Miracle-dz/UniFAR-FTUs huggingface dataset:
High Performance Multicast Routing Protocol in Mobile Ad-Hoc Network

paper intends to improve the performance of MANETS - Mobile Ad-hoc networks handling high volume traffic by introducing an enhanced tree based multicast routing protocol with efficient management of topology and groups with an improvised label mechanism capable of building alternate paths and secure better transmission stability. A comparative analysis of the simulation results have shown that the proposed protocol is better performing than the existing multicast routing protocols with slightly higher control overhead.
Efﬁ cient Data Cleaning Algorithm and Innovative Unique user Identiﬁ cation Algorithm using

: A novel technique, fuzzy based particle swarm optimized histogram equalization has been proposed for enhancement of mammographic images. The novelty of the proposed work is the automatic enhancement of the mammograms by fully adaptive calculation of the parameters based on the characteristics of the image. This technique consists of three stages. In the ﬁ rst stage, the grey level intensities of the mammographic images are transformed to an adaptive fuzzy domain with values ranging from 0 to 1. In the second stage, weighted threshold histogram equalization is applied to the fuzzy set where the optimal values of the parameters required for computing constrained PDF is obtained using particle swarm optimization. In the ﬁ nal stage, the fuzzy domain is converted back to spatial domain. Performance of the proposed algorithm is evaluated using well known image quality assessment techniques like SSIM, NCC, UIQI and DE. Experimental results show that proposed method being adaptive to different mammographic images provides controlled contrast enhancement while maintaining the richness of details and preserving the brightness level.
Emotion Recognition With Facial Expressions Classification From Geometric Facial Features

This paper presents a novel emotion recognition model using the system identiﬁcation approach. A comprehensive data driven model using an extended self-organizing map (SOM) has been developed whose input is a 26 dimensional facial geometric feature vector comprising eye, lip and eyebrow feature points. The analytical face model using this 26 dimensional geometric feature vector has been effectively used to describe the facial changes due to different expressions. This paper thus includes an automated generation scheme of this geometric facial feature vector. This also include a method like single input and single output, by giving single image as an input we can obtained a result of its specific expression which is observed, we also able to observe the confusion matrix of all the images which were used in training in which the overlapping of their expressions are given. This paper gives the provision of using real time images as an input test image.
Design and Implementation of MIMO-OFDM us-ing Encoding and Decoding techniques on FPGA

— In this paper, we present a memory-efficient and faster convolution encoding and adaptive viterbi decoding implementation technique for MIMO-OFDM communication systems on FPGA. A method for the 1/3 convloution encoder and Modified viterbi decoder design used for coding techniques on FPGA. Our design utilizes the minimum required on-chip memory for the MIMO-OFDM implementation. Using the proposed design method, the I/O rate for MIMO-OFDM based communication systems are doubled for 2 × 2 MIMO systems. The whole design is implemented using Xilinx ISE 13.4 using VERILOG on Vitrex 5 FPGA and simulated using Modelsim 6.3f.
BRAIN MORPHOMETRY FOR IMAGE-BASED CLASSIFICATION OF NEURODEGENERATIVE DISEASE

The modern tomographic imaging methods are playing very good role to detect the brain structure and function and also in understanding the way in which these changes occur during the development. The information obtained through the analysis of brain image help to differentiate the normal one. The brain diseases including Alzheimer’s Disease (AD), introduce tissue loss that is believed to be regionally specific. The functional changes can often be identified early in the disease process different diagnosis and treatment decision difficult moreover imaging based structural measurements. These more advance morphological brain changes are being explored as possible to surrogate markers of therapeutic efficiency of potential treatments to stop the progression of AD. Therefore, more advanced methods of morphometric analysis could potentially help diagnose the disease even earlier, before measurable functional loss occurs and at stage in which treatment might be more and at stage in which treatment might be more effective and may help to monitor the efficiency of interventions in clinical trials.
Implementation of Automatic Color Mixing and Filling Using PLC & SCADA

: The objective of this paper is to design, develop and monitor “Automatic Color Mixing and Filling Using PLC and SCADA”. A modern programmable logic controller deliver a wide range of functionality, including basic relay control, motion control, process control, and complex networking, as well as being used in Distributed Control Systems. There are several different types of interfaces that are used when people need to interact with the programmable logic controller to configure it or work with it. In this application, going to mix the color in different proportion and fill in a bottle using PLC (Programmable Logic Controller) and SCADA (Supervisory Control and Data Acquisition ). Here Allen Bradley Micrologix 1400x PLC is used, which acts as the heart of the system. The system sequence of operation is designed by ladder diagram. Several electronics and electric devices that usually be controlled and operated by the PLC such as a submersible motor pump, conveyor belt, solenoid valve, relays etc. This system is applicable in color mixing plant, milk industries, chemicals, foods, mineral water and many more manufacturing, processing and packaging industries. A prototype has been developed to illustrate the application.
Isolation, Identification and Characterization of Efficient Cellulolytic Fungi from Natural Resources

