Miracle-dz/UniFAR-FTUs huggingface dataset:
UNIQUE DATA MINING APPROACH TO PREDICT PLACEMENT CHANCE

Data mining is an effective methodology, which can be used to analyze large amount of data to produce hidden patterns and relationships. The main idea proposed is analyzing the law student’s historical data and predicting the appropriate, qualitative specialization chances. A student enters his/her rank, gender, sector and reservation category, and this model predicts specialization that suits for entered student as example with criminal law as E(excellent) and civil law as P(poor) based on the entered information. Different algorithms were applied for same set of data and comparison will be made in terms of accuracy, precision and truth positive rate. This paper will work for law students selecting a best specialization for them which ensures best carrier based on data entered. A model is proposed where in a latest algorithm, one from each different category of models such as clustering, classification and association models are selected and applied on the same dataset. Hence the name CCA model. Rock algorithm from Clustering, Naïve baye’s from classification and frequent pattern algorithm from Association models are selected. These algorithms are applied, to predict accurately one among the various courses offered which predict better placement chances. Student will enter Rank, Gender, Category and Sector and the model will give answer in terms of Excellent [E], Good [G], Average [A] and Poor [P] for the data entered. Algorithms are compared in terms of precision, accuracy and truth positive rate. From the results obtained it is found that the Clustering model predicts better in comparison with other two models. This work will help the students in selecting a best course suitable for them which ensure better placement based on the data entered
Acquired Credit Unions: Drivers of Takeover1

In this paper we study acquired credit unions and analyze their financial performance up to six years prior to merger, on a quarterly basis. The primary focus is on balance sheet (asset liability management) and profitability variables (return on assets). We find that acquired credit unions during the period 2008 (third quarter) to 2014 (first quarter) experienced negative return on assets for several quarters prior to their takeover. This was the result of a declining loan portfolio and increasing charge offs. In spite of decreasing lending activity, such credit unions continued to increase their deposits, i.e., adding to their cost base. Due to declining loans, their net interest margin as a proportion of deposits was also in decline. We argue that this is an indicator of poor management ability. Furthermore, our analysis finds that operating expenses were increasing over time, something that has been documented in previous literature also for smaller credit unions and is attributable to lack of economies of scale. The average asset size of the acquired credit unions in our sample is about $22 million just before acquisition. We attribute our findings to poor business strategy followed by such credit unions. We also conclude that signs of trouble are evident up to two years before merger on average and regulatory policy may have to become more proactive to manage the consolidation challenge faced by the credit union industry in general.
Review of Existing Methods in K-means Clustering Algorithm

– Data mining is the process of extracting useful information from the large amount of data and converting it into understandable form for further use. Clustering is the process of grouping object attributes and features such that the data objects in one group are more similar than data objects in another group. But it is now very challenging due to the sharply increase in the large volume of data generated by number of applications. Kmeans is a simple and widely used algorithm for clustering data. But, the traditional k-means is computationally expensive; sensitive to outlier’s i.e. unnecessary data and produces unstable result hence it becomes inefficient when dealing with very large datasets. Solving these Issues is the subject of many recent research works. In this paper, we will do a review on k-means clustering algorithms.
MODIFIED ORTHOGONAL FREQUENCY DIVISION MULTIPLEXING

The main drawbacks of OFDM are high PAPR and inter symbol interference. The selected mapping technique is one of the most popular PAPR scheme for OFDM system. The present scheme, which is used to reduce PAPR, is SLM. In SLM scheme, the modulated information is multiplied with different phase sequences and finally we select the phase-multiplied data with minimum PAPR. The main drawbacks of SLM technique are computational complexity and the need for transmission of several side information bits for each data block which causes reduction of data rate. In the proposed method the side information embeds in predefined phase sequence and predefined phase sequence transmitted in side lobe. The receiver is designed in such a way that it receives all the multipath signals, then it does the time scaling to arrange all the multipath signals and finally it averages all the time scaled multipath signals. The Proposed technique is Improved SLM (ISLM). The decoding computational complexity of ISLM is much lower than that of the SLM. The simulation results show the PAPR reduction performance and ISI reduction performance in multipath channel verses AWGN channel by using ML technique at the receiver.
Counterfeit Avoidance using Mark-Time Protocol in Mobile Networks

a track of the nodes through which the packets are sent, the retransmission of packets within the network can be avoided. A new protocol called the MARK-TIME protocol is introduced to track the information and store it in common at the base station of the network. The distance of the node in the network is calculated along with the path, is used to transmit the message packets within the network. The detail of the packets and the path along which the data is transmitted is stored in the proxy server at the base station. In the case of delay or loss in communication, due to mobility of nodes in the network, it leads to loss of the receiving packets in the receiver (sink node). It sends a message to the proxy server, which tracks the node and provides efficient transmission without duplication of packets within the mobile network.
Internal and External factors based clustering algorithm for MANET

