Miracle-dz/UniFAR-FTUs huggingface dataset:
Management of Acne Scars with Platelet-Rich-Plasma (PRP) Therapy as a Tvaka Rasayanam

Introduction: Acne vulgaris can lead to lifelong sequel in the form of atrophic or hypertrophic scars, depending on the degree of inflammation. Acne scar management remains a challenge to dermatologists. Platelet rich plasma (PRP), in which the platelets are in a highly concentrated form, has shown its effect in accelerating tissue repair and wound healing. The platelet releases a variety of growth factors which bring about tissue remodelling. Aim: To study the efficacy and safety of PRP in managing acne vulgaris as tvak rasayanam at Skin Care Unit, R.G. G.P.G. Ayurvedic College, Paprola, Kangra (H.P). Materials and Methods: Based on inclusion and exclusion criteria twenty patients were included in the study. PRP was made by double spin method. The area to be treated was cleaned with spirit and betadine, the skin was stretched and activated PRP loaded in an insulin syringe was injected into and around the scars by multiple punctures. Results: Out of 20 patients, 20% patients (4) showed marked improvement in the grading of acne scars, i.e. They improved up to grade 1 or there was an improvement by 2 grades. 50% patients (10) showed moderate improvement. In these patients grading of acne scars improved by 1. In 15% patients (3) although the grading did not changed but there was visible improvement in the appearance of the scars. In only 15% patients (3), here was no significant improvement was seen even after 4 sittings but texture of skin is mildly improved. Conclusion: Platelet- Rich-Plasma (PRP) therapy is easy to perform. Done as an outpatient procedure. It doesn’t hamper the daily activities quality of the patient. It works well for superficial acne scars, providing good results with minimal side effects like erythema and edema which subside within 2-4 hrs. PRP is thus an effective modality for treating acne scars.
Pattern Based Geographical Indexing of Text Documents

— As the internet users are increasing day by day with large amount of digital data. Geographical based tagging of their digital data such as audio; video, images is new area of exploration for the researchers. Geographical information is attached with the data in the form of longitude and latitude values. In this work, textual data is geo Indexed with the flicker website image tags. Most of the previously done work is done on the basis of term based approach, but this work introduces pattern based geo-indexing of the textual data with the help of Ripley’s method. Results show that proposed work of pattern based approach is more efficient as compared to the previous researches.
An Efficient Classification Algorithms for Employee Performance Prediction

-In any organization’s talent management is becoming an increasingly crucial method of approaching HR functions. Talent management can be defined as an outcome to ensure the right person is in the right job. Human talent prediction is the objective of this study. Due to that reason, classification and prediction in data mining which is commonly used in many areas can also be implemented in this study. There are various classification techniques in data mining such as Decision tree, Neural networks, Genetic algorithms, Support vector machines, Rough set theory, Fuzzy set approach. This research has been made by applying decision tree classification algorithms to the employee’s performance prediction. Decision tree is among the popular classification technique which generates a tree and a set of rules, representing the model of different classes, from a given data set. Some of the decision tree algorithms are ID3, C5.0, Bagging, Random Forest, Rotation forest, CART and CHAID. In this study, C4.5, Bagging and Rotation Forest algorithms are used. Experiments were conducted with the data collected from an institution which is implemented in WEKA tool.
PERSONALIZATION USING RECOMMENDATION SYSTEMS: STATE-OF-THE ART REVIEW

: In the era of internet, the proliferation of services will be a great challenge to the users in selecting the best and optimal services that suit the user requirement. So there is a need to filter, prioritize and efficiently deliver relevant information according to the user requirement. Recommendation systems rescue the users by selecting the required content after searching through large volumes of services. Recommendation system predicts user responses to options using diverse prediction techniques like content-based system, collaborative filtering system and hybrid system. This paper, in detail, explores different characteristics, approaches, evaluation metrics and potentials of different prediction techniques in recommendation systems that endorse the quality of services for the users.
COMPARATIVE SURVEY ON IMAGE DENOISING OF HYPERSPECTRAL IMAGES