The isolation and screening of efficient cellulase producing fungi from natural sources was studied in this work. This research was aimed to isolate cellulolytic fungi with overproduction of cellulase components. Efficient Cellulase-producing fungi were isolated from different samples like soil, compost, decayed lignocellulosic waste etc using different isolation strategies. A novel methodology of isolation and primary screening of cellulolytic fungi was adopted by exposing carboxy methyl cellulose agar, which is a selective media for cellulolytic microorganisms, directly in the environment which is rich with cellulolytic microbes. Among the various isolates obtained from different environment, five different fungi were selected depending upon the diameter of clear zone produced in Carboxy methyl cellulose agar for further screening in liquid media and one potent strain NASC3 was identified as efficient cellulolytic fungi. Molecular identification of strain NASC3 was done by PCR amplification of 18s rDNA region using primers ITS4 and ITS5. The amplified products were sequenced and analyzed using ClustalW . The phylogenetic analysis of strain NASC3 showed highest homology with Penicillium citrinum. These Cellulase positive isolates may be an integral part of future work to develop good cellulases or produce efficient cellulase producing systems such as microbial consortia which can be used for industry. Isolation and characterization may also provide a good starting point for the discovery of such beneficial enzymes.
SVM CLASSIFIER ALGORITHM FOR DATA STREAM MINING USING HIVE AND R

: Big data is a challenging functionality for analyzing the large volume of data in the IT deployment in a different dimension. To make that analysis process in more efficient manner we use Hive tool for query processing and providing statistical report using RStudio. The processing load in data stream mining has been reduced by the technique know as Feature Selection. However, when it comes to mining over high dimensional data the search space from which an optimal feature subset is derived grows exponentially in size, leading to an intractable demand in computation. To reduce the complexity of using accelerated particle swarm optimization. (APSO), we connect the data by using Hadoop technology. Hadoop technology is easier to store and retrieve the data in a big data environment. With the dataset the data’s are analysed and the statistical report is produced using SVM algorithm in R software where R language is used. This R-software environment is used to provide a statisical computing and graphics. This statistical report compares the accuracy between the linear and non linear grid where the higher accuracy dataset is efficient. The final graph provides combination of the linear and nonlinear with respect to cost and sigma which is the userdefined value. PSO with SVM algorithm increases the performance of analysing the data.
Hand Gesture Recognition to Speech Conversion in Regional Language

- Generally deaf-dumb people use sign language for communication, but they find difficulty in communicating with others who don’t understand sign language. Due to which communications between deaf-mute and a normal person have always been a challenging task. We propose to develop a device which can convert the hand gestures of a deaf-mute person into speech. This methodology provides a map for developing a Digital wireless glove which is fitted with Flex sensors and accelerometer. These sensors sense the gestures of a person in the form of bend of fingers and tilt of the hand fist. This system includes a voice playback IC to give the real time speech output in regional language as well as a LCD module to display the text. The text display being in English, the voice output of this device will be in regional language (here Marathi). So this device acts as a communicator as well as a translator providing more flexibility in communication.
BCI based EEG Signals for Emotion Classification

: The human brain which is the central processing unit of the human machine is responsible for multiple tasks such as perception, cognition, attention,emotion, memory and action. In human life emotions significantly affect one’s wellbeing. Providing methodologies to access to human emotions would be a key for successful human machine interaction. Understanding Brain Computer Interface (BCI) techniques to identify the emotions also help in aiding people to interact with the world like a common man. Many techniques were devised to identify the human emotions of which usage of EEG signals to classify the emotions as happiness, fear, anger and sadness were found promising. These emotions are evoked by many means such as showing subjects pictures of smile and cry facial expressions, by hearing to emotionally mixed audios or by watching videos and at time combination of these.This paper is a survey of all the optimized methods to filter the EEG signal and comparative study of the various classification methods used to classify the emotions is carried out and a multimodal classification technique which makes use of EEG signals and at the same time efficiency is measured with Natural Language Processing(NLP) is proposed for improving the accuracy.
Mindfulness, Democracy, and Education.