in Abstract—Several MANET characteristics that distinguish MANET from other wireless networks and makes routing a challenging task. This paper presents a novel clustering algorithm, which uses both internal and external factors of node in mobile ad hoc network. In Cluster based MANET routing scheme, various mobile nodes are formed as clusters. Each cluster has its own cluster head, which is having more responsibilities like, minimizing the topology changes, stability and re-affiliation of on demand networks. Since the proposed work uses both internal and external factors of node, it is aiming at finding quick response during the routing process Simulations are performed using NS2 simulator. We compare the proposed clustering algorithm with other algorithms, which are Low energy adaptive clustering hierarchy (LEACH) and A Weighted Clustering Algorithm for Mobile Ad Hoc Networks (WCA). Our analysis and simulation results show that, the proposed algorithm efficiently manages cluster head updates, re-affiliation, cluster head stability and it outperforms the other algorithms.
Email Summarization using Latent Dirichlet Allocation (LDA)

Summarizing email conversations are challenging due to the characteristics of emails, especially the conversational nature. Most of the existing methods dealing with email conversations use the email thread to represent the email conversation structure, which is not accurate in many cases. We are presenting an approach which will help to remove flaws of Clue Word Summarization (CWS) algorithm. In CWS the emails are used to mark with only one category whereas according to LDA one email may not be categorized into one category only. The email may have more than one categorization. We have designed system for categorization of emails using NetBeans IDE. The accuracy of thealgorithm is calculated by testing both the algorithm with no. of content inputs. The Accuracy of CWS is 75% whereas the accuracy of LDA is 95%. The processing time of CWS is 1800 milliseconds whereas the processing time of LDA is 1500 milliseconds.
Dedicated Client Architecture in MapReduce and its Implications on Performance Considerations

Big data refers to a large quantity of data that has to be processed at one time. With the advancement of social media and the virtual world, a vast amount of data is created every second. A technique has to be designed to effectively process this ever-increasing collection of information. One such algorithm is the MapReduce algorithm. The result or output of the algorithm provides useful insights about the data used as input and can be further used for Decision Making and Prediction algorithms. Also, new data is generated frequently for Big Data processing. Hence, MapReduce implementations must not only be accurate but also as instantaneous as possible. This paper discusses not only the details of Map Reduce Algorithm; it also suggests architecture called Dedicated Client Architecture which would increase the efficiency of the algorithm. General Terms Big Data, Map Reduce, Client Server, dedicated client.
Information Security in Cloud Computing: A systematic Literature Review and Analysis

: Cloud computing is an evolutionary outgrowth of prior computing approaches which build upon existing and new technologies. Cloud computing is a model for on demand network access to a shared pool of resources such as servers, storage, applications and related services. Cloud computing can be provisioned and released with minimum interaction and preferably without intervention of cloud service provider. The rising awareness and implementations of cloud services and its underlying technologies cause the need for security requirements being up to date. These developments have created new security vulnerabilities, including security issues whose full impressions are still rising. This paper presents an overview and study of cloud computing, with several security threats, security issues, currently used cloud technologies and countermeasures. The security challenges in cloud computing are formidable, especially for public clouds whose infrastructure and computational resources are owned by an outside party that sells those services to the general public. Cloud security requirements have been addressed in publications earlier, but it is still difficult to estimate what kinds of requirements have been researched most, and which are still under-researched. This paper carries out a systematic literature review by identifying cloud computing security requirements from publications. Along with security issues, upside of information security in cloud computing has also been part of this work.
Study of Surface Roughness by Stylus Profilometer and Binary Laser Speckle B/D Counting Techniques

In this paper, surface roughness measurements by non contact method of Binary Speckle Pattern (BSP) and contact method of stylus profilometer have been presented. When an optically rough surface is illuminated with a highly coherent source of Laser, the granular bright and dark pattern known as speckle is observed. Intensity of the Speckle image depends on the surface roughness and carries information about it. The speckle images obtained by HeNe laser with CCD camera were binarized and examined. To evaluate the surface roughness, pixel ratios of bright and dark regions (B/D counting) of various speckle images of different surface roughness are used. The results are compared with the surface roughness parameter Ra obtained from stylus profilometer.
A Dynamic Approach to Extract Texts and Captions from Videos

