Miracle-dz/UniFAR-FTUs huggingface dataset:
Review on Mood Detection using Image Processing and Chatbot using Artificial Intelligence

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - Human gesture is the thing which plays a very interesting role in general life application. It can be easily recognize using image processing. Let us consider an example of driver’s gesture who is currently driving the vehicle and it will be quiet useful in case of alerting him when he is in sleepy mood. We can identify the human gesture by observing the movements of eyes, nose, brows, cheeks which may vary with time. The proposed system is introduced for recognizing the expressions by focusing on human face. There were two implementation the approach is based on that is face detection classifier and finding and matching of simple token. There is one more approach we have adapted i.e. chatbot which is built using artificial intelligence. Using chatting application system let the user to chat with bot and this leads to identifying the users’ mood on the basis of text or speech using text processing. Considering the both approaches the system will be able to provide jokes, songs and links to webpages by recognizing the users’ response.
Machine Learning Classifiers- A Survey

Recently there has been a strong development in machine learning classification approaches for analyzing brain activity patterns. The main goal of these approaches is to reveal the information represented in voxels of the neurons and classify them in relevant classes. The functional magnetic resonance imaging (fMRI) has provided researchers with detailed three dimensional images of a human brain, The scanner measures the signal at small volume elements known as voxels (volumetric pixels) and stores them in a three dimensional grid. In this paper, we reviewed various machine learning techniques for classification of large, high dimensional, complex data such as voxels, which have shown promising result in fMRI classification. Further, this paper proposed methodology for designing new improved classifier.
IPL DREAM TEAM - A PREDICTION SOFTWARE BASED ON DATA MINING AND STATISTICAL ANALYSIS

The Indian Premier League (IPL) is an annual Indian Twenty20 cricket tournament, founded in 2008 by the BCCI. Management of an IPL team requires handling of a large amount of data pertaining to the players and team management and the probabilistic values associated with the practical use of the data. Our software, the IPL Dream Team is developed for use by the owner of an IPL team franchise. The software we propose aims at providing a comprehensive solution to the handling of all such complex data and presenting it to the user in a clear, concise and readily usable form thereby eliminating the expenses involved in manual data management.
An Hybrid Genetic Algorithm, Kernel SVM ANFIS Based Multilayer Attack Classification System Size

— An Intrusion Detection System (IDS) gathers information from a network or computer system, and observe the information used for symptoms of system breaks. IDS examine each and every one inbound and outbound network activity and recognize mistrustful patterns with the intention of might designate a network attack beginning somebody effort in the direction of split into or cooperation a system . A predefined threshold value is used in these schemas to designate the stage of normalcy. While there are incidences of latest intrusion events which are more and more a key component of system safety, the arithmetical method cannot distinguish them, some of the classification algorithm has been also proposed to solve IDS problem, but these classification schemas may not perform well because of time to complete the process. To overcome this issue, learning techniques are used which help out in recognize original intrusion activities in a computer system. The objective of the proposed system is to design a new intrusion detection schema by using Swarm Intelligent and other multilayered classification system for Attack detection . This proposed schema used to identify and classify the intruders. In the proposed Intelligent ANFIS Layered Attack Classification System (IALACS), we use an Adaptive Neuro Fuzzy Inference System (ANFIS). The first layer involves Kernel Support Vector Machine (KSVM) classification for detecting the normal and attack. The middle layer involves Neural Network (NN) classification to categorize the attacks into several classes. The third layer involves Genetic Algorithm (GA) to categorize the attacks into various subclasses. The proposed SIANFIS be able to be able to notice an intrusion behavior of the networks, since the IALACS system contains a five intelligent layer classification and
A Texture Segmentation Using Modified Hill-Climbing Approach

