Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey on Face Recognition Using Attribute Based Tree Construction and Sparse Codewords

: Improvement in digital technologies makes availability of digital camera and camera smart phones easier. Many of peoples are able to buy camera smart phones and share photos clicked by them in online system. Smart phone users are also interested in searching facial photos of their interest which includes sport icons and others. This makes available digital photos online which are in turn searched on photos content based. Many applications like to give features like content based image search from large a digital photo which is an emerging technology. This paper proposes semantic codewords generation method to utilize human face attributes that contain semantic clues of the face photos to improve content based face retrieval. In human attributes based face image retrieval system, proposes two methods named attribute-enhanced sparse coding and attribute embedded tree construction to improve the face retrieval. The sparse codewords of existing images from database are get compared with sparse code words of query image and attribute based tree construction gives result of face image retrieval ordered from higher matching to lower matching.
“Key clip extraction for Quick Browsing from Surveillance Videos”

The video information is growing exponentially day by day. Surveillance videos generate hours of videos, the video uploaded on internet is increasing on large scale, new TV channels also add to the content. It is necessary to develop some model to manage this information. Video summarization is a key problem. Video summarization is usually defined as a temporally condensed representation of video. It aims to provide a compact video representation while, preserving the essential activities of the original video. It plays a prime role where the resources like storage, communication bandwidth and power are limited. It can allow us to extract high level information when we are not interested in the whole unit, especially during organization and classification. We follow the pyramidal reduction using motion entropy approach and this method. The method involves of extracting the optical features of the video. Whenever long tubes exist in the input video such that no temporal rearrangement of the tubes can give very short video. One option is cut the long activity tube into larger subsections and then finds the entropy. And then we use a suitable threshold based on motion entropy to discard other frames and mutual information to get dependence of frames. Again divide each selected section into subsections and repeat the same till we get the appropriate results. These results in a dynamic video summarization with précised constitute. The increasing demand of the cameras for surveillance systems not only requires the large storage devices but also requires the reduction in the time to browse the whole video. To meet such requirements video summarization is the only solution. A good visual summarization should collect as much as possible visual information from input video and also maintain the chronology between them. 2. PRESENT THEORIES & PRACTICES
An Enhanced Human Activity Prediction at Rainy and Night Times

Human activity prediction aims to recognize an unfinished activity with limited motion and appearance information. A generalized activity prediction framework was proposed for human activity prediction where Probabilistic Suffix Tree (PST) was introduced to model casual relationships between constituent actions. Then, each kind of activity in videos was predicted by modeling interactive object information through Spatial Pattern Mining (SPM). This framework mined the temporal sequence patterns. For efficient human activity prediction a Spatio-Temporal Frequent Object Mining (STOM) was proposed in which the spatial, size and motion correlation among objects information were collected along with the temporal information. After the collection of this information, the objects were identified by using Modified Histogram Of Gradient (MHOG) and then the objects were tracked by particle filter technique. The frequent action of detected objects were identified by using Frequent Pattern-growth (FP-growth) which predicted the infrequent action as abnormal human activity in videos. However, MHOG based Object Detection and Tracking-STOM (MHOGODT-STFOM) based human activity prediction is not more effective at night time and rainy time. So in this paper, Enhanced Object Detection and Tracking- STFOM (EODTSTFOM) and Removing Rain Streaks-EODT-STFOM (RSREODT-STFOM) are proposed for human activity prediction even at night time and rainy time. In EODT, a modified Contrast Model is used which combined the contrast information and local entropy information to detect object contents present in the current image frame. Then, the objects are tracked by Kalman filter. In RSR-EODT, the rain streaks in the images are removed based on the deep Convolutional Neural Network (CNN). Then the objects are detected and tracked by modified Contrast Model and Kalman filter respectively. After the object detection and object tracking by EODT and RSR-EODT, the frequent actions are obtained by applying STFOM. The frequent actions are considered as normal activities and the infrequent actions are considered as abnormal activities. Thus the proposed EODTSTFOM and RSR-EODT- STFOM methods predict the human activities even at night time and rainy time.
International Journal of Current Research in

