Miracle-dz/UniFAR-FTUs huggingface dataset:
Hybrid Optimization using Genetic Algorithm and Neural Network in CBIR

-Widespread cardinality of images and paintings has made traditional keyword based search, an inefficient method for retrieval of required input image data. When we input an image into the database, then Content-Based Image Retrieval (CBIR) system gets the similar images from a large database for that query image. Implementation of CBIR can be done using features like color, texture and shape, these features are called low level features. In this thesis, using a feed-forward back propagation neural network, classification of images in CBIR system is proposed. At first, the neural network is trained about the features of images in the database. The image features that are used in this CBIR system are color histogram, GRB Pattern and Side loop level normalized r2. There are two steps of BPNN 1. Training 2. Testing the training is carried out using BPNN. On the bases of training, resultant image comes out. So training is the best and crucial step in CBIR system for better recognition of image from database when query image is inputted. In addition to that the back propagation neural network, genetic algorithm has been used to optimize the result set of neural network. GA is configured with a fitness function. The proposed method shows the promising results in terms of precision and recall of image retrieval. The proposed method is applied on the African dataset. The whole result simulation is taken place in MATLAB environment.
Interesting Pattern Generation in Smart Electricity Meter Data Using Subgroup Discovery Algorithm

: At present, data mining and knowledge discovery in electricity meter data suffer from insufficient focus on intelligent data analysis of subgroups whose patterns vary significantly from aggregate patterns present in an entire dataset, lack of effort towards generating intuitively understandable and practically applicable knowledge for industrial practitioners and limited knowledge regarding the link between unusual consumption patterns and household consumers’ socio demographic characteristics. The proposed work overcomes these problems by using pattern recognition which helps to identify unusual consumption patterns within the dataset. Pattern recognition can be accurately implemented using subgroup discovery algorithms. This project work addresses these practically important but technically challenging issues by applying subgroup discovery algorithms to smart electricity meter dataset. K-means clustering is used to partition the dataset instance into usual and unusual consumption patterns. Subgroup discovery is applied on the unusual consumption patterns into discovering interesting relationships between different objects in the set with respect to a property which is of interest to the user the target variable. Subgroups are patterns which are extracted normally represented in the form of rules. Subgroups whose patterns are unusual and whose sizes are large enough are discovered, and their descriptive and predictive models are generated. The performance is evaluated quality measure using accuracy as a measures the support and confidence of the generated rule within the subgroups. The methodologies and algorithms presented for electricity dataset here, generic and applicable to a wider range of data mining problems where predictive analytics.
Diagnosing Intracranial Neoplasms through Magnetic Resonance Imaging & Convolutional Neural Networks

: The paper employs modelling and predicting intracranial neoplasms by implementing convolutional neural networks on magnetic resonance imaging (MRI) scans, and then assessing the probability of a malignant or benign tumor in the region. The model implemented in the paper, a convolutional neural network (CNN), utilizes 193 images of MRI scans of the brain with and without a visible neoplasm to train the weights; while 50 validation and 10 test images are applied to evaluate the effectiveness of the model. The data utilized in the model is, primarily, resized to optimum fitting through computer vision — specifically, contouring and extremity recognition. Further, the data is augmented through the use of data generators which enable the model to achieve the benefits of a larger dataset without compromising the integrity of the primary resonance scans. The model incorporates transfer learning from the reputed [1]VGG16 CNNmodel along with a half dropout layer to ensure maximum reliability and certainty of the outputs. The model is evaluated through the Root Mean Square method obtained from a binary cross-entropy loss observed in the training. The total parameters of the model are 14,739,777 with 25,089 trainable parameters are trained for 30 epochs sub-classed into 50 steps.The predicted values of the model are compared to the pre-determined outputs of the dataset in the training, validation and test data frames. Finally, the paper will analyze possible implementations and steps that may be enacted in conjunction with the approach developed in the research — and others such — to develop a better chance of forecastingand treating the fatal neoplasm.
A Robust Approach for Multi-Modal Image Registration

: Image registration is the process of transforming the different sets of data into one coordinate system, which is required for comparing or integrating the data obtained from different measurements such as different viewpoints, different times , different imaging modalities etc. In medical imaging, multiple modalities of the same subject or organ provide complementary information that is very important for medical diagnosis and computer –aided surgery. In this work, a registration method based on extracting structural features by utilizing the complex phase and gradient-based information is proposed.This method has been tested on different modes.
SYNTHESIS OF COPPER NANOPARTICLES FROM BEE PROPOLIS EXTRACT AND ITS ANTIBACTERIAL PROPERTY