— Image segmentation is crucial to object-oriented remote sensing imagery analysis. In this paper, a newly modified texture segmentation algorithm is proposed using spectral, shape and intensity features. This algorithm is a robust technique that can be applied directly to the color images. The image is preprocessed using Adaptive Switching Median Filter, which removes the impulse noises and keeping the fine details of the image intact in the most efficient manner. Also, the preprocessed image is smoothened using morphological operators, which reduces the false detection of abnormal cells. Then, the preprocessed image is transformed into HSV (Hue, Saturation and value) color space representation in order to analyze and establish a color contrast gradient. The multiscale morphological gradient in the intensity channel of the preprocessed image is obtained and multiplied with the color contrast gradient. The shape feature is extracted from the preprocessed image based on the descriptors such as compactness, convexness, rectangularity and eccentricity, moment invariants. Based on these spectral, shape and intensity features, markers are extracted for this image and given as input to the watershed algorithm which uses a Hill-Climbing approach to identify and label the neighborhood pixels. This algorithm may reduce the computational complexity by avoiding the process of computing lower-complete image.
Chalkbrood: pathogenesis and the interaction with honeybee defenses

There are numerous threats that affect bee populations worldwide such as exposure to pesticides; genetic diversity, poor nutrition and the impact of pathogens. Between them, Ascosphaera apis is the etiological agent of chalkbrood disease that affects honeybees brood. To understand the biology of this pathogen, we revised the phylogeny, morphology, and sexual reproduction. The pathogenesis, closely related to the factors that affect the virulence the A. apis and their interactions with the host, are determinant at moment of developing chalkbood. The honeybee develops several strategies to defend themselves from these pathogens. First, the individual immunity mechanisms such us perithrophic membrane, the microbiota of midgut larvae and the humoral and cellular immunity are the first defense barriers against A. apis. Later, other mechanisms would appear, related to the social immunity, such as their social organization, the polyandry, the hygienic behavior and the social fever, that change the environmental conditions in the bee colony reducing A. apis viability. However, other pathogens such as Nosema spp, Varroa destructor, several viruses, and the presence of pesticides affect the sanitary status of the honeybee allowing the fungus to develop easily. Finally, we describe to our knowledge, the best three natural alternatives that could be studied in order to employ them in field trails. Keywords— chalkbrood, epidemiology, honeybees, host ́s resistance, pathogen ́s virulence.
Innate immune mechanisms of controlling respiratory virus infection.

Interferons (IFN) are a family of cytokines that are well known for their ability to inhibit virus replication. IFNs are classified as type I (IFN-α/β), type II (IFN-γ), or type III (IFN-λ) according to sequence homology and receptor binding. The type III IFNs are the most recently discovered and, despite evidence of their antiviral effects in vitro, little is known about their relative importance to antiviral immunity in vivo. It has been believed that IFN-α/β represent the most critical component of innate antiviral immune responses based on reports demonstrating heightened sensitivity to viral infection in mouse models lacking the type I IFN receptor (IFNαβR-/-). However, we and others have demonstrated that IFNαβR-/mice are, in fact, not more susceptible to infection with either respiratory syncytial virus (RSV) or a pneumotropic strain of influenza A virus while STAT1-/mice, which lack signaling from all three IFN receptors, are more susceptible. The experiments detailed here test the hypothesis that IFN-λ is capable of fully compensating for the loss of IFN-α/β signaling in protecting the murine host against respiratory virus infection. In support of this hypothesis we observed that IFN-λ is the major IFN induced following influenza virus infection, with peak levels exceeding that of IFN-α and IFN-β by ~10-fold in both wild-type and IFN-αβR-/mice. Importantly, the
On Dynamic Rule Generation for Real Time IDS Using GABIDS

: GABIDS ( G enetic A lgorithm B ased I ntrusion D etection S ystem (IDS)) is a dynamic IDS to support the real-time rule generation to identify abnormal network behavior. The chromosomes for the GA are essentially produced from information connectivity in the network. The currently existing IDS typically detect the attack type on basis of traditional static methods like fingerprinting. These IDS may not be robust enough to handle a new attack-type. In this paper, we propose GABIDS framework which dynamically generates the new set of rules to handle the unknown abnormal network behavior at any instant and update the Rule-database accordingly. This feature dynamically updates the rule set by capturing each new and unique network behavior state. GABIDS uses KDD Cup 99 dataset for training and testing purpose.
PRINCIPLES OF PROTEIN CRYSTALLIZATION