The use of triazole fungicide as agrochemicals into soil leading to many long lasting effects on the microbes present in the soil environment which indirectly affecting the soil health, the current work evaluates the impact of propiconazole on cellulase and invertase activities in red loam soil of groundnut (Arachis hypogaea L.) and paddy soil (Oryza sativa). In vitro experiment was conducted to evaluate the effect of propiconazole on their application rates (1.0, 2.5, 5.0, 7.5 and 10.0 kg/ha) on cellulase and invertase. The period of experimental set up was 10, 20, 30 and 40 days under the in vitro conditions. The results show that propiconazole was found stimulatory for the activities to the cellulase and invertase at lower application rates in both soils after 10 days of incubation period and an outstanding increase in the activity of cellulase and invertase was observed at the application rate 5.0 and 2.5 kg/ha respectively. Conversely, the comparable trend was observed after 20 days of incubation. The higher application rates (7.5 and 10.0 kg/ha) were found toxic to activity of cellulase and invertase in both red loamy and paddy soil. However, the results of the present investigation suggest that, recommended application rates of propiconazole have greater impact on the enzyme activity in the soil and the application of higher doses and regular contact of propiconazole will cause the harmful effect on enzyme activity in red loamy and paddy soil. Accepted: 31 July 2017 Available Online:06 August 2017
Mutagenesis and Selection of Lactobacillus bulgaricus and Streptococcus thermophilus for Potential use as Starter Culture

Mutagenesis and Selection of Lactobacillus bulgaricus and Streptococcus thermophilus isolated from “Kindirmo” a Nigerian local yogurt for potential use as starter culture was undertaken. The mutants isolated showed ability to grow on MRS agar, and were catalase negative. Their colony color varies from white to creamy grey. Most isolates were gram positive and were presumed to have high potentials of lactic acids bacteria and few were gram negative, and were presumed to have defective gene(s). The mutant isolates showed ability to produce acids in TSB and high degree of fructose utilization. The increased acids production with increase fructose utilization by some mutant cells may also suggest the use of fructose as a supplement to increase the rate of milk fermentation in “lazy-milks”. [The Journal of American Science. 2008;4(3):80-87]. (ISSN: 1545-1003).
A Survey on Classifying the Stages of Malignancy in Breast Cancer using Mammogram Images

Breast cancer is one of the leading causes of cancer death in women around the world. In order to reduce the death rate, the tumors have to be detected at the early stage. CAD system are considered to be the best technique for screening. This paper presents a survey of various staging techniques used for early and accurate detection of tumor, which aids in early medication by reducing false positive and false negative discrepancies. The several stages involved in the detection of tumor are pre-processing, segmentation, feature extraction and classification lastly staging of cancer, if confirmed for malignancy. Pre-processing stage improves the quality of the image by removing artifacts, and enhancing the image. Segmentation deals with removal of the pectoral muscle and segmenting the tumor part. Classification is used to classify the detected tumor as Malignant or Benign. The tumors identified as Malignant are then processed using various parameters like size, shape, energy levels and several other features for staging. Keywords— Breast cancer, early detection, CAD, preprocessing, segmentation, classification.
RECOGNITION OF URDU CHARACTER WITH HMM TECHNIQUE

: This paper deals with an Optical Character Recognition system for printed Urdu, a popular Pakistani/Indian script and is the third largest understandable language in the world, especially in the subcontinent but fewer efforts are made to make it understandable to computers. Lot of work has been done in the field of literature and Islamic studies in Urdu, which has to be computerized. Research has been carried out for years to automata Arabic and Urdu script. But the biggest hurdle in the development of Urdu OCR is the challenge to recognize Nastalique Script which is taken as standard for writing Urdu language. Nastalique script is written diagonally with no fixed baseline which makes the script somewhat complex. Overlap is present not only in characters but in the ligatures as well. This paper proposes a method which allows successful recognition of Nastalique Script.
Minimization of Number of Handoff Using Genetic Algorithm in Heterogeneous Wireless Networks

