Miracle-dz/UniFAR-FTUs huggingface dataset:
Smart Body Monitoring system using IoT and Machine Learning

In the modern health care environment, the usage of IoT technologies brings convenience of physicians and patients, since they are applied to various medical areas. The body sensor network (BSN) technology is one of the core technologies of IoT developments in healthcare system, where a patient can be monitored using a collection of tiny-powered and lightweight wireless sensor nodes. However, the development of this new technology in healthcare applications without considering security makes patient privacy vulnerable. In this paper, at first, we highlight the major security requirements in BSN-based modern healthcare system. Subsequently, we propose a secure IoT-based healthcare system using BSN, called BSN-Care, which can efficiently accomplish those requirements. The body sensor network (BSN) technology is one of the most imperative technologies used in IoT-based modern healthcare system. It is basically a collection of low-power and lightweight wireless sensor nodes that are used to monitor the human body functions and surrounding environment. Since BSN nodes are used to collect sensitive (life-critical) information and may operate in hostile environments, accordingly, they require strict security mechanisms to prevent malicious interaction with the system.
Lossless Compressed Tone Mapped Image Enhancement for Digital Printing

– In this paper presents a novel lossless compressible Tone mapping Framework which provides high Quality images for digital printing. First, we discuss a lossless Tone mapping framework which favors the output to stay faithful to original input and also have enhanced details and contrasts. Second, we pass the first output through novel smoothing effect to remove small – resolution structures that are with large amplitudes. Smoothing strength can be improved by passing through coarse scale, medium scale and fine scale smoothing filters and finally, the combined output is passed through an optimization approach in which the contrast of the image is preserved to its maximum. The result is a crisper, better looking digitally printable output with sharper edges and smoother curves with no blurs and jaggies.
Android Speech-to-speech Translation System For Sinhala

— Collaborator (Interactive Translation Application) speech translation system is intended to allow unsophisticated users to communicate in between Sinhala and Tamil crossing language barrier, despite the error prone nature of current speech and translation technologies. System build on Google voice furthermore also support Sinhala and Tamil languages that are not supported by the default Google implementation barring it is restricted to common yet critical domain, such as traveling and shopping. In this document we will briefly present the Automatic Speech Recognition (ASR) using PocketSphinx, Machine Translation (MT) architecture, and Speech Synthesizer or Text to Speech Engine (TTS) describing how it is used in Collaborator. This architecture could be easy extendable and adaptable for other languages.
Proposing an Enhanced Face Recognition Technique Using Multi-Wavelet Transform

Face recognition has been an active research for more than three decades. In general face recognition consists of feature extraction and classification. Principal Component Analysis (PCA) is one of the methods for features extraction under the appearance-based approach. However PCA came with large database, having some problems like load computation, and poor recognition accuracy. This research is an attempt to focus on large size of database problems with PCA technique. In order to make PCA work well, we suggest using Multi-Wavelet transform, which has two features. The first one is that it can decompose the image into frequency sub-band and the second feature is to reduce the resolution of the image. Moreover, in this research, the face recognition system can be classified into two phases. The first phase is training, which is of four steps. The first one is applying Multi-Wavelet transform to reduce the size of image. The second is PCA for feature extraction from images after reducing the size. The next is projection images, and finally is the library of projection images. The second phase is recognition, which is composed of three steps. The first step is similar to the training phase applied in the Multi-Wavelet transform. The second one is PCA for extraction the average from testing images, while the third is using the classification method via the Nearest Mean Classifier. Observations from the experiment results, based on Yale databases, show that the proposed method has better accuracy rate than the original PCA.
Software Defect Prediction for Quality Improvement Using Hybrid Approach

In today’s computing environment, software systems have become increasingly complex and versatile. Therefore it is necessary to continuously identify and correct software design defects. Software defect prediction plays an important role in improving software quality and it helps to reduce time and cost for software testing. The Software defect prediction is a method which predicts defects based on historical database. Different machine learning techniques are used to predict software defects from historical databases. The paper mainly focuses on generating accurate rules for software defect prediction System. For this purpose, K-means clustering technique is used for discretization. Then association rule mining is applied to generate rules in large volumes of data using Apriori algorithm. Software defect prediction system has been experimented on open sources NASA defect dataset. It contains software metric data and error data at the function or method level. In this paper comparison of proposed approach with existing approaches is discussed and the results show that proposed method is generated only interesting rules therefore it is effective for software defect prediction.
Study of the Glycemic Control and the Biochemical Profile in the Gabonese Diabetic Patient

