Miracle-dz/UniFAR-FTUs huggingface dataset:
Wavelet Domain Interpolation with Edge Extraction and Sparse Representation generating super resolution image

: In this paper, it is proposed a new image resolution enhancement algorithm based on discrete wavelet transform (DWT), lifting wavelet transform (LWT) and sparse recovery of the input image. A single low resolution (LR) is decomposed into different sub bands using two operators DWT and LWT. In parallel, the LR image is subjected to a sparse representation interpolation. The higher frequency sub-bands in addition to the sparse interpolated LR image are combined to give a high resolution (HR) image using inverse discrete wavelet transform (IDWT). The qualitative and quantitative analysis of this method shows prominence over the conventional and various state-of-the art super resolution (SR) techniques.
A Review on Analysis and Classification of Sentiments using Dual Sentiment Filtration

- In past few years there is rapid growth in internet content. People are interested to express their opinion on any topic. It is difficult to search opinions and notice them and get them analyzed on web as there is availability of large amount of content on web. Also there exists a problem of polarity shifting which changes statement orientation of given texts. For the sentiment analysis the text is to be modeled in statistical machine learning approaches for which one popular technique used is Bag-of-words (BOW). Bag-of-words is very simple and quite efficient in some type of text classification but because of some basic limitations which it has while handling the polarity shift problem, the performance of BOW sometimes remains limited. Then there was also a difficulty while handling more complex polarity shift patterns. For addressing the problem of text classification a model called Dual Sentiment Analysis (DSA) is proposed. In this, firstly a new technique for data expansion is proposed which is generated by reversing the sentiment review for each of the training and test review. On this basis, two algorithms are proposed called Dual Training Algorithm and Dual Prediction Algorithm. The Dual Training Algorithm is one which uses both the original and reversed training reviews in pairs for sentiment classifier’s learning. The other, Dual Prediction algorithm classifies the test reviews by considering two sides of one review. The DSA framework is also extended from polarity classification i.e., positive-negative to the 3-class classification i.e., along with positive, negative the neutral class also. Neutral is also considered along with the existing two positive and negative. It is done by taking the neutral review in consideration. And then finally, for removing the DSA Framework’s dependency on external dictionary for reversing reviews, one corpus-based method is proposed to construct the pseudo antonym dictionary. Along with DSA the proposed system is also going to work towards the use of syntactic construction as features for inconsistency classifier that can help to improve performance.
Sensory Characteristics, Total Polyphenol Content and In vitro Antioxidant Activity of Value Added Processed Barnyard Millet Flour Chapattis

Materials and methods: Roasted and pressure cooked barnyard millet flour incorporated chapattis were developed at four different levels i.e., 10%, 30%, 50% and 100% millet flour to wheat flour. Sensory evaluation was done for all the developed products using 9 point hedonic scale. For the best variation, the total polyphenol content was estimated using folic ciocalteau method and the in vitro antioxidant activity were evaluated on the basis of measuring Ferric reducing ability power (FRAP) and scavenging activity for DPPH radicals by methanolic extracts.
Phyllodes Tumor Presenting with a Rare Widespread Abdominal Metastases: A Case Report and Review of the Literature

Phyllodes tumour is a rare breast neoplasia. The malignant variant is even rarer and it is unusual for abdominal metastases to occur sparing the more contiguous chest cavity and lungs. The patient presented with abdominal pain and distension, protracted vomiting and rapid weight loss five weeks after simple mastectomy for a bosselated, fungating right breast lesion. Exploratory laparotomy was carried out for the unexplained acute abdomen and it revealed an inoperable, generalized abdominal seedlings. This report illustrates a rare case of metastases at an unusual site from malignant phyllodes tumor occurring shortly after simple mastectomy.
Nasal Carriage of Staphylococcus aureus and MRSA among Companion Animals and Livestock in Wayanad District, India