— Detecting text and caption from videos is of great importance and is of greater demand for video retrieval, annotation, indexing, and content analysis etc. In this paper, we have chosen 2 approaches to treat the text extraction problem, to detect text and caption from videos. Each of both uses the characteristics of artificial text. In connected regions approach we use split and merge algorithm in order to split non homogeneous regions and to merge adjacent homogeneous regions. Using edge detection, edges will be computed using certain threshold. Then each text area will be binarized. Then the region with a possibility of text in the video is detected. The most distinctive feature of the proposed operator over most other operators is that it offers excellent line, edge, detail
The effects of a watery extract of Agathosma on the development of hypertension

III OPSOMMING . V ACKNOWLEDGEMENTS . VII DISCLOSURE OF INTEREST . VIII TABLE OF CONTENTS . IX LIST OF FIGURES. XV LIST OF TABLES . XVII LIST OF ABBREVIATIONS . XVIII CHAPTER 1: Literature Review . 1 1.1 Obesity . 1 1.1.1 Concept of Obesity . 2 1.1.2 Definitions of obesity: . 3 1.2 Fat stores . 5 1.2. 1 Fat distribution in Obesity (pear and apple shape) . 6 1.3 Hypertension . 7 1.3. 1 Effect of age on BP . 9 1.3. 2 Effect of race on BP . 9 1.4 Regulation of BP . 10 Stellenbosch University http://scholar.sun.ac.za
Primal-Dual Asynchronous Particle Swarm Optimisation ( pdAPSO ) Hybrid Metaheuristic Algorithm for Solving Global Optimisation Problems

: Particle swarm optimization (PSO) is a metaheuristic optimization algorithm that has been used to solve complex optimization problems. The Interior Point Methods (IPMs) are now believed to be the most robust numerical optimization algorithms for solving large-scale nonlinear optimization problems. To overcome the shortcomings of PSO, we proposed the Primal-Dual Asynchronous Particle Swarm Optimization (pdAPSO) algorithm. The Primal Dual provides a better balance between exploration and exploitation, preventing the particles from experiencing premature convergence and been trapped in local minima easily and so producing better results. We compared the performance of pdAPSO with 9 states of the art PSO algorithms using 13 benchmark functions. Our proposed algorithm has very high mean dependability. Also, pdAPSO have a better convergence speed compared to the other 9 algorithms. For instance, on Rosenbrock function, the mean FEs of 8938, 6786, 10,080, 9607, 11,680, 9287, 23,940, 6269 and 6198 are required by PSO-LDIW, CLPSO, pPSA, PSOrank, OLPSO-G, ELPSO, APSO-VI, DNSPSO and MSLPSO respectively to get to the global optima. However, pdAPSO only use 2124 respectively which shows that pdAPSO have the fastest convergence speed. In summary, pdPSO and pdAPSO uses the lowest number of FEs to arrive at acceptable solutions for all the 13 benchmark functions
CONTROLLED-RELEASE EFFERVESCENT FLOATING MATRIX TABLETS OF METFORMIN USING COMBINATION OF POLYMERS

Objective : It is always challenging to prepare the floating gastro retentive formulation of the high dose drug. The present research was aimed to prepare gastro retentive controlled release, matrix tablets of metformin, using the combination of different ionic, anionic and polyanionic polymers with HPMC. Methods: Formulations were prepared using sodium alginate, pullulan, kappa carrageenan, xanthan gum, poloxamer 68 in combination with HPMC K15M. All matrix tablets were prepared by direct compression method and evaluated for swelling, floating adhesive period and drug release. Results: All the tablets showed acceptable physicochemical properties. Statistical analyses of data revealed that tablets prepared using HPMC K15M and kappa carrageenan, formulation F2, is best in terms of showing excellent floating properties, extended adhesion periods and sustained drug release characteristics with similarity factor as 92 on comparison with the theoretical release of the drug. Conclusion: The combination of HPMC K15M and kappa carrageenan can be further optimized by applying the appropriate statistical design.
Spirogyra biomass a renewable source for biofuel (bioethanol) Production

Biofuels refer to renewable fuels from biological sources that can be used for heat, electricity and fuel. The fuels obtained from algae are termed as third generation fuels. The production of fuel from algae provides many advantages when compared to the fuel produced from other sources like agrobased raw materials. Other than environmental pollution control the algal biofuel will help in reduction of the fuel cost when compared to the agrobased and fossil fuels. In the present study algae specifically Spirogyra was used for the production of bioethanol by the fermentative process. A comparative study was carried out by using chemically pre-treated and untreated Spirogyra biomass. The Spirogyra has a very simple cell wall made up of cellulose and starch that can be converted to ethanol by the fermentation process. The Spirogyra biomass was subjected to saccharification process by the fungal organism Aspergillus niger MTCCC 2196 for the hydrolysis, this process was followed by the fermentation using yeast Saccharomyces cerevisiae MTCC170 for the production of alcohol. A high yield of ethanol was recorded for untreated Spirogyra biomass when compared to chemically pre-treated biomass. The yield of alcohol using algal biomass is more when compared to alcohol produced from other sources like agrobased raw materials.
Plant steroidal hormone 24-epibrassinolide regulate pathogen stress tolerance in Solanum lycopersicum L. by modulating antioxidant defense expression