: A new vertical handoff decision algorithm is proposed to minimize the number of handoff in heterogeneous wireless networks, which comprise a number of wireless networks. In this paper we have proposed a multi criteria vertical handoff decision algorithm which will select the best available network with optimized parameter values (such as cost of network should be minimum) in the heterogeneous wireless network. The decision problem is formulated as multiple objective optimization problems and simulated using genetic algorithm. The simulation result shows that the number of handoff can be minimized if we take optimized network parameter values.
Generational and Social Mobility of Dentists in Bulgaria During the Period of Transition . Facts and Figures

Citation: Lydia Katrova. , et al. “Generational and Social Mobility of Dentists in Bulgaria During the Period of Transition. Facts and Figures”. EC Dental Science 18.11 (2019): 260-268. Abstract Introduction: This study is part of a longitudinal research of the re-professionalization of dentists in Bulgaria during the period of social and economic transition. Purpose of the Study: Of this study was to demonstrate the dynamic of professional reproduction based on the analysis of the generational mobility and succession of career choice among dental students. Methods: This investigation encompasses 5 comparable surveys carried out in: 1995 (n = 120), 2004 (n = 100), 2008 (n = 100), 2014 (n = 100), 2018 (n = 130) collected through a 20-item self-administered questionnaire focused on: family of origin social status, educational background, health status and health behaviors, motivation for career choice and expectations for professional realization of dental students. Data were analyzed comparatively. Intergroup numerical distributions were analyzed. Results: For the whole studied period the portion of students originating from families with medium income prevails (up to 70% in 2004, more than 80% in 2008 and 2014, and about 70% for 2018). Men/women ratio for the period 2004 2018 is reflecting a continuous de-feminization of the dental profession. After 1995 the percentage of parents with medical or dental degree doubled. The part of students who graduated from highly competitive high schools doubled twice (from 8% in 1995 to 20% in 2004, up to 48% in 2008, and stays sustainably over 70% after 2014). Important influences on their choice of dentistry are related to parents’ example (up to 44%) or professional image of a dentist (40% 50%). Most attractive professional feature for students after 1995 are the “independent nature of the profession” (about 60%) and the “expectation for financial security” (about 40%), followed by features as “working for people” (about 30%) and “social prestige” (30% 40%). Conclusion: Dentists in Bulgaria followed a sustainable development toward socio-professional identity accordingly to the overall social differentiation process, demonstrated by quasi uniform middle incomes’ status origin and prevailing higher and professional level of education of parents. Generational and social mobility of dentists is directed toward consolidation of the social status, evidenced by the family continuity in professional choice with slight trend toward opening to other social groups. The professional group keeps its social position of well-educated and healthy and wealthy style individuals, sharing the basic values of their pears globally.
QSAR studies of breast carcinoma using Artificial neural network, Bayesian classifier and Multiple linear regression

- Breast cancer is a disease that affects millions. It starts as a tumor but end up spreading all over the body. It uses nutrition of body for its own growth and there is no regulatory mechanism for its growth. Breast cancer has no cure once it progresses to an advanced stage. Therefore, it is very crucial to detect the breast cancer at early stage. An attempt has been made to develop a prediction model for breast cancer to find out the malignancy of a tumor using machine learning algorithms of artificial intelligence. Artificial neural network (ANN), Bayesian classifier and Multiple linear regression (MLR) were used to generate the prediction models using a dataset of 699 patients. Descriptors used to generate prediction models were uniformity of cell size, bare nuclei, bland chromatin and normal nucleoli. The percentage accuracies of generated models for ANN, Bayesian classifier and MLR were found to be 83.73%, 83.53% and 82.14% respectively. These models can potentially be useful for preliminary classification of breast cancer.
ROLE OF GENERAL GYNECOLOGISTS IN THE PREVENTION OF INFERTILITY