Bacteria remain the most enduring life forms on earth that has developed various mechanisms to surpass adversities. Through the advancement in medicine, antibiotics were discovered to counteract bacterial infections. However, multi-drug resistance (MDR) is now an emerging issue in the management of such infections and Staphylococcus aureus is a predominant pathogen that has developed resistance to antibiotics. S. aureus is a International Journal of Current Microbiology and Applied Sciences ISSN: 2319-7706 Volume 8 Number 12 (2019) Journal homepage: http://www.ijcmas. com
Multiclass Emotion Extraction from Sentences

— This paper aims to investigate the extraction of different classes of emotion from sentences using supervised machine learning technique, Multinomial Naïve Bayes (MNB). Here a bag of word approach is used to capture the emotions. The unigrams are mainly used for this and the bigrams and trigrams are used to capture lower order dependencies. The work is done on the ISEAR dataset [14]. The experiments with different feature sets selected using Weighted log-likelihood score (WLLS) [12] shows that the MNB classifier provides good results when the unigram feature set size is 450 which provides an average accuracy of 76.96% across all emotion classes.
UNIVERSIDADE DO VALE DO RIO DOS SINOS

Av. Unisinos, 950 Caixa Postal 275 CEP 93022-000 São Leopoldo Rio Grande do Sul Brasil Fone: (51) 3590 8476 Fax: (51) 3590 8486 http://www.unisinos. br 1 IDENTIFICAÇÃO Programa de Pós-Graduação em Engenharia Civil Disciplina: Estudos avançados em tecnologia do concreto Ano/Semestre: 2016/2 Carga horária total: 45h Carga horária teórica: 45h Carga horária prática: -Créditos: 3 Área temática: ENGCIVL Código da disciplina: 114475 Professor: Mauricio Mancio
Optimal treatment strategy in KRAS wild type (wt) metastatic colorectal cancer (mCRC): Cetuximab plus FOLFIRI followed by FOLFOX4 with or without cetuximab-The Capri trial from the Gruppo Oncologico Dell’Italia Meridionale (GOIM).

e14565 Background: Cetuximab in combination with standard doublet chemotherapy is an effective treatment in KRAS wt mCRC patients (pts). The present study investigated a treatment strategy of cetuximab in combination with FOLFOX4 as a second line therapy for KRAS wt mCRC pts treated in first line with cetuximab plus FOLFIRI. Methods: KRAS wt m CRC pts were treated in first line with cetuximab plus FOLFIRI and at progression were randomized (1:1) to receive FOLFOX alone (Arm A) or in combination with cetuximab (Arm B). Primary endpoint for each treatment line was progression-free survival (PFS), with secondary endpoints overall survival (OS), response rate (RR), disease control rate and safety. 221 events would be required to statistically differentiate PFS between the two arms with 80% power for the second line treatment. Here we report the results of first line cetuximab plus FOLFIRI treatment. Results: From July 2009, 344 pts were enrolled: 58% males, median age 63 years (range, 20-81 years), ECOG perfo.
SURVEY OF SUPERVISED CLASSIFICATION FRAMEWORK DEVELOPMENT ON HETEROGENEOUS NETWORK

--------------------------------------------------***--------------------------------------------------- Abstract Network Traffic Analysis (NTA) in heterogeneous networks is one of the emerging research areas receiving substantial attention from both the research community and traffic analyzers. Many tasks in NTA can be naturally cast in a supervised and unsupervised learning model. Many supervised classification models and unsupervised clustering learning models in data mining have been proposed for heterogeneous network. Due to the importance of network traffic analysis in data mining research with the rapid development of new models, we provide a comprehensive review on supervised classification on heterogeneous network. In this paper a systematic analysis and comparison of various research works conducted using supervised classification models for network traffic analysis is presented. It addresses the problem of network management such as traffic load, quality of service, and trend analysis. This survey cover real time supervised classification and analyzes techniques for heterogeneous networks. It provides taxonomy of the different supervised classification algorithms and evaluates the various performance metrics that are significantly used for the purpose of comparison. A detailed review is provided covering classification learning algorithms, global voting algorithm and hybrid algorithms. The survey
Comparison of ANNs, Fuzzy Logic and Neuro-Fuzzy Integrated Approach for Diagnosis of Coronary Heart Disease: A Survey