Nature has diverse processes for synthesis of nanoscaled inorganic materials which have contributed to the enhancement of innovative area of research based on biosynthesis of nanoparticles. In the present study, copper nanoparticles were synthesised by using simple method of chemical reduction from ethanol extract of propolis. The biosynthesized copper nanoparticles were characterized with UV-Vis spectroscopy, Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). The UV-Vis spectra confirmed the peak for synthesized copper nanoparticles at 448 nm. The XRD analysis showed that the copper nanoparticles are crystalline in nature. The FTIR measurement was carried out to identify the possible bio molecules responsible for efficient stabilization of copper nanoparticles. These biologically synthesized copper nanoparticles were effectively utilized for antibacterial activity against human pathogens viz, Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, Strptococus mutans, Bacillus subtilis, Klebsella pneumonia, Enterococcus faecalis and Citrobacter spp. The antibacterial activity was tested by well diffusion zone inhibition method. Staphylococcus mutans, Klebsella pneumonia and Enterococcus faecalis exhibited the highest zone of inhibition in copper nanoparticles.
Detection of drug resistance gene expression in Candida albicans isolated from oral thrush of children via real-time PCR technique

The present study was intended to check the candida drug resistance (CDR) gene expression in isolates of Candida albicans (C. albicans) of oral thrush, a disease condition that affects inner lining of mouth of immunosuppressed people especially children. From Al-Diwaniya hospital/Al-Diwaniyah City/Iraq, 30 isolates were obtained as 3 groups of tens. Depending on the age of the children, the isolates were categorized into under one year of age (T1), older than one year of age (T2), and nonpathogenic isolates (C). Using quantitative real-time PCR (qPCR), T1 showed significant (p˂0.05) expression of the CDR1 and CDR2 genes and higher than T2 and C groups. On the other hand, T2 did not reveal any differences (p>0.005) when compared to C group for both genes. The results indicate that the isolates belong to the children of less than 1 year of old activated resistance against some known and used anti-thrush drugs. This study alarms medical officials and encourages future studies of how to overcome this risky problem.
Fabrication Methods, Recent Developments and Applications of Carbon-Carbon Composites (CCC): A Review

1Assistant Professor, Mechanical Engineering Dept., Canara Engineering College, Mangalore, VTU, Karnataka, India. 2Professor, Mechanical Engineering Dept., MITE, Moodabidri, VTU, Karnataka, India. 3Professor, Dept. of Metallurgical and Material Science, NITK, Surathkal, Karnataka, India. 4Scientist ‘E’, Advanced Systems Laboratory, DRDO, Hyderabad, India. ---------------------------------------------------------------------***---------------------------------------------------------------------Abstract Carbon Fibre Reinforced Carbon (CFRC) or CarbonCarbon(C-C) or Carbon-Carbon Composites (CCC) is an exceptional composite material comprising of carbon filaments implanted in a carbonaceous framework. Initially produced for aviation applications its low thickness, high warm conductivity and brilliant mechanical properties at hoisted temperatures make it a perfect material for flying machine brakes, rocket spouts and reentry nose tips apart from a few modern and biomedical applications. The carboncarbon item innovation is adaptable and offers outline adaptability. It withstands temperatures more than 2000°C without significant deformation.
Lightweight Node Authentication and Establishing a Secure AODV protocol in Mobile Ad hoc Network

: A MANET is an independent network communication through a wireless medium without requiring any fixed infrastructure, where a target node is not in the direct range of transmission over a sender node, and where midway nodes are needed in order to forward packets. The midway nodes here are not only the host but also acts as a router for hand over packets. Nonetheless, it is very simple to attack the defenseless character of the ad hoc networks by malicious node like the dropping or hand over of forged data etc. As the need for dynamic network is constantly developing, security problems in the network layer in particular must be properly solved according to MANETs. This paper discusses a series of routing violations and proposes an improvement in AODV's design to authenticate route requests using digital signatures to prevent malicious node attacks.
Character Recognition System for Text Entry Using Inertial Pen

 Abstract — Pen with inbuilt inertial sensors” are new input instruments which may be used as an alternative to keyboard. These devices are used to capture human handwriting or drawing motions in 3D space in real-time and use the sensor data for recognition. Motion trajectory recognition is challenging because different users have different speeds and styles and orientations to write the characters or make gestures. Ultrasonic, infrared and optical sensing were the most popular technologies for obtaining data for recognition. The “Inertial pen” as an input device is proposed for handwritten character and gesture recognition. Inertial consist of a inertial sensors (accelerometer gyroscope and magnetometer), microcontroller, and a wireless transmission module, for sensing and collecting movement data for writing alphabet or making gesture. The sensor data is received and given to the computer which has alphabet and gesture recognition algorithm. The recognition algorithm composes of the procedures of sensor data acquisition, signal pre-processing, feature generation, feature selection, and feature extraction and classification/ decision algorithm. Character recognition accuracy depends mainly on the features selected and classification technics. Accuracy is evaluated by using two classification technics first Simple PNN classifier and second KNN classifier. KNN classifier gave better recognition accuracy of 82%.
HSI Classification : Analysis 1