Background: Management of diabetes remains a challenge in Africa. Objective: The aim of this study was to evaluate the glycemic control in diabetics patients with diabetes in Gabon sub-Saharan country. Methods: This study involving 87 diabetic patients (Men25 ; Women : 62) were investigated anthropometric parametres, glycemic control and biochemical profil. Results: All our results show that with an average age of 53±11.02 years diabetic Gabonese patients present a poor glycemic control (P <0.0001): Glycemia (Control: 4.95 ± 1.16 mmol/l vs Diabetic : 10.27 ± 4.47 mmol/l) ; HbA1c (Control : 5.05 ± 0.46% vs Diabetics : 7.40 ± 2.36%) associated with a hepatic steatosis : Alat ( Alat : Control: 17.25 ± 13.7u/l vs Diabetic: 25.84 ± 13.19 u/l), Asat (Control: 18 ± 13.20 u/l vs Diabetic: 36.93 ± 17.87 u/l). Conclusion: Is evidence, a high proportion of patients with diabetes remains poorly controlled. This is the case in Gabon diabetic patients. ISSN 2475-5451
Optimization of pressurized hot water extraction of Lavandin essential oils via central composite design

Pressurized hot water extraction (PHWE) and hydrodistillation were compared for the extraction of essential oil from Lavandin. Using GC-FID analysis in conjunction with central composite design, the maximum extraction yield was obtained for temperature of 148 o C, pressure of 33.7 (bar), static time of 10.7 (min), dynamic time of 17.6 (min), and flow rate of 0.69 (mL/min). The PHWE is a practical technique for separation of constituents such as 1,8-Cineole (8.1%), linalool (32.1%), linalyl acetate (32.3%), and camphor (5.2%) from Lavandin to be applied in the food, fragrance, pharmaceutical and natural biocides industries. The PHWE method is quicker (28.3 min versus 3 h) and more extraction yield (5.10 versus 1.52) than hydrodistillation
A Novel Approach Towards Automatic Text Summarization Using Lexical Chains

— Text summarization is a process of extracting text by virtue of reduction of document contents while preserving the salient information intact. By using different set of parameters like position, format and type of sentences in an input text, frequency of words in a text etc., techniques have been developed. But the parameters vary depending on source of input texts. This in turn affects the performance of the algorithms. In this paper, we present a new method of automatic text summarization by making use of lexical cohesion in the text. Until now lexical chains have been used to model lexical cohesion. These lexical chains are sequences of words having semantic relations between them. In our proposed algorithm, we have used a modification of lexical chains to model the relationships that exist between words.
Improving Prediction Accuracy Using Hybrid Machine Learning Algorithm on Medical Datasets

— In Computer Aided Decision(CAD) systems, machine learning algorithms are adopted to assist a physician to diagnose disease of a patient. The purpose of this study is to improve the prediction accuracy on medical datasets by hybridizing machine learning algorithms. In this paper Hybrid Machine Learning algorithm is proposed using two supervised algorithms, Naïve Bayes and JRIP. The methodology adopted in this paper for proposing new Hybrid Machine Learning Algorithm is implemented by using R programming language and weka software tool. Further, comparative study is made between individual algorithms and proposed hybrid algorithm to prove the improvement in prediction accuracy on medical datasets. The proposed algorithm shows enhanced performance compared to the individual classifiers and assist the physician in diagnosis.
Early Stage Disease Prediction by using BPNN-NB Ensemble Technique

— Many diseases turn fatal when left untreated and most of the patients are not aware that they have a certain disorder. Hence, detecting disease at an early stage must be important for improving the life span of the affected people. Diabetes and Cancer are two of the most commonly occurring diseases that can be predicted and treated at early stages. In this paper, a novel classification technique is implemented that combines the Back Propagation Neural Network (BPNN) and Naïve Bayes (NB) classifiers for predicting the cancer and diabetes. The proposed technique is analysed for different evaluation metrics like accuracy, precision, recall and false positive rate. However, this algorithm can also be used to predict lots of other diseases by using certain datasets, which will be a future scope of our work.
Partial Mitochondrial DNA Barcode of Rafflesia

In this paper, partial DNA barcode of R.mira syn. R.magnifica a critically endangered holoparasitic plant from Maragusan Compostela Valley Province was provided using mitochondrial gene sequence matR. Partial matR sequences generated from 3 replicates of R.mira (syn.R.magnifica) showed very close genetic variability between Philippine endemic species R. speciosa and R. manillana as shown in phylogenetic tree using neighborjoining with bootstrap support and confirmed by pair-wise
An Exploration Of Various Change Detection Techniques For Hyperspectral Images

Change detection is the process of automatically identifying and analysing regions that have undergone spatial or spectral changes from multi temporal images. Remote sensing is the most suitable technology used for change detection in many application areas. Hyperspectral remote sensing images deal with a large number of wavelength bands as compared to single band or multispectral images. Thus ample of spectral information provided by hyperspectral images present promising change detection performance. The main objective of this paper is to study the comparative analysis of various change detection techniques for hyperspectral images. This paper presents a review of advantages and limitations of different change detection techniques.
Potentials and Capabilities of FACTS Controllers for Quality and Performance Enhancement of Power System