— Data mining is an art of searching large databases to discover useful patterns and trends that go beyond simple analysis. Data mining plays an important role in health care. Health care industry comprises of large amount of data which has to be refined in order to get useful information from it. In this paper, we compare three techniques for mining health care data to predict whether a person suffers from coronary heart disease or not. These techniques are: ANNs, fuzzy logic and neuro-fuzzy integrated approach. Although ANN and fuzzy logic have a lot of advantages but they have some disadvantages too. Neuro-fuzzy is a combination of advantages of ANN and fuzzy logic. By comparing all these techniques, we conclude that neuro-fuzzy integrated approach is the best among these three techniques for diagnosis of coronary heart disease.
A Review on Frequent Pattern Mining

- Data mining is the technique that discovers hidden pattern in data sets and association between the patterns. In this association rule mining one of the techniques used to achieve the objective of data mining. These rules are effectively used to uncover unknown relationships producing result that can give us a basis for forecasting and decision making. To discover these rules we have to find out the frequent item sets because these item sets are the building blocks to obtain Association rules with a given confidence and support.
Optimal Bidding Strategy for GENCO in Deregulated Electricity Market by Using Particle Swarm Optimization (PSO)

1PG Student, Department of Electrical Engineerin, Jabalpur Engineering College, Jabalpur, India 2Assistant Professor Department of Electrical Engineerin, Jabalpur Engineering College, Jabalpur, India ---------------------------------------------------------------------***--------------------------------------------------------------------Abstract – Bidding strategies are highly associated with profit maximization. In deregulated electricity market GENCOs (suppliers) and large consumers (buyers) need a optimal bidding model to maximize their profits. Therefore each GENCO and large consumer bids strategically for selection of bidding coefficient to check out rivals bidding strategy. In this paper bidding strategy problem is modeled as an optimization problem and solved by using particle swarm optimization (PSO) method. PSO has many similarities with evolutionary computational techniques such as Genetic Algorithm (GA). However unlike GA, PSO has no evolution operators such as mutation and crossover. The proposed method is tested with IEEE-30 bus system in which 6 generators and 2 loads are considered. Results are compared with the solution obtained using Monte Carlo method. Test results indicate that the proposed algorithm gives more profit, and more reliable than Monte Carlo approach.
DESIGN OF 320/321 PRESCALER CIRCUIT USING 4/5PRESCALER

: In this paper, Frequency divider by 2 circuit using D-flip-flop is done, based upon this a4/5 prescaler, 16/17 prescaler,32/33 prescalerareverified. Along with it the design of wide band multimodulus 320/321prescaler.CMOS single phase clock pre-scalar,wideband single phase clock 2/3 pre-scaler are also designed. A dynamic logic multiband flexible integer-N divider is designed which uses the wideband 2/3 prescaler, multimodulus,4/5 prescaler, 32/33prescaler. Frequency synthesizer is implemented by using a phase-locked loop (PLL), and it is one of the power-hungry blocks in the RF front-end modules. The first-stagein a frequency synthesizeris the frequency divider which consumes a large portion of power The proposed multiband flexible dividerprovides a solution to the low power PLL synthesizers for various wireless communication devices
A robust mean shift integrating color, GLCM based texture features and frame differencing