1 Electronics Dept. 2Asst. Prof. Electronics Dept., AVCOE, Sangamner ---------------------------------------------------------------------***----------------------------------------------------------------------Abstract: Spatial features are extracted for the classification of Hyperspectral Image (HSI) image. For features extraction Local Binary Pattern is used (LBP), Gabor filters used to extract global features. Then two level fusions are applied like feature level & decision fusion. To get classification output Extreme learning machine (ELM) is added. With the use of LBP & ELM we get more efficient& cost effective results.
Conversational Agents Embodying a Character Using Deep Neural Networks

This paper presents a preliminary study on how a deep neural architecture can be used to build a chatbot which simulates a conversation having the style of a given character, including famous personalities. Thus, we analyze how well a sequence to sequence (seq2seq) neural model can learn to generate meaningful responses for open domain dialogue and to imitate a persona. For the experiments, we trained the model to imitate two different personas: Romania's most famous poet, Mihai Eminescu, and Eric Cartman from the South Park series. The model was trained without using any handcrafted rules on an end-to-end dialog and is able to build new utterances to a wide number of user inputs using original distinguished expressions for both characters. However, the generated utterances still need to be improved for a meaningful dialogue.
ROLE OF DIETARY AND LIFE STYLE MODIFICATION IN CHILDHOOD OBESITY

In the modern era of mechanical life, Childhood obesity accounts for major world, as evidenced by its increased prevalence over few years. Imbalance between calorie intake and calories utilised due to various avoidable and unavoidable reasons are the major cause of same. Interrelation shi genetic, behavioural, and environmental causes further worsens the situation. Physical, psychological, and social health problems along with decreased exer tary, lifestyle and intervention strategies should be done to prevent the same and the urgent need of the hour. Hence addressing this major problem this study Sthaulya (childhood obesity). 15 Patients of childhood obesity for present study were selected from OPD and IPD of PG Department of Bal Roga, NIA, Jaipur after screening them for the same by following different diagnostic criteria’s. Promising, significant result was found in clinical, a file, which concludes that patients treated with caloric diet as per age along with dietetic and life style modific tion is beneficial in treating childhood obesity Keyword: Biochemical, Caloric obesity, Life style, INTRODUCTION As evidenced by recent studies conducted worl wide Obesity is emerged as a major public crisis among children as well as adults. definition the percentage of weights for indiv if greater than the maximum expected weight with respect to age is considered as either overweight or obese. Present problem is of increasing concern due to its direct and indirect effects over the normal physiology of the child . Body mass index (BMI), a INTERNATIONAL AYURVEDIC MEDICAL JOURNAL
Australian tertiary students’ attitudes towards youth obesity in educational institutions

Educational institutions have been identified as ideal locations to implement health promotion strategies that aim to prevent and treat youth obesity. However, the tertiary training of future health promoters currently lacks health and nutrition instruction. This study sought to investigate attitudes towards youth obesity and perceptions of the roles of schools and educators in strategies to treat and prevent youth obesity. Participants (n = 155) were tertiary students who enrolled in a general-level health and nutrition elective that housed an intervention to increase awareness of obesity. Baseline and post-intervention responses to the Perceptions of Youth Obesity and Health Education questionnaire were collected and compared spanning the 12 weeks. Agreement that health is linked to being of normal weight and that general educators should have a major role in school strategies did not waver over the course of the intervention. The belief that schools are ideal places for obesity prevention strategies was higher at baseline than post-intervention. It was desired that obesity awareness would increase but perceived importance of involvement of school staff in youth obesity strategies did not increase. Further research is required to investigate the influence of attitudes and perceptions held by future health promoters towards their roles in prevention and treatment strategies on professional practice.
Study of Selection on Performance of Genetic Algorithms

Genetic Algorithms are biologically inspired optimisation algorithms. Performance of genetic algorithms largely depends on type of genetic operators - Selection, Crossover, Mutation and Replacement used in it. The paper focuses on different selection and replacement operators. Selection operator is used to select the individuals from a population to create a mating pool which will participate in reproduction process. Replacement operator decides which individuals stay in a population and which are replaced by removing or replacing some offspring or parent individuals. The paper focuses on three selection operators - Roulette Wheel Selection, Rank Selection and Annealed Selection. Annealed selection is the new approach which blends the exploitative nature of roulette wheel and exploratory nature of rank selection. Generational and µ+λ replacement operators are implemented in this paper. Implementation is carried out using MATLAB code on two test problems - Benchmark TSP Eil51 problem and Benchmark DeJong's Sphere Function (F1). The paper compares the performance of genetic algorithm using The paper compares the performance of genetic algorithm using these three selection approaches with generational replacement and µ+λ replacement. The results are optimistic and clearly demonstrate that the genetic algorithm with µ+λ replacement is better than the one with generational replacement. Out of the three selection operators, annealed selection outperforms the other two.
Molecular Characterization of Different Strawberry (Fragaria x ananassa Duch.) Cultivars Growing under Mid Hill Conditions of Himachal Pradesh