: Removing noise from the original image is a major problem for researchers. Image denoising is a major area of research to get the clear images in the field of simple photography, medical image recognition, satellite image processing etc., Hyper spectral image(HSI) denoising is very essential to the quality assessment of the image. This paper compares the three different methods for denoising the HSI. The methods are multidimensional filtering, wavelet transform and shearlet transform. Shearlet transform is one of the emerging technique and is better in terms of quality measured through the performance evaluation technique such as PSNR (Peak signal to noise ratio), MSE(Mean Square Error) and so on .
A Survey and Analysis on Recommendation System Algorithms

A BSTRACT - The paper is comprehensive survey of methodologies and techniques used for recommendation systems. Recommendation shows significant part in many fields and has become a great attention in the field of research.Important factor for the searching and recommendation system is to recognize and recognizeuser’s personalized first choice from their historic rating behaviors. Recommender systems are now common in the research public, where many suggestions or recommendations are provided on the basis of algorithms. These algorithms usually implement differently in several fields and domains. Without properly defined algorithms systems are useless, therefore, it is essential from the research viewpoint, as well as from a real-world view, to be able to decide on an algorithm that exactly matches the domain and the interest of user. In this research paper we emphasized on graphical model based, supervised, semi-supervised and unsupervised algorithms that are necessary part of recommendation system.
Feature Extraction and Modification for Illustrating 3D Stone Tools from Unorganized Point Clouds

This paper presents a method for extracting and modifying features for illustrating stone tools. Features are detected from unorganized point clouds obtained by a 3D laser scanner. The curvature of each point in the point clouds is estimated by local surface ﬁtting algorithm and used for detecting potential feature points. Feature lines are extracted by directionally growing algorithm. Our directionally feature line growing method is simple to detect feature lines from unorganized point data. The main idea of our method is to extract feature lines using principal curvatures and principal directions of the potential feature points along the axis directions and to merge all extracted lines. In the illustration of stone tools, to maintain form and manufacturing information, it requires to modify the detected features by speciﬁc knowledge on illustrating stone tools. Using the power of data parallel computation on the GPU, our method can be accelerated multiple times. Finally, our experimental results are compared to the actual illustrations drawn by archaeological illustrators.
Generating Future Systems through Mobile Cloud Computing and Approaches to Cyber Foraging

: As mobile applications as well as cloudcomputing grow immensely mobile cloud computing (MCC) is introduced as evergreen technology in recent world. MCC integrates cloud computing and the mobile environment as well as overcomes the various issues on the mobile computing side which area related to performance which includes battery life, storage, environment in mobile computing. In this paper we can have an overview of the MCC including the definition, comparison of cloud and mobile cloud, its architecture, and brief applications. Various issues regarding the mobile cloud computing side are tackled and their proposed solutions are explained.
International Journal of Food and Nutritional Science

Transmissible spongiform encephalopathies (TSEs) belong to prion diseases that are invariably common fatal neurological disorders and affect humans and animals. Prion protein (PrP) was regarded as the only infectious agent inprion diseases and much attention was focused on the safety of PrP in foodstuffs. In this review, theprogress on the safety of milk relation to PrP and transmission route of prion diseases through milk are reviewed, including the discovery, content, source and the risk assessment of PrP in milk and milk products. And some possible control ways were proposed, which might be of particular interest for the improvement of milk safety relation to PrP in dairy food. *Corresponding author: Jin Zhao, China Jiliang University, College of Life Sciences, Hangzhou, 310018, China, Tel: +86571-86835772; Fax: +86-57186914449; E-mail: zhaojin@cjlu.edu. cn Received Date: March 13, 2017 Accepted Date: July 18, 2017 Published Date: July 27, 2017
IMPACT OF TOPIC MODELLING METHODS AND TEXT CLASSIFICATION TECHNIQUES IN TEXT MINING: A SURVEY