The present investigation illustrated antifungal efficacy of 24-epibrassinolide in the management of Alternaria alternata rot inflicting tomato fruits. Application of 24-epibrassinolide completely inhibited the growth of A. alternata in vitro as well as in vivo. There was a significant reduction in mycelial growth as well as conidial count of A. alternata after addition of 24-epibrassinolide in PDA medium as compared to control. An increase in malondialdehyde content was recorded after pathogen inoculation suggesting oxidative stress. A significant decrease in malondialdehyde content was observed in tomato fruits affected with A. alternata and treated with 24epibrassinolide, with maximum decrease recorded for 10 -9 M epibrassinolide concentration both at pre as well as post inoculation. Enhanced activities of antioxidant enzymes like superoxide dismutase, catalase, ascorbate peroxidase and glutathione reductase were recorded in response to pathogen inoculation, activities got further stimulated by the application of epibrassinolide when compared with pathogen-inoculated tomato fruits only. Similarly, non enzymatic antioxidants viz., ascorbic acid, glutathione and total phenols were enhanced in tomato fruits inoculated with A. alternata and treated with -epibrassinolide, revealing its positive role in disease resistance as observed by a decrease in rot percentage. Results, thus, demonstrate practical efficacy of 24-epibrassinolide as a plant-based antifungal product for post harvest application.
International Journal of Current Research in

Fresh elder leaves of species viz., Costus pictus and Costus speciosus were collected from Western Ghats, Tamil Nadu. Leaves were washed thrice in sterile distilled water and shade dried and powdered. Then 500g of powdered leaves was soaked in 1 litre of ethanol water mixture (3:1) for 10 days in an air tight clean glass container. Mixing was done every day so as to enable to enhance the maximum extraction of bioactive compounds. After that, the solvent containing bioactive extracts were filtered through muslin cloth and kept in the rotary flash evaporator (with solvent trap) so as to obtain solvent free extract residue and tested for in vitro assessment of anti-inflammatory property. Leaf extract of C. pictus and C. speciosus at different concentrations provided significant protection against denaturation of proteins. The C. pictus extract showed maximum percentage inhibition of 97.87% at 400 mg/ml, whereas C. speciosus gave 86.04% at 400 mg/ml. Diclofenac sodium, a standard anti-inflammatory drug showed the maximum inhibition 97.56% at the concentration of 400 mg/ml. So it can be concluded from the present study that, the C. pictus and C. speciosus leaf extract can be used as an anti-inflammatory agent. Accepted: 23 September 2016 Available Online: 06 October 2016
Predictive Analysis of Crops Cultivation for a Smart Green Environment Using Azure Services

: Internet of Things is a pervasive field and can be efficiently implemented in the agriculture sector. Internet of things can revolutionize the world today making it more efficient and smart. Agriculture is very essential in a country and integrating it with Internet of things (IoT) technology can take automation in agriculture to a whole another level. Ever increasing population brings with an increase in demand for food and to sustain the farming must be made more productive. IoT enhances the agricultural productivity by providing the farmer with information about soil moisture, temperature, humidity and acidity of the soil. This research work implements a practical system which deals with monitoring the crop field through a wireless network of sensors (light, humidity, temperature, soil moisture, water level indicator etc.) along with automating the irrigation system based on several field constraints. The farmers can monitor the farm conditions through a web app from anywhere, anytime and receive timely notification about the changes in the farm. This makes IoT based farming highly efficient when compared with the conventional cultivation approach. IoT technology and predictive data analytics can be used to enhance the agricultural productivity by providing the farmer, information about soil moisture, temperature, humidity and acidity of the soil. In IoT-based farming, the crops are monitored with the help of light, humidity, temperature, soil moisture and Ultrasonic sensors along with automating the irrigation system. Even in case of environmental issues, IoT based farming provides great benefits like more efficient water usage and optimization of fertilizers and plant treatments. The article aims in making use of IoT technology for smart agriculture. ThingSpeak Math works IoT platform is used for analyzing and presenting agriculture fields sensor data. The major objective of this paper is to collect real-time sensor data of a green environment and make predictions on crops cultivation pattern based on the weather condition through MS Azure IFTTT services.