Genetic similarity of fifteen strawberry cultivars was assessed using RAPD and ISSR molecular markers. The DNA of the fifteen strawberry cultivars were extracted and amplified by PCR with RAPD and ISSR primers. The DNA Fragments were separated in agarose gel for RAPD and in polyacrylamide gel for ISSR markers. The genetic similarity matrix was estimated by Jaccard coefficient. Studied cultivars were found to be coherent into two groups. RAPD markers projected more coherence with origin and genealogy of the cultivars. ISSR markers projected more similarity between ‘Chandler’ and ‘Shasta’ whereas as per RAPD marker ‘Chandler’ and ‘Sweet Charlie’ were more closely related. In molecular assessment Elyana cultivar was found to be marked cultivars as it shows long flowering duration, higher yield and bigger fruits and can be used as a potential genotype to breed cultivars among the locally adapted cultivars.
Success: A divergent phenomenon which need to know for making a software project successful

leads to the understanding of divergent phenomenon of “success” which should properly be addressed by the researchers and technical experts to make software project successful. The rest of the paper is organized as follows: Section II gives an intensive overview of related work (Literature Review). Section III describes the rationale about intended study, formulation of theoretical framework, discussion
Determining Bug Epidemic Patterns Using Decision Tree Algorithm

-Data mining is the process of extracting useful and vital information from large sets of data that involves learning in a practical and non-theoretical sense (Witten et.al, 2011). Research topic involves the application of data mining techniques in agriculture field for predicting future trends such as bug epidemic. This acquires algorithms for describing hidden patterns frequently from large sets of data. Applications of appropriate data mining techniques are advised to apply to deliver productive decision making (Tripathy et.al, 2012). This application is known as Insect Epidemiology Data Mining (IEDM). IEDM is an additional exploration of Discrete Mathematics and Theoretical Computer Science (DIMACS) that aims to provide an opportunity to develop and test problem instances and other methods of testing and comparing performance of algorithms (Abello and Cormode, 2004). Researchers nowadays are able to offer number of information on agricultural-related activities which then can analyze the potential data mining and collect the relevant information. Data mining in agriculture requires historical data that helps to create automated prediction and analysis of various trends and behaviors to provide relative solutions. However, data mining in agriculture can be used to improve the agricultural production such as rice crop and awareness in bug infestations. Discovering patterns in massive data sets will enable to predict possible outcomes by using this technique. This will be helpful for the agricultural farmers in forewarning about bug epidemic and enables to identify the factors that influence the pest population density. Farmer benefits will learn more about ecological and sustainable management options for pest epidemic and can apply pest control strategies to reduce crop loss. Technically, data mining in agriculture is a popular in a nontrivial process useful in classification applications since it resembles human reasoning and can be easily understood
A Region based Active contour Approach for Liver CT Image Analysis driven by fractional order image fitting energy

— Computer tomography images are widely used in the diagnosis of liver tumor analysis because of its faster acquisition and compatibility with most life support devices. Accurate image segmentation is very sensitive in the field of medical image analysis.Active contours plays an important role in the area of medical image analysis.It constitute a powerful energy minimization criteria for image segmentation. This paper presents a region based active contour model for liver CT image segmentation based on variational level set formulation driven by fractional order image fitting energy. The neighbouring intensities of image pixels are described in terms of Gaussian distribution. The mean and variances of intensities in the energy functional can be estimated during the energy minimization process.The addition of the fractional order fitting term to the energy functional makes the segmentation more accurate and be robustness to noise.In order to ensure the stable evolution of the level set function a penalty term is added into the model. Also this model has been compared with different active active contour models. Our results shows that the presented model achieves superior performance in CT liver image segmentation
Optimal PMU Placement Using Binary Integer Programming

Optimalphasor measurement units (PMUs) placement involves the process of minimizing the number of PMUs needed while ensuring the entire power system completely observable. A power system is identified observable when the voltages of all buses in the power system are known. Cases with and without the zero injection measurements are considered. The optimal PMU placement problem can be achieved using linear constraints. This implies that optimal PMU placement problem with zero injection busses can be solved by standard Binary Integer Programming (BIP) solvers. Subsequently, a simple and an effective methodology has been presented to handle single PMU outage as well as single line outage in the system.