This study discussed about the principles of protei n crystallization. Since the first established rema rk of protein crystallization about 160 years back the growth protein crystal has enhanced into a comprehensive field of research wi t several applications. In this an overview of protein crysta lliz tion, their principles with some essential asp ects of protein crystallization is described. Protein crystallizati on constitutes a comprehensive research field rathe r varied from conventional crystallization of small molecule or in-organic com pounds. The most similarly used techniques of prote in crystallization are explained together with their major principles and the approaches used to detect applicable conditions of crystallization of a specific protein.
OXIDATIVE DNA DAMAGE PROTECTIVE AND HEPATOPROTECTIVE ACTIVITY OF TERMINALIA BELLERICA (BAHEDA)

Objective: To investigate the ethanolic extracts of Terminalia bellerica for its in vitro DNA damage protective activity against hydroxyl radical (OH • ) and hepatoprotective activity against CCl 4 Methods: The DNA damage assay was performed using supercoiled pBR322 plasmid DNA with Fenton’s reagent. Protection of human liver-derived HepG2 cells against CCl induced toxicity in HepG2 liver cells. 4 induced damage was determined by trypan blue exclusion assay and Tetrazolium bromide salt MTT assay. Liver cells were pre-exposed to 0.5 µmol/ml of CCl 4 Results: The low dose (0.001-0.1 µg/ml) of leaf and bark has an effect on the percentage viability (101±4.04) and 97±4.04) for leaf and bark ex- tracts respectively) of HepG2 cells. Whereas, the percentage cell viability 99±2.89 and 103±4.04 were found for fruit pulp and seed extracts respec-tively. The uniform DNA damage protective activity was observed in a dose-dependent manner for leaf, fruit pulp, seed and bark extracts of T. belle- rica. for 60 min at room temperature and screening assay was carried out in HepG2 cells to evaluate the cytotoxicity of tested extracts with concentration 0.001 to 100 µg/ml. Conclusion: Leaf and bark of T. bellerica exhibit 10 fold less toxicity compared to fruit pulp and seed during CCl and bark has more therapeutic potential against hepatotoxicity.
Lazy Learning Associative Classification in MapReduce Framework

: he core objective of the work is to propose a distributed environment based lazy learning Associative Classification (AC). Associative Classification is a hybrid version of data mining tasks which integrated both Association Rule Mining (ARM) and Classification technique to construct accurate classifier. Unfortunately, the AC used for learning these classifier are less popular in real time for building application due to its higher computation time complexity and memory constraints in large volume of datasets. Moreover, single processor’s CPU resources and memory are limited, which makes the algorithm incompetent to handle such datasets. To overcome such downsides, we proposed a distributed and parallel computing for lazy learning associative classification for accelerating algorithm performance by projecting the testing instances with large training datasets. In this work, we have implemented MapReduce based algorithms which reduce the computation by eliminates the need of constructing generalized classifier. It also well handled rare rules and generated institutive rules. The proposed algorithm may be suitable in area such as network intrusion detection, fraud detection, crowd analysis, rare disease prediction and crime analysis. Our algorithm has been compared with well known existing algorithms in relations of precision and running time. The experiments result has strengthened the proposed algorithm well handle the rare rules in distributed environment and is making better performance even the size of the datasets is huge.
Bendamustine and rituximab for the treatment of relapsed indolent and mantle cell lymphoma: when timing of a study matters