At present the demand of electrical power is growing day by day with a very fast rate, so we require much capable transmission and distribution system with the most excellent quality of supply. That’s why power quality is a major issue in the distribution system and this is the area which attracts all electrical engineers. The major impact of poor quality of supply is on the highly loaded distribution system, on which the maximum load is connected. In this paper authors focus on the FACTS technology, classification schemes, applications, potentials and control attributes in terms of the performance and quality issues of power supply. Power quality means the nonstandard supply voltage, supply current and supply frequency, that results malfunctioning or failure of operation of various equipments and accessories. Key Words-FACTS devices, Transmission and distribution system, power quality and Power flow control.
Machine Vision Based Autonomous Fruit Inspection And Sorting

- India is a country with agricultural backbone. Major most of the agricultural products in India are grains, vegetables and fruits. As it comes to fruit harvesting and packaging in India, human labor has been an indispensable part. But manual hand picking and packaging offers many drawbacks such as time-inefficiency, monotony etc. Apart from this, with labor costs peaked to extreme high nowadays, it is a need to search for cost-efficient and time-efficient measures for fruit harvesting and packaging. One emerging technology for fulfilment of this purpose is ‘Machine Vision’ technology. Machine Vision or ‘Image Processing’ technology is extremely fast, cost-efficient and hygienic. This paper aims at design and development of a low cost automated mechatronic system for inspection and grade-wise sorting of fruits and fruit vegetables such as lemons, tomatoes, apples, guavas, mangoes etc. using image processing technique. Fruits are sorted as immature, rotten and edible. Edible fruits can further be graded and sorted according to size. This paper explained technique to collect data like size, color, defects of fruit particularly lemon and sort it out in different categories as mentioned before. Suitable matlab algorithms like conversion to binary image, area calculation, and average pixel value calculation are used. For sorting Arduino-Uno microcontroller is used. Our vision is to make this system frugal yet efficient with flexible algorithm so that every single fruit farmer can set up his own fruit sorting facility and will avail himself his own market, bringing self-reliability to farmer.
Evaluation of Feature Selection Algorithm for Android Malware Detection

This paper synthesizes an evaluation of feature selection algorithm by utilizing Term Frequency Inverse Document Frequency (TF-IDF) as the main algorithm in Android malware detection. The Android features were filtered before detection process using TF-IDF algo-rithm. However, IDF is unaware to the training class labels and give incorrect weight value to some features. Therefore, the proposed approach modified the TF-IDF algorithm, where the algorithm focused on both sample and feature. Proposed algorithm applied considers the feature based on its level of importance. The related best features in the sample are selected using weight and priority ranking process. This increases the effect of important malware features selected in the Android application sample. These experiments are conducted on a sample collected from DREBIN dataset. The comparison between existing TF-IDF algorithm and modified TF-IDF (MTF-IDF) algo-rithm have been tested in various conditions such as different number of sample, different number of feature and combination of different types of feature. The analysis results show feature selection using MTF-IDF can improve malware detection analysis. MTF-IDF proved either using various kinds of feature or various kinds of dataset size, algorithm still effective for Android malware detection. MTF-IDF algorithm also proved that it could give appropriate scaling for all features in analyzing Android malware detection.
DOES ISLAMIC BANKING DEVELOPMENT REGARDING SIZE AND ACTIVITY LEAD TO FINANCIAL CRISIS? CASE OF SUDAN

In this paper, empirical analysis is used to determine Sudan’s Islamic banking development impacts that describe the causality relationship and identify its size and activities over the period of 1990Q1 to 2010Q4. Short and long-run estimations were identified through the Auto Regressive Distributed Lag ARDL model in the study. Regarding size and activity identified in the results from the long and short run, Islamic banking development ganger causes the crisis and these are same such feedback from the crisis. Findings describe that in the long run, Islamic banking activity development hurts the crisis. On the other side, Size of Islamic banking has positive effects on the crisis to the estimation for the long run and short run. In addition, from the findings, activity based system of Islamic banking is more suitable than the size of Islamic banking to get the goal of growth before crises in Sudan. Islamic banking play very vital role in promoting growth and reduce negative effects from international and internal economic and financial crises.
EFFECTS OF AUTOMATED TRAFFIC ENFORCEMENT ON DRIVER BEHAVIOR AT A SIGNALIZED INTERSECTION IN SAUDI ARABIA

