various bacterial isolates obtained from the fermenting Ugba, Bacillus and lactic acid bacteria were dominant from the beginning to the end of the fermentation the oil bean seeds. The proximate composition of the fermenting seeds showed the presence of protein, fats, fibre, carbohydrates, and ash. Temperature variations in oil bean seed fermentation showed higher temperatures in the purchased Ugba compared to the laboratory Ugba after 72 hours fermentation. There were significant reduction in pH and titratable acidity as the fermentation time progressed, showing that temperature, pH and titratable acidity of fermenting African oil bean seeds were affected by the metabolic activities of resident microorganisms.
A Survey on Machine Learning Techniques for Intrusion Detection Systems

: The rapid development of computer networks in the past decades has created many security problems related to intrusions on computer and network systems. Intrusion Detection Systems IDSs incorporate methods that help to detect and identify intrusive and non-intrusive network packets. Most of the existing intrusion detection systems rely heavily on human analysts to analyze system logs or network traffic to differentiate between intrusive and non-intrusive network traffic. With the increase in data of network traffic, involvement of human in the detection system is a non-trivial problem. IDS’s ability to perform based on human expertise brings limitations to the system’s capability to perform autonomously over exponentially increasing data in the network. However, human expertise and their ability to analyze the system can be efficiently modeled using soft-computing techniques. Intrusion detection techniques based on machine learning and soft-computing techniques enable autonomous packet detections. They have the potential to analyze the data packets, autonomously. These techniques are heavily based on statistical analysis of data. The ability of the algorithms that handle these data-sets can use patterns found in previous data to make decisions for the new evolving data-patterns in the network traffic. In this paper, we present a rigorous survey study that envisages various soft-computing and machine learning techniques used to build autonomous IDSs. A robust IDSs system lays a foundation to build an efficient Intrusion Detection and Prevention System IDPS.
HYBRID RECOMMENDATION USING ASSOCIATION RULE MINING BY PARTIAL EVALUATION OF WEB PERSONALIZATION FOR RETRIEVAL EFFECTIVENESS

-World Wide Web is the biggest source of information. Though the World Wide Web contains a tremendous amount of data, most of the data is irrelevant and inaccurate from users’ point of view. Consequently it has become increasingly necessary for users to utilize automated tools such as recommender systems in order to discover, extract, filter, and evaluate the desired information and resources. Most recommendation algorithms attempt to alleviate information overload by identifying which items a user will find worthwhile. Web page recommender systems predict the information needs of users and provide them with recommendations to facilitate their navigation. Web content and Web usage mining techniques are employed as conventional methods for recommendation. In this paper, we proposed hybrid recommendation systems in m-commerce, which could integrate multiple association rules together to improve recommendation performance. The effects of the hybrid recommenders are examined by comparing the results of hybrid system against the results of single recommendation method. Result shows that the hybrid recommender provides successful recommendation when the recommended page is generated by all the systems of the hybrid. Our proposed approach based on both straight and meandering rules are attached into one set of global association rules, which might be used for the recommendation of web pages and for personalization. These experiments have shown that the use of reduced datasets saves computational time, and neighbor information improves performance.
An Advance Repetitive Control Scheme for Fuzzy Current Control Based Grid-Connected Inverter with Frequency-Adaptive Capability

In this paper, an advance repetitive control method (RCM) for fuzzy current control based grid connected inverter with a frequency adaptive capability is proposed. The main objective of the proposed scheme is to reduce total harmonic distortion (THD) such that by changing the FIR filter according to varied grid frequency and keeps the resonant frequencies fitting the grid fundamental and harmonic ones. The repetitive control scheme is adopted because it is very efficient in tracking periodic signals and low output total harmonic distortion (THD) where it is required that the ratio of sampling frequency be an integer. However, in real industrial application the ratio may be a non-integer then the resonant frequencies of RCM will diverts from the real grid fundamental and harmonic frequencies and this will take down the system performance. In order to establish the improvement of performance the proposed control scheme is compared with traditional repetitive control scheme based on PI controller. The control strategy is evaluated in grid -connected mode with experiments under different frequencies. The proposed controller effectively shows that it is better than the other control schemes in terms of the THD level and component harmonic of inverter systems .