In a recent issue of The Lancet Oncology , Rummel and colleagues reported the mature results of StiL NHL 2-2003, a multicenter, randomized, open-label, non-inferiority phase 3 trial comparing bendamustine plus rituximab (BR) with fludarabine plus rituximab (FR) in patients with relapsed or refractory (R/R) indolent non-Hodgkin lymphoma (iNHL) or mantle-cell lymphoma (MCL) (1). When this study was conceived, fludarabine-containing regimens were widely used, and rituximab was rapidly being integrated into the chemotherapy regimens for CD20-positive lymphomas ( Table 1 ) (12). This study and others reintroduced bendamustine for the treatment of lymphoid neoplasms after its long clinical abeyance. In fact, the authors reported the results of a simultaneous trial, the StiL NHL 1-2003, demonstrating the superiority of BR as compared to rituximab-cyclophosphamide-doxorubicin-vincristine-prednisone (R-CHOP) in patients with newly diagnosed iNHL or MCL (13).
DIURETIC ACTIVITY OF MATOA LEAVES EXTRACTS AND FRACTIONS (POMETIA PINNATA J.R. FORSTER & J.G FORSTER) AND ITS INFLUENCE ON POTASSIUM AND SODIUM LEVELS

Objective: The goal of this research were to determine diuretic activity of matoa leaves (Pometia pinnata J.R. Forster & J.G Forster) extracts and fractions and its influence on potassium and sodium levels. Methods: Matoa leaves were extracted by reflux method followed by evaporation using rotary evaporator. The subjects were male Wistar rats that were divided into 11 groups: Furosemide (3.6 mg/kg bw); control group carboxymethylcellulose 0.5%; matoa leaves extracts with doses of 50 mg/kg bw, 100 mg/kg bw, and 150 mg/kg bw; matoa leaves aqueous fraction with dose of 10.94 mg/kg bw, 21.88 mg/kg bw, and 32.82 mg/kg bw; matoa leaves ethyl acetate fraction (MEF) with dose of 4.35 mg/kg bw, 8.71 mg/kg bw, and 13.06 mg/kg bw. Rats were placed in metabolic cages during an observation study. Urine volume was measured for 5-24 hrs. Potassium and sodium levels in urine were determined using atomic absorption spectrophotometry. Results: The effective dose of matoa leaves extract and fractions for diuretic activity was matoa leaves MEF 8.71 mg/kg bw which could increase the excretion of sodium and potassium in the urine of the male Wistar rats. Conclusion: Matoa leaves extract and fractions could increase the excretion of sodium and potassium in the urine of the male Wistar rats.
A Survey on ECG Signal Feature Extraction and Analysis Techniques

: Electrocardiogram (ECG), a non-stationary signal, is extensively used to measure the rate and regularity of heartbeats. Comparison of overall ECG waveform pattern and shape enables doctors to diagnose possible diseases. Currently there is computer based analysis which employs certain signal processing to diagnose a patient based on ECG recording. Noise severely limits the utility of the recorded ECG and thus needs to be removed for better clinical evaluation. ECG feature extraction is also required because it plays a significant role in diagnosing most of the cardiac diseases. One cardiac cycle in an ECG signal consists of the P-QRS-T waves. The feature extraction scheme determines the amplitudes and intervals in the ECG signal or any other features of it for subsequent analysis. Recently, numerous research and techniques have been developed for analyzing the ECG signal. This proposed paper discusses various techniques and transformations proposed earlier in literature for feature extraction and analysis of ECG signals and makes comparison among them.
Microwave Generated Nanocomposites for Solubility Enhancement of Atorvastatin Calcium: In Vitro- In Vivo Characterization

Atorvastatin calcium is anti-hyperlipidemic agent has low aqueous solubility resulting in low oral bioavailability thus presents a challenge in formulating a suitable dosage form to improve the aqueous solubility. Nanocomposites are novel technology for enhancing the solubility of Atorvastatin calcium. Nanocomposites formulation of Atorvastatin was prepared by microwave induced diffusion method (MIND). The natural polymers like gum acacia, modified gum karaya and synthetic polymer like PVP K-30 were used as carrier in the formulation. Six different formulations were prepared with varying ratios of drug and carriers and corresponding physical mixtures were also prepared. The selections of natural carriers were based on their surfactant and wetting properties. The optimum drug-to-carrier ratio was found to be 1:4 AAN which enhanced solubility nearly 14 fold as compared to pure drug. In vitro drug release exhibited cumulative release of 84.72% as compaired to 48.47% for the pure drug. The optimized nanocomposites were characterized by Fourier transform infrared spectroscopy, Differential scanning calorimetry, X-ray diffraction, Scanning electron microscopy, and Transmission electron microscopy. In a Triton-induced hyperlipidemia model, a 2-fold increase in the lipid lowering potential was obtained with the reformulated drug as compared to pure drug. These results suggest that nanocomposites using natural carrier is a promising approach for oral delivery of Atorvastatin calcium.
Comparison of Efficient Algorithms for Sequence Generation in Data Mining