: For automating road network Saudi Arabia recently introduced 'Saher Traffic Monitoring Enforcement System' (STMES) using red-light and speed-limit camera. This paper analyzes the impact of STMES on driver behavior and decision making process at signalized intersections. It proposes models for analyzing driver's probabilities of stopping and crossing decisions at signalized intersections during yellow-light interval on the basis of speed, distance from intersection and vehicle type. Models also compare STMES fitted intersections with conventional intersections without automated systems. Binary logistic regression and Artificial Neural Network (ANN) models are developed using video data at intersections in Makkah city. Analyses reveal that STMES increases stopping probability by 26 percent and distance to the stop line is the most significant factor affecting the drivers’ decisions in the yellow interval. Dilemma zone shifts closer to the stop line and the number of early stops increases due to STMES. Artificial neural network model performs better than binary logistic regression model in predicting the stopping probability at signalized intersections. Research findings suggest that ANN models can be effectively used in traffic signal design and dilemma zone reduction to improve efficiency and safety at roadway intersections.
Community Perceptions of Human-wildlife Conflicts and the Compensation Scheme Around Nyungwe National Park (Rwanda)

In African countries, crop-raiding by wild animals and human-wildlife conflicts are recurrent and common concerns for conservationists, protected areas managers and surrounding populations. At different levels, these challenges hinder the achievement of conservation goals. Although researchers have widely studied human-wildlife conflicts in Nyungwe National Park, few have examined its impact and lived experiences of local communities who are regularly facing crop-raiding by wild animals. The present study conducted in areas adjacent to the park including those affected with a high rate of crop raiding aimed to assess: (1) the community perceptions on human-wildlife conflicts and (2) the community perceptions on the compensation schemes set up to minimize the conflicts. For this purpose, a qualitative method was adopted. It relied on field semi-structured interviews with the representatives of conservation key stakeholders including local communities that experienced crop raiding. On one hand, the findings indicated that communities affected by generalized crop raiding are complaining about socio-economic and livelihood losses, food insecurity and injury. On the other hand, they showed that affected communities are not satisfied with the compensation scheme. Difficult requirements for compensation, complicated compensation procedure, rejection of compensation claims, undervalued payment and irregular compensation and delay of payments were identified as key problems that keep human-wildlife conflicts at high levels. The compensation process is so slow, so long and so costly that the victims of crop raiding abandon compensation claims. As a result, local communities continue to deal with self-defense since monetary compensation is unable to decrease the level of crop raiding. The findings call for an improved compensation scheme for reduced human-wildlife conflicts and efficient management following the model of effective compensation. Even though fencing the Park could be expensive, it would be a viable option to minimize wild-animals escape from the park. In addition, enhancing collective guiding system was proposed by the local community as their innovative strategy that could reduce crop raiding.
FRAUD DETECTION WITH SOCIAL NETWORK ANALYSIS: ANALYSIS ON IDENTITY FRAUD AND DETECTION SCHEME

Fraud is a crime where the purpose is to appropriate money by an illegal form or method. Fraud leads to large losses to businesses or States or individuals. Since its detection is complex, there is not yet a fraud detection framework that can detect and prevent fraud in an efficient& accurate way. Almost any business enterprise that involves money and services can be compromised by fraudulent acts. Such areas as Capital Markets, Social Security, Insurance, Telecommunications, Financial Institutions, and Credit Cards are examples of where fraud may occur and where, in the recent past, there has been an effort to develop methods to combat this type of financial fraud. Each one of the areas has specific characteristics; therefore, a single solution that may be deployed to fight fraud in Credit Cards cannot be applied to Insurance industry. As a consequence, one of the approaches to fight fraud is to pursue a model that describes fraudulent behaviors, or, better, create mechanisms that distinguish fraudulent from non-fraudulent behaviors.
Assessment of the level of some physico-chemical parameters and heavy metals of Rebu river in oromia region, Ethiopia

Abbreviations: ANOVA, analysis of variance; APHA, american public health association; CCME, Canadian council of ministers for environment; COD, chemical oxygen demand; DO, dissolved oxygen; EC, electrical conductivity; EDWQ, Ethiopian drinking water quality; EMA, Ethiopian mapping agency; ICMR, Indian council of medical research; ISO, international standard organization; SD, standard deviation; SPADNS, sodium 2-(para sulphonate)-1, 8-dihydoxy-3, 6-naphthalene disulphonate; SPSS, statistical package for the social science; TDS, total dissolved solid; US-EPA, united state environmental protection agency; WHO, world health organization
MODIFICATION TWO-DIMENSIONAL DIFFERENCE-HISTOGRAM BASED ON REVERSIBLE DATA HIDING

— Reversible data embedding is also known as lossless data embedding in which invisible data (also known as payload) embeds into a digital image in a reversible fashion. This technique can be used for media notation, copyright security, integrity validation and covert communication etc. The present paper is discussed on various RDH methods in which PSNR has been calculated and it is used for covert communication. The present paper gives the comparison of different reversible data hiding techniques in terms of PSNR, which shows that the histogram modification scheme is one of the best methods of RDH.