- The continuous growth of Information technology increases the amount of data explosively. Organize and analyse large document collection has become a big challenge. Text classifiers and topic models are used to sort out this problem. This paper mainly focuses on these two categories. First category discusses the three methods of topic modeling. They are Latent Semantic Analysis (LSA), Probabilistic Latent Semantic Analysis (PLSA) and Latent Dirichlet Allocation (LDA). The second category is text classification models. This includes Naïve Bayes Classifier, K-Nearest Neighbor and Support Vector Machines (SVM). A literature survey has done and explored the two categories. Finally, mentioned the combination of text classifiers and topic models can improve the classification accuracy. A combined approach of LDA and SVM show better performance than the others.
Evolutionary learning of Ontology Merging Algorithms

-Ontology merging is an activity of merging two or more source ontology’s to get single coherent ontology for extended knowledge. Literature suggests that in past, several ontology merging algorithms have been proposed by researchers for semantic information retrieval. The key idea associated with all those algorithms was how to merge ontology’s semantically which gives the best possible results for intelligent information retrieval. In this paper a review study of existing merging algorithms for ontology is presented with the association to solve domain specific problems. Quality metrics and various key factors for selection of best ontology merging algorithm is also discussed which will help to create new approaches for ontology merging by removing existing problems in available merging algorithms.
Epidemiologia dei disturbi psichiatrici nella popolazione generale. Le esperienze e i risultati italiani

Le indagini nella popolazione generate iniziano in Italia nella meta degli anni '80, come terzo filone della ricerca epidemiologica in psichiatria, quasi in contemporanea con i primi due, cioe quello relativo agli studi con il registro dei casi (Torre & Marinoni, 1982) e quello dei disturbi psichiatrici nelle medicina di base (Bellantuono et al., 1987). II punto di forza degli studi di popolazione e che essi permettono di conoscere lo spettro completo della morbilita «reale» presente nella popolazione generate e l'individuazione di gruppi a «rischio», con notevoli vantaggi per I'attivita di pianificazione dei servizi e per interventi di prevenzione, in termini di supporto e di riduzione dello stress. Per raggiungere questi obiettivi uno studio di popolazione dovrebbe avere almeno 4 caratteristiche: dovrebbe essere condotto a) in una popolazione con una precisa delimitazione geografica, da usare come denominatore; b) con uno scopo o una ipotesi precisa; c) con un metodo standardizzato per la misurazione della morbilita psichiatrica; d) con metodi appropriati per misurare le variabili che si ritengono causali (Jablensky, 1986; Brown & Craig, 1986; Henderson, 1988).
Adaptive sequence of Key Pose Detection for Human Action Recognition

Vision based human action recognition allows to detect and understand meaningful human motion. In this method, human silhouette extraction technique like background subtraction is applied. The extracted human silhouettes are processed in order to obtain the contour based feature. Finding the most characteristic poses among the training data returns the key poses. K-means clustering is an efficient technique to cluster poses based on attributes like distance. With reference to Weizmann dataset, six actions such as running, walking, jumping, bending, waving one hand and waving two hands are considered. The actions are learned by making use of sequences of key poses. The sequence of key poses model the temporal evolution between key poses with respect to the original training sequences. Action recognition is done by means of Minimum Distance Classifier. Action recognition of unknown key poses is matched with known classes of cluster center, then the correct action is recognized. Weizmann dataset deals with single view scenarios with an average success rate of 93%. © 2015 IWNEST Publisher All rights reserved . To Cite
The effect of L-Carnitine on the recovery of achilles tendon injury in postmenopausal rats