General gynaecologists have a significant role to play in the prevention of infertility. Some of the causes of infertility are iatrogenic and secondary to pelvic surgery. Careful assessment of women with gynaecological conditions such as fibroids and ovarian cysts and identification of those who can be managed without surgery may avoid potentially eliminate fertility difficulties secondary to pelvic adhesions in these women. When surgery is clinically indicated, primary prevention of pelvic adhesions would be of paramount importance. The measures to avoid postoperative adhesions include the use of a reliable surgical technique and potential use of anti-adhesion agents. Ovarian surgery for endometriomas and other benign cysts should be performed in the hands of experienced surgeons or in ‘centres of clinical expertise’; maximum efforts are exercised to preserve normal ovarian tissue as much as possible. General gynaecologists also have a role in the identification and early treatment of subclinical and overt STIs and pelvic infections; these efforts are likely to reduce the likelihood of tubal damage and subsequent infertility.
IN VITRO PROPAGATION OF CEROPEGIA FIMBRIIFERA BEDD., AN ENDANGERED, ENDEMIC PLANT OF SOUTH INDIA

A successful and efficient in vitro propagation protocol for C. fimbriifera, a rare and threatened species from South India, has been developed. Nodal explants were cultured on Murashige and Skoog (MS) medium fortified with different concentrations of cytokinines BAP (6-benzylaminopurine), Kinetine (Kn), TDZ (Thidiazuron) for multiple shoot formation along with Auxin IAA (Indole-3-Acetic Acid), IBA (Indole-3-Buyric acid) and NAA (Napthalein-3-Acetic Acid) for root induction. Among the four cytokinines tested BAP 3 mg/L showed higher number of multiple shoots (7.5±0.3) with length 11.2±0.8 cm. Induced multiple shoots were excised and rooted best (5.1±0.4) on half Strength MS medium with IAA 2mg/L. The well grown plantlets were subsequently transferred to greenhouse and acclimatized further for field transfer.
EVALUATING PERFORMANCE OF DSDV & DSR ROUTING PROTOCOLS IN MANET THROUGH QoS PARAMETERS

-Ad hoc is a network connection method which is often associated with wireless devices. The connection is established for the duration of one session and requires no base station. Instead, devices discover others within range to form a network for those computers. Devices may search for target nodes that are out of range by flooding the network with broadcasts that are forwarded by each node. Connections are possible over multiple nodes known as multi hop ad hoc network. For this ad hoc network I have tested the quality of service parameters like throughput, packet loss and delay for protocols DSDV and DSR with different number of nodes for various counts of agents and sinks. The comparisons have shown that DSR perform better than DSDV on all Quality of Service Parameters when the number of nodes increases, for various numbers of agents and sinks. Network simulators enable us to study the performance and behavior of these protocols on various network topologies. The performance of the network is validated with an NS2 for DSR and DSDV routing protocols.
Green Synthesis and Characterization of Copper Nanoparticles Derived From Murraya Koenigii Leaves Extract

In the recent years the green and eco-friendly method of synthesis for metal nanoparticles is an an emerging field in nanotechnology and nanoscience. The importance of nanoparticles in society and industries is due to the remarkable change in the physical and chemical properties of the materials in nanodimensions. The Copper nanoparticles (CuNPs) are mostly found their applications in the field of medical, electronic devices, biosensors, reagents in various reactions, lubricants, anti-biotic, anti-fungal, anti-microbial agents and many more. The present study involves the green and eco-friendly synthesis of CuNPs using the leaves extracts of the plant Murraya Koenigii (Curry leaves) and 1 mM Copper Sulphate (CuSO4.5H2O) solution. For this synthesis the different bio-components present in the leaves extract works as a reducing agents. The synthesized CuNPs were characterized by UV-visible spectroscopy, Scanning Electron Microscopy (SEM) and FTIR. The CuNPs formed were confirmed by the characteristics surface plasmon resonance (SPR) peak found at 340nm in UV-visible spectra. The morphological study from SEM images, it is confirmed that unsymmetrical spherical size Copper nanoparticles settled on leaves extract residue. FTIR spectrum clearly illustrates the green synthesis of copper nanoparticles mediated by the leaves extract. The method is fast and easy. As it does not involve the use of harmful and costly chemicals, it is more economical.
Survey on Fraud Malware Detection in Google Play Store