- Mean shift is a kernel based widely used algorithm for tracking the location of object robustly. Classical mean shift uses color histogram to represent the object. However, the use of only color restricts the algorithm to track the object only in simple cases and it fails in complex situations like illumination changes, occlusion, and abrupt changes in the location of object. To improve the performance of mean shift, some authors have added some features to basic mean shift. As color based target representation is combined with texture-based target representation, based on spatial dependencies and co-occurrence distribution within interest target region for invariant target description, which is computed through so-scaled Haralick texture features, is an efficient mean shift for target tracking in some real world complex conditions. But it still fails, if there are some abrupt changes in the location of object. So a novel algorithm is presented in this work, which is robust to track the object in above mentioned complex situations. It is the combination of color and gray level co-occurrence matrix based texture features along with the use of frame differencing for abrupt motion changing target detection. Many experimental results demonstrate the successful of target tracking using the proposed algorithm in many complex situations, where the basic mean shift tracker obviously fails. The performance of the proposed adaptive mean shift tracker is evaluated using the VISOR video Dataset, creative common dataset and also some proprietary videos.
Susceptibility of Egyptian native breed chickens (Gmisa) to experimental infection with highly pathogenic avian influenza virus (H5N1)

Highly Pathogenic Avian Influenza (HPAI) H5N1 virus is an ongoing public health and socio-economic challenge, particularly in Egypt. H5N1 is now endemic in poultry in many countries, and represents a major pandemic threat and have caused multiple human infections. The main objective of this work to highlights on the susceptibility of Egyptian Gimazachickens to experimental infection with H5N1 avian influenza virus and to get a comprehensive view of Gimaza adaptation to H5N1 virus. Nucleotide and amino acid alignment of HA gene after serial passage in Gimaza chickens revealed no changes in cleavage site and receptor binding site. Only few amino acid substitutions in some antigenic sites were recorded. Gimaza breed appeared to be more sensitive to H5N1 Giza strain as evaluated by the MDT and virus shedding. [Saleh M.M, Selim A. , Shahera A.A. , El Shahidy M.S. and ElTrabilli M.M. Susceptibility of Egyptian native breed chickens (Gmisa) to experimental infection with highly pathogenic avian influenza virus (H5N1). Nat Sci 2017;15(7):95-104]. ISSN 1545-0740 (print); ISSN 2375-7167 (online). http://www.sciencepub. net/nature. 13. doi:10.7537/marsnsj150717.13.
QoS Routing Method for Wireless Multimedia Sensor Networks

 Abstract — The advent of wireless multimedia sensor networks has opened a new perspective on existing wireless sensor networks by upgrading its existing capabilities (due to the integration of visual sensor and high-computing engines) and applications such as intelligent transportation systems, wireless multimedia sensors networks (WMSN), and so on. However, there are many challenges in these networks that require attention and finding an appropriate approach to address them. Transferring multimedia data while considering the parameters of service quality and the issue of resource limitation can be mentioned as one of the most important of these challenges. Therefore, routing in wireless multimedia sensor networks has long been considered as one of the most important and crucial problems, and the network optimal performance is not possible without a proper routing approach. In this paper, a novel approach is proposed for routing in wireless multimedia sensor networks using intelligent capabilities and using meta-exploration algorithms in such a way to improve the overall network performance. The goal is to optimize some service quality parameters, such as energy consumption, network lifetime and energy consumption balancing. We have achieved all these three goals. Especially in order to reach our third goal, we have defined a new criterion called the ratio of the instability period and shown that the duration of the instability period is short in the proposed method. In fact, the nodes have balanced energy consumption in the proposed method and they die almost together.
An Adaptive Cluster Validity Index for the Fuzzy C-means

Summary Based on the basic theory of fuzzy set, this paper suggests the notion of FCM fuzzy set, which is subject to the constraint condition of fuzzy c-means clustering algorithm. The cluster fuzzy degree and the lattice degree of approaching for the FCM fuzzy set are presented, and their functions in the validation process of fuzzy clustering are deeply analyzed. A new cluster validity index is proposed, in which the two factors such as the cluster fuzzy degree and the lattice degree of approaching are taken into comprehensive account. The notable advantage of the index is that it can adaptively adjust the relative significance levels of the two factors. Also, this paper gives the algorithm to apply the cluster validity index to the cluster validation for the fuzzy c-means algorithm. The experimental results indicate the effectiveness and adaptability of the proposed cluster validity index.
A Hybrid Approach of Intrusion Detection using ANN and FCM