In this article, we explain how mindfulness can enhance a democratic way of being, connecting practices of awareness, reflection, dialog, and action to democratic citizenship and social arrangements. We begin by sharing our understanding of democracy as a philosophy and a political system. We then provide a background for the concept of mindfulness as it is used by those in the field of education and health care and as we connect it to democracy and democratic education. We introduce a mindfulness pedagogy and use this pedagogy to develop our concept of mindfully democratic schools. We use the work of John Dewey and Paulo Freire, along with other philosophers of education, to address concerns that mindfulness is fundamentally an apolitical and individual practice rather than a social practice. We end by sharing how mindful practices are imagined and supported within democratic public schools with three vignettes from the Coalition of Essential Schools, whose vision and principles promote teachers’ and students’ mindfully democratic action. In doing this, we describe the types of schools that we see as necessary for our vision of democracy. We invite readers to offer their own stories of mindfully democratic schools. In this article, we explain how mindfulness can enhance a democratic way of being, connecting practices of awareness, reflection, dialogue, and action to democratic citizenship and social arrangements. Democracy itself is a contested concept, and one that we feel should remain unfinished, as democracy is always in the making (Dewey, 1916). With this in mind, we begin by sharing our understanding of democracy as a philosophy and a political system. We then provide a background for the concept of mindfulness as it is used by those in the field of education and health care and set the boundaries for how we will use the term here, as we connect it to democracy and democratic education. We introduce a threepronged mindfulness pedagogy that includes mindful teaching, teaching mindfulness, and engagement in mindful practices, and use this pedagogy to develop our concept of mindfully democratic schools. We use the work of Dewey and Freire, along with other philosophers of education, to address concerns that mindfulness is fundamentally an apolitical and individual practice rather than a social practice. Though we are Andrea Hyde is an associate professor at Western Illinois University. Andrea studies schoolbased yoga and mindfulness programs in K– 12 and higher education and relates this work to critical pedagogy and social justice education. Jim La Prad is an associate professor at Western Illinois University. His teaching and scholarship include experiential education, philosophy of education, multicultural education, and transformative educational leadership. He is the 2008 National Society for Experiential Education’s Higher Education Leader of the Year. Submit a response to this article Submit online at democracyeducationjournal.org/home Read responses to this article online http://democracyeducationjournal.org/home/vol23/iss2/2
Bioremediation of Crude Oil Polluted Soil Using Biofertilizer from Nitrogen-fixing and Phosphate-solubilizing Bacteria

This study examined bioremediation of crude oil polluted soil (PS) stimulated with nitrogen-fixing bacteria (NFB) and phosphate-solubilizing bacteria (PSB). Five set-ups designated as A (500 g PS + 50 g NFB); B (500 g PS + 50 g PSB); C (500 g PS + 50 g NFB+PSB); D (500 g PS + 50 g NPK); and E (500 g PS only: control) were designed. Total petroleum hydrocarbons (TPHs) were monitored for 4 weeks. Toxicity of the biofertilizer on maize plant was determined. The combination of the biofertilizers with normal soil for the ecotoxicity testing was in the following ratios: 100:0, 75:25, 50:50, 25:75, and 0:100. The NFB were classified as Azotobacter sp. and Rhizobium sp. while the PSB identifies as Pseudomonas and Bacillus using their 16S rRNA gene sequences and deposited in GenBank under the accession numbers MN134485.1-MN134488.1. After 28 days study, TPH reductions were 97.8%, 97.5%, 94.3%, 92.1%, and 34.6% in NFB, NFB+PSB, PSB, NPK treatments, and control. There was significant difference (P<0.05) between the set-ups when compared to the control. For toxicity testing, the 25:75 concentration in all treatment set-ups best supported plant growth. It was concluded that biofertilizer is effective in remediating oil contaminated soil and in improving soil fertility.
The best estimation for shift duration in tunnel excavation using stochastic simulation

— For the mining industry tunnels is fundamental to allow access to the mineralized zones and prepare the exploitation units for the later extraction of the mineral. Therefore, every aspect that improves the time required for the construction of these kinds of infrastructure would favor any mining project, and one of the most sensitive aspects for estimating the time taken for the construction of a tunnel is the configuration of the work shifts. This study deals with three types of shift configurations, which correspond to those used in Chile. A stochastic simulation algorithm based on the Monte Carlo method and on Markov's Chain theory is proposed to find the shift configuration that improves the tunnel construction time. The tunnel excavation cycle was characterized statistically considering a probability density function for every unit operation, and through a simulation algorithm developed for that purpose it will represent the tunnel excavation time associated with a histogram of the process. After evaluating all the existing shift configurations, it is possible then to decide which one is the most favorable for tunnel construction. The result obtained from the model is a histogram of the total tunnel excavation time that is directly related to the duration of the shifts. This model was applied in the San Pedro mine.
Machine Learning Techniques used for the Detection and Analysis of Modern Types of DDoS Attacks