- The use of mobile devices including Tablets, Smart watch, and note books are increasing day by day. Android has the major share in the mobile application market. Android mobile applications become an easy target for the attackers because of its open source environment. Also user’s ignorance the process of installing and usage of the apps. To identify fake and malware applications, all the previous methods focused on getting permission from the user and executing that particular mobile application. A malware detection framework that discovers and break traces left behind by fraudulent developers, to detect search rank fraud as well as malware in Google Play. The fraud app is detected by aggregating the three pieces of evidence such as ranking based, co-review based and rating based evidence. Finally aggregating all the activities of front running apps, it can be achieve certain accuracy in classifying benign standard datasets of malware, fraudulent and legitimate apps. Additionally, we apply incremental learning approach to characterize a large number of data sets. It combined effectively for all the evidences for fraud detection. To accurately locate the ranking fraud, there is a need to mining the active period’s namely leading sessions, of mobile Apps.
The Most Effective Therapeutic Regimen for Patients with Severe Middle East Respiratory Syndrome Coronavirus (MERS-CoV) Infection

Background: More than two years after its discovery, the Middle East respiratory syndrome coronavirus (MERSCoV) continues to have a high case-fatality rate. A specific therapy with proven effectiveness for MERS-CoV infections still does not exist. Method: MERSCoV-positive patients were treated with different suggested treatment options. The virological and clinical progress of these patients in response to the treatment was evaluated. Results: Both patients had co-morbidities and were critically ill with acute respiratory distress syndrome on mechanical ventilation at the time of diagnosis. Even though low dose ribavirin/ pegylated interferon α combination was started early for the first patient, it was provided late for the second patient. Both patients recovered from their infection. Conclusion: By evaluating the clinical and virological response of the patients to the different treatment options implicated, it seems that currently the most effective therapy against severe MERS-CoV infection is the low dose ribavirin/ pegylated interferon α combination.
A Review of Image Based Fingerprint Authentication

Fingerprints, because of their uniqueness and other related characteristics, are the most widely used & highly accepted biometrics. Fingerprint biometric systems are either minutiae-based or pattern image based. The minutiae-based algorithms depends upon the local discontinuities in the ridge flow pattern and are used when template size is important while image-based matching algorithm uses both the micro and macro feature of a fingerprint and is used if fast response is required. A lot of research has been done in minutiae based fingerprint authentication systems that is why they are being widely used by both human experts and machines. But, in the literature a very few algorithms for image based fingerprint authentication are available. So, in this paper a review of image based fingerprint authentication algorithms has been discussed.
Emotion Classification along Valence Axis Using ERP Signals

Emotions play a very crucial role in our daily life. It is the emotion by which one can express his/her feelings. Emotion can be seen in person’s face in voice or in gesture such as reaction to stimuli. EEG signals play a significant role in knowing feelings or emotions of a person. This paper describes the extraction of event related potentials from the acquired EEG signals of three subjects by using the images from International affective Picture Systems for evocation of emotions. The event related potential features selected for emotion classification are P100, N100, P200, N200, P300 and N300 collected from three electrodes namely Cz, f3 and p4. The Support Vector Machine classifier has been used to classify the emotions into two classes along the valence axis. An accuracy of 95% is achieved on p4 electrode, 93.75% accuracy on f3 electrode and 92.5% accuracy is achieved on Cz electrode.