Background: Use of surgery for the treatment of achilles tendon rupture has its limitations. Therefore, additional treatment modalities are required during recovery of these ruptures. The present study investigated the effect of L-Carnitine (Carnitene®) on the recovery of injured achilles tendons in postmenopausal rats. Methods: This experimental study was conducted in 60 mature female Sprague-Dawney rats. Ovariectomy was performed for postmenopausal period. Carnitene® was administered in two doses as 100 mg/kg at low dose and 200 mg/kg at high dose using insulin injectors under sterile conditions. Inflammation score, Visual Analogue Score (VAS), and cartilaginous metaplasia were evaluated. Results: In rats with ovariectomy and tendon incision there was no difference in the effect of Carnitene® administration on the inflammation score (p=0.36), VAS score (p=0.26), cartilaginous metaplasia (p=0.44), and fibrosis development (p=0.44). Conclusion: There was no difference in the effect of the drug on postmenopausal tendon recovery. We recommend the use of Carnitene® for tendon recovery as a nontoxic and biocompatible agent.
Review of Energy Enhancement Protocol Wireless Sensor Networks

Wireless sensor network (WSNs) comprises of base station and number of nodes. Sometimes remote sensors are used which are generally known as remote sensor actuator systems. WSN is a large network of sensor nodes and these nodes are directly interacting with the environment by sensing the physical and ecological parameters like temperature, weight and so on. The limited energy of the sensor nodes is the major bottle neck of wireless sensor network. By conserving the on board energy, the life span of wireless sensor network can be well extensive. Data communication being the leading energy devastating movement of wireless sensor network, data decrease can provide better in conserving the nodal energy. The basic aim of this paper is to study and evaluate the performance of various techniques that are used for the communication in WSNs. The various algorithms that are used to enhance the energy of sensor nodes in wireless sensor network are portrait
BIOCHEMICAL PATTERN FOR HEPATITIS C AND ACUTE LYMPHOBLASTIC LEUKEMIA IN HUMAN SERA

Present experimental work aimed to show role of the molecular biology in diagnosis of hepatitis C liver disease (HCV) and acute lymphoblastic leukemia (ALL) which occur as a result of the disturbances of protein and enzymes fractions at the molecular level. The study carried out using vertical slab gel electrophoresis for detection of the protein pattern, catalase and peroxidase. Protein fractionation of the control samples produced 13 bands with Rf ranged between 0.17 and 0.96 and (amount, 3.14 7.24). Comparing hepatitis C with control one out of these 13 bands are completely disappeared at Rf 0.86 (amount 9.34). Ten bands appeared to be common bands in all HCV samples except one sample only nine common bands were produced while the band number ten was disappeared. The data showed that 5 characteristic bands were produced. One from these five bands determined at Rf 0.7 in all HCV sera samples except the first sample. Comparing leukemia samples with control only two were considered as common bands. These bands completely appeared in all sera samples. On the other side one band was completely disappeared in all leukemia samples. The rest bands distributed between different leukemia samples. 15 bands produced as characteristic bands. Electrophoresis pattern for catalase mentioned that six bands were produced in control samples. When hepatitis C compared with the control showed that two out these six bands were completely disappeared and other all HCV four bands considered as common bands. The amount of catalase enzyme completely decreased in all bands. In leukemia five common bands were produced with the appearance of one characteristic band, from the other side one band was disappeared. A documentation of peroxidase pattern data showed that tow common bands were appeared with Rf 0.1 and 0.33, the amount of these two bands were decreased when the amount of HCV compared with control in the same rows. In leukemia there is only one common band was produced with appearance of a three characteristic bands. [Journal of American Science. 2010;6(11):203-216]. (ISSN: 1545-1003).
A study of visfatin serum level in patients with Systemic Lupus Erythematosus ( SLE )