- Distributed Denial of service (DDoS) attacks is the most devastating attack which halts the normal functionality of critical services provided by the various organizations in the internet community. These attacks have become more sophisticated and continue to increase in number day by day, thus making it difficult to detect and counter such attacks. Therefore there is a need of intelligent intrusion detection system (IDS) to detect and classify any anomalous behavior of the network traffic. In this paper, the work is carried out on the new dataset which contains the modern type of DDoS attacks such as (HTTP flood, SIDDoS). This work incorporates various machine learning techniques for classification: Naïve Bayes, MLP, SVM,
Trend Analysis and Prediction of Air and Water Pollutants using Regression algorithm SMOreg

Air and water pollution has become very common problem and it can be one of the factors affecting human health. Because of the bad performance of the traditional modelling methods in prediction, in the present paper, the author adopt SMOreg to study the pattern of data, and also its future trend. The author proposes a novel method called SMOreg. The result shows that the new method improves the accuracy of prediction. This paper proposes a scalable, flexible, economic model which will be useful to identify the air and water pollution pattern and also show its future trend and it will also help to visualize environment data and extract meaningful patterns from the environment data so that it will allow the policy makers to take the remedial measures. Java based environment data mining systems enable Non Government Organizations or policy makers to fully exploit their environment analytical capacity when and where it is needed.
Extraction and HPLC Analysis of Sage (Salvia officinalis) Plant

In this study, the sage (Salvia officinalis) plant collected from the Mediterranean region was extracted with different solvents and methods. The extract yields were compared. The quantities of rosmarinic acid, carnosic acid and carnosol, which are responsible for the antioxidant capacity of the sage plant, were analyzed qualitatively and quantitatively by an HPLC. Ground sage plant were sieved with 70 mesh sieves. Maceration were done for powder sage plant at 45°C with 70% ethyl alcohol in “1:6” (w/v) ratio at different time (3; 6; 8; 10 hours); 100% methanol and 100% ethanol extractions; Soxhlet extraction efficiencies were compared. The proportions of rosmarinic acid and the amounts of carnosic acid and carnosol were analyzed in the extracted at UV detector and 280 nm wavelength by a Thermo Scientific Ulimate HPLC instrument. As a result of the experiments, a maceration method with 70% ethanol with 25% extraction yield was chosen. As a result of calculations on the standard graphic of rosmarinic acid in analytical standards. It was determined that 7.45 mg "rosmarinic acid"; 3.42 mg “carnosol+carnosic acid” in gram 70% ethanol extract of sage.
Google File System and Hadoop Distributed File System-An Analogy

Big Data has indeed been the word which IT Industry is talking about lately. With advancement of automation and data being processed in real time, it has now become a necessity for companies to look forward to sustainable solutions to store their huge datasets and compute valuable information out of it. High performance computing heavily relies on distributed environments to process large chunks on data. With ever-increasing data, a reliable and easy to use storage solution has become a major concern for computing. Distributed File System tries to address this issue and provides means to efficiently store and process these huge datasets. This paper presents and compares two common distributed processing frameworks involved in dealing with storage of large amounts of data-Google File System (More commonly now known as ‘Colossus’) and Hadoop Distributed File System. Also, it gives an insight to the MAPREDUCE framework common to both GFS and HDFS.
Gender Classification with Weighted Principal Component

 Abstract : Gender Classification in the field of forensic Science becomes essential in the case of criminal investigation. Automated tools can help forensic experts by reducing their manual efforts. Soft computing techniques like Fuzzy Computing, Neural Networks and Genetic Algorithm are all helpful to develop automated tools for human identification. Lateral Cephalogram plays a vital role in Gender Classification from skeletal remains. Principal component analysis (PCA) is a technique that is useful for the compression and classification of data. This study proposes Weighted Principal Components of lateral Cephalogram landmarks as an ideal measure . Also this study recommends BPN as an optimal classifier for Gender Classification from lateral Cephalogram.
Marital Satisfaction, Quality of Life and Psychiatric Morbidity Among Workers in Two Tertiary Institutions in the Niger Delta Region of Nigeria

Citation: Chukwujekwu DC. , et al. “Marital Satisfaction, Quality of Life and Psychiatric Morbidity Among Workers in Two Tertiary Institutions in the Niger Delta Region of Nigeria”. EC Psychology and Psychiatry 8.5 (2019): 373-381. *Corresponding Author: Chukwujekwu DC, Department of Neuropsychiatry, University of Port Harcourt Teaching Hospital, Port Harcourt, Rivers State, Nigeria. Received: April 01, 2019; Published: April 26, 2019