- Data mining is the method or the movement of analyzing data from different perspectives and summarizing it into useful information. There are several major data mining techniques that have been developed and are used in the data mining projects which include association , classification , clustering , sequential patterns, prediction and decision tree . Among different tasks in data mining, sequential pattern mining is one of the most important tasks. Sequential pattern mining involves the mining of the subsequences that appear frequently in a set of sequences. It has a variety of applications in several domains such as the analysis of customer purchase patterns, protein sequence analysis, DNA analysis, gene sequence analysis, web access patterns, seismologic data and weather observations. Various models and algorithms have been developed for the efficient mining of sequential patterns in large amount of data. This research paper analyzes the efficiency of four sequence generation algorithms namely GSP, SPADE, PrefixSpan and FreSpan on a retail dataset by applying various performance factors. From the experimental results, it is observed that the PrefixSpan algorithm is more efficient than other three algorithms.
THE RELATIONSHIP BETWEEN PERCEIVED SOCIAL SUPPORT AND RESILIENCY IN STUDENTS OF KERMAN UNIVERSITY OF MEDICAL SCIENCES (2014)

having coping ABSTRACT Background & Aim: Students are the future of society. Therefore, recognizing factors affecting their health can be helpful in enhancing their mental health and academic achievement. Accordingly, the aim of this study was to investigate the relationship between social support and resiliency in students. Methods & Materials: The statistical population of study consisted of 1509 undergraduate students, were studying in the Kerman University of Medical Sciences in 2014. In this study, 500 students were selected among entire population by using simple random sampling method. They were measured using Connor-Davidson Resilience Scale (CD-RISC) and Multidimensional Scale of Perceived Social Support (MSPSS). To analyze data used, in addition to descriptive statistics, inferential statistical methods were used, including: Pearson correlation coefficient, t-test, ANOVA, and linear regression analysis by SPSS software. Results: The findings showed a significant correlation between social support and resiliency of students (P<0.0001, r=0.51). Among the subscales of social support, social support of family, social support of important people in life, and social support of friends had the highest correlation with resiliency (P<0.0001), respectively. The regression results cleared that social support predicts 26 % of the changes in resiliency. Conclusion: The results of this study indicated that increased social support improves the resiliency of the students, and it helps them to cope with problems and emotions.
Effect of limiting the hop count: Distributed Three-Hop Routing Protocol

---Hybrid Wireless Networks have gained a lot of importance because of its ultra-high performance in today’s networking world. Hybrid Wireless Networks consists of both Mobile Ad-hoc Network (MANET) and Infrastructure Wireless Networks. MANETs are infrastructreless and most probably short lived networks where mobile nodes communicate directly through peer-to-peer. Infrastructure Wireless Network requires a central point of access. As there are advantages of using these two together there are also disadvantages that leads to high overload, increase in number of hotspots, low reliability. Thus, we need routing protocols to increase the network scalability and capacity. However, these flaws will be overcome using Distributed Three-Hop Routing Protocol leading to low overhead, reduced number of hotspots and increase in reliability. A distributed Three-Hop Routing protocol (DTR) divides a message stream into number of segments and then transmit these segments in a distributed manner. It makes full use of widespread base stations using its high speed ad-hoc interface and reduces congestion using its cellular interface. DTR reduces the overhead by eliminating the work for route discovery and maintenance. Finally we can improve the lifetime of the network by analyzing the impact of limiting the hop count. This is done by calculating the energy efficiency of every node.