Background: Systemic Lupus Erythematosus (SLE) (also, known as Lupus) is a chronic inflammatory autoimmune disease. Visfatin is an adipokine which is secreted mainly from white adipose tissue (WAT) and is a pre B cell colony enhancing factor (PBEF) that regulates immunity and inflammation. So, it can trigger many autoimmune and inflammatory processes thus may affect SLE disease course and prognosis. Objective: Measurement of serum visfatin level in Lupus patients and healthy persons to assess its possible role in SLE. Subjects and methods: This study included 30 patients with SLE and 30 healthy volunteers with matched age and sex. So, we had two groups, patient and control. We assessed serum visfatin level in both groups by enzyme linked immunosorbent assay (ELISA). Results: As regard serum visfatin level, there was a highly statistical significant difference between control and patient groups. Results also showed a highly statistical significant positive correlation between visfatin and erythrocyte sedimentation rate (ESR) as well as C reactive protein (CRP) in patient group. Conclusion: All Lupus patients recorded high concentration of circulating visfatin and the more the ESR and CRP, the more would be the serum visfatin concentration. So, visfatin might have an important role in both inflammatory and immune processes involved in SLE. Hence, this indicates its possible role in SLE progression. Recommendation: More studies are needed to investigate this adipocytokine and its role in systemic inflammatory and immune processes in SLE. Also, more studies are required on large number of patients. Further studies could be valuable about visfatin inhibitors which might have a role in the prevention of SLE progression and complications. [Mekky Abd El-Monem Aly, Mohamed Farouk Mosa, Hatem Galal Abd Allah and Abd Allah Hamdy Mohammed Abozeid. A study of visfatin serum level in patients with Systemic Lupus Erythematosus (SLE). J Am Sci 2019;15(10):82-86]. ISSN 1545-1003 (print); ISSN 2375-7264 (online). http://www.jofamericanscience. org. 10. doi:10.7537/marsjas151019.10.
Providing a combined method for denoising using a bilateral filter and wavelet transform

In this paper, we provide an optimal and new method for denoising using a bilateral filter which in this method, first using wavelet transform, we perform a preprocessing and then use a bilateral filter for denoising. In the bilateral filter, after filtering, the edges of the image are lost and that is due to the use of neighboring pixels in this filter and also this filter has a better performance in low variance noises. To eliminate this flaw, a combination of this filter and wavelet transform was used to get better results. At the end, we compare the results with similar methods and discuss how to get optimal parameters in this filter.
Hash-Based String Matching Algorithm For Network Intrusion Prevention systems (NIPS)

- Network Intrusion Prevention Systems (NIPS) are employed in-line with the network segment that needs to be protected. As the packets within the network passes through the NIPS device, the packets are inspected for the presence of any attacks. Like viruses, most intruder activities have some kind of signatures, hence a NIPS device contains a pattern matching algorithm to match the virus signatures within the rule list with the incoming network packets. When an attack is identified, the NIPS blocks the infected data packet with a unusual signature pattern. The pattern-matching algorithm must be able to operate at network speeds, while simultaneously detecting the main bulk of intrusions. This paper proposes an alternative algorithm using a Hash Function which uses a SRAM that creates fingerprints of the packet payload which are then compared with the patterns signatures. The proposed hash based system consumes around 0.56 times or 56 percent less memory than the memory consumed by the RTCAM method. It can also be observed from the results that as the TCAM width doubles the initial width the memory consumption increases around 1000kb the initial memory consumption value in RTCAM method. But in the case of hash based method as the block size is doubled the memory consumption increases by a small value around 200kb only from the initial memory consumption value. Hence the proposed hash based method is efficient than the RTCAM method in terms of memory consumption. Furthermore, the system is fully compatible with Snort’s rules syntax, which is the basic standard followed for intrusion prevention systems.
Performance Analysis Of Low Power Using Hybrid And Subthreshold Adiabatic Logic For Digital Circuit

By using CMOS logic and transmission gate logic, a hybrid 1-bit full adder is designed, which is reported in this paper. The design is first implemented for 1-bit full adder and then extended for application of 4-bit Ripple Carry Adder(RCA). The Modification in the 4-bit Ripple Carry Adder is done by using Subthreshold adiabatic logic(SAL) circuits.It is analyzed to make great improvement in ultra low power circuit design. When comparing with the existing full adder designs, the present implementation was found to offer significant improvement in terms of power.After the implementation, the comparison of power is made between Hybrid logic and the Subthreshold adiabatic logic for 4-bit Ripple Carry Adder.Hence, 18% of power is reduced using SAL logic. The circuit is simulated using Cadence Virtuoso tool in 180nm technology.