The rapid development and expansion of World Wide Web and local network systems have changed the computing world in the last decade. However, this outstanding achievement has a drawback. The highly connected computing world has also equipped the intruders and hackers with new facilities for their destructive purposes. The costs of temporary or permanent damages caused by unauthorized access of the intruders to computer systems have urged different organizations to increasingly implement various systems to monitor data flow in their networks. These systems are generally referred to as Intrusion Detection Systems (IDSs). There are two main approaches to the design of IDSs. In a misuse detection based IDS, intrusions are detected by looking for activities that correspond to known signatures of intrusions or vulnerabilities. On the other hand, anomaly detection based IDS detect intrusions by searching for abnormal network traffic
Predicting the Analysis of Heart Disease Symptoms Using Medicinal Data Mining Methods

– Medicinal data mining methods are used to analyze the medical data information resources. Medical data mining content mining and structure methods are used to analyze the medical data contents. The effort to develop knowledge and experience of frequent specialists and clinical selection data of patients collected in databases to facilitate the diagnosis process is considered a valuable option. Diagnosis of heart disease is a significant and tedious task in medicine. The term Heart disease encompasses the various diseases that affect the heart. The exposure of heart disease from various factors or symptom is an issue which is not complimentary from false presumptions often accompanied by unpredictable effects. Association rule mining procedures are used to extract item set relations. Item set regularities are used in the rule mining process. The data classification is based on MAFIA algorithms which result in accuracy, the data is evaluated using entropy based cross validations and partition techniques and the results are compared. Here using the C4.5 algorithm as the training algorithm to show rank of heart attack with the decision tree. Finally, the heart disease database is clustered using the K-means clustering algorithm, which will remove the data applicable to heart attack from the database. The results showed that the medicinal prescription and designed prediction system is capable of prophesying the heart attack successfully.
Frontal Enhanced Face Detection using Skin Pixels and ANN

With the growth of internet visual data like images, have attracted many researchers for searching the face in images from the collection. This paper focuses on the face detection of images by utilizing the visual features. Here Skin probability map is created for skin pixels. Skin pixels searching are done on the basis of passing image from the trained neural network and geometrical feature utilization. Experiment is done on standard and artificial dataset. Results show that proposed work is better for single as well as multi-face detection in given image.
Impact of AM fungi on biochemical changes in potato plants

Mycorrhizal occurrence in terrestrial plants has been long known and it is now a known fact that AM fungi positively influence plant growth. The term Mycorhiza was coined by Frank (1885) for the non pathogenic, symbiotic association between roots and fungi. These are members of the fungal group that mutualistically associate themselves with plant roots. The relation is mutualistic due to the increased mineral availability to plants by activity of AM fungi and a complementary nutritional and habitat security offered by the plant member.
Semi Supervised Learning of Online Data Streams with Max Flow Algorithm

: This paper aims to propose the semi-supervised learning system that deals with huge and dynamic data in time and memory efficient manner. In today’s world the numbers of applications are available in information system which deals with large amount of data in changing environment. In such environments, this data is available in the form online streams. The memory and time limitations are the major aspects which need to be considered in online learning which are due to volume as well as speed of the data. In online streams, small quantity of information is labelled and huge quantity of unlabelled information is accessible. Consequently, semi regulated learning is the best way to deal with take in the information in order to decrease human efforts and as yet accomplishing better precision also the performance of the system. This paper examined the concept of semi supervised learning. For learning the data stream max flow algorithm is used in this paper. The Max Flow/ Min Cut algorithm is applied for achieving the accuracy of the proposed system. The algorithm shows improved efficiency and enhancement in performance. To further enhance the performance, Cosine Similarity and Feature Selection Techniques are used. The comparative study shows that the proposed system gives better performance in terms of time and memory efficiency than the old learning system. The system is tested using KDD99 dataset for classification of network intrusion attacks. The system shows the higher accuracy and improved performance results. Application: The proposed system gives solutions to various two class problems. The applications such as network intrusion detection system which can be used to classify anomaly and normal network, online audio background noise reduction of streams