Miracle-dz/UniFAR-FTUs huggingface dataset:
A NOVEL APPROACH FOR SELF ADAPTIVE MULTI-AGENTS RESOURCE ALLOCATION IN CLOUD ENVIRONMENTS

The creation of virtualization and Cloud Computing technologies assures a number of features such as improved flexibility, stabilized energy efficiency and lower operating costs for IT domain. However, highly erratic workloads make it demanding to offer quality-of-service assurance in the mean while promising competent resource utilization. To evade breach of service-level agreements (SLAs) or unproductive resource usage, resource allocations need to be tailored endlessly during operation to reflect dynamic application workloads. In this paper, we present a novel approach to self-adaptive multi-agent resource allocation in cloud environments based on online architecture-level performance models. We present a comprehensive setup of a delegate enterprise application, the new Virtenterprise_Cloudapp benchmark, adopted in a virtualized cluster environment.
Comparative evaluation of gingival thickness in smokers and non-smokers

Smoking has its effects on the periodontium, which is reflected by morphologic and histologic changes in the gingiva. Gingival thickness is one among the factors, which has become a subject of considerable interest in periodontics both from the epidemiological and therapeutic point of view. By taking gingival thickness into consideration during treatment planning, more appropriate strategies for periodontal management may be developed, resulting in more predictable treatment outcomes. The purpose of the study was to estimate the gingival thickness in different regions of the mouth in smokers and non-smokers. The study group included 40 age-matched smokers and non-smokers with and without periodontitis. Gingival thickness assessment was done on 6 index teeth; 6 sites on each tooth by transgingival probing using 15 sizes endodontic reamer and thickness was measured using vernier caliper. A statistically significant difference in gingival thickness was observed in different sextants of mouth in smokers and non-smokers. Keywords— Smoking, Periodontium, Gingival thickness
Satellite Image Denoising Based on Entropy Thresholding using Shearlet Transform

— Satellite images have become universal standard in almost all applications of image processing. However, satellite images are susceptible to noise arising due to unresolved flaws in acquisition and transmission system. Development of a denoising algorithm in satellite images is still a challenging task for many researchers. Most of the state of the art denoising schemes employ wavelet transform but the main limitation of wavelet transform is that it can preserve only point singularity. Shearlet transformation is a sparse, multiscale and multidimensional alternative to wavelet transform. Shearlet transform is optimal in representing image containing edges. In this paper, a novel image denoising algorithm utilizing shearlet transform and entropy thresholding is presented which was found to exhibit superior performance among other state of the art image denoising algorithms in terms of peak signal to noise ratio (PSNR).
REAL-TIME SCHEDULING POLICY IN EMBEDDED SYSTEM DOMAIN: A FRAME WORK

: Scheduling a sequence of jobs released over time when the processing time of a job is only known at its completion is a classical problem in CPU scheduling in time-sharing and real time operating system. We discuss here different scheduling techniques used in Real-Time systems. Even if there are several scheduling policies, the preemptive scheduling policies hold promising results. In this paper we have done an extensive survey on various scheduling algorithms. We are extracting the positive characteristics of each scheduling and placed it on this paper.
An Optimization Approach for Fault Tolerance in Centralized Network Architectures

: In the context of a Multi-site Enterprise with a VPN connection, making electronic transactions is critical especially when financial transactions are concerned. We focus in this paper on improving fault tolerance in centralized network architectures to guarantee availability of critical services. Previous works suggested using spatial redundancy. Currently proposed approaches, mainly those recommended by the world leader Cisco in high-availability clusters, suggest to duplicate all equipment and network links. The originality of our approach is based on a heuristic named smartDuplication that suggests an intelligent choice of network equipment and links to duplicate. It uses a new quality function S (G) to compare two centralized network architectures with respect to their ability to tolerate faults. We show that choosing the set of equipment or links to duplicate while taking into account the cost factor of buying equipment boils down to the KnapSack NP-complete problem. We then propose polynomial time heuristic.
Sleep Disorders in Schizophrenia. A Review of Literature

Up to 80% of patients with schizophrenia have suffered from sleep disorders (SD), which are normally under diagnosed and undertreated due to lack of consideration in the management of schizophrenia. The most common sleep disorders in schizophrenia are insomnia, restless leg syndrome (RLS), obstructive sleep apnea syndrome (OSAS), hypersomnia, parasomnia and circadian rhythm disorders. The relevance of insomnia should be emphasized, as it can be a prodromal signof schizophrenia, as well as an alarm sign of an incipient psychotic decompensation. Additionally, the clinical recovery of a psychotic process is usually correlated with sleep normalization. Multiple polysomnographic disturbances on sleep architecture have been found in patients with schizophrenia, including increased sleep latency, awakenings during sleep, sleep-wake cycle inversion, as well as poor sleep efficiency. These objective disturbances have been correlated to subjective poor sleep quality and other main clinical symptoms in schizophrenia.
Factors Affecting Non-Compliance among Psychiatric Patients in the Regional Institute of Medical Sciences, Imphal

AIMS: This study was carried out with an objective to assess different reasons of medication non-compliance in a sample of out-door psychiatric patients and to evaluate the correlation between clinical variables and causes of non-compliance to drugs. This study was conducted at Psychiatry department, Regional Institute of Medical Sciences; from (30 th Sept 2012 to 31 st Aug 2013) . Data from a non-probability sample of 50 follow-up patients with a definite psychiatric illness was collected. Patients were recruited who were between 18 and 60 years of age and who were treated in the outpatient clinics of the psychiatry department. Information regarding sociodemographic characteristics (e.g., age, gender, level of education, marital status, and income) were recorded on a proforma designed by the author. A questionnaire designed for this purpose was administered to examine the factors that cause non-compliance. Data was analyzed using SPSS for window 20.0 version. Chi-square analysis done for variable of interest. Non-compliance to drug treatment is most common in between 21-30 years of age groups, more in females 72% than males 28%, those who are married(60%) than single(40% ),and with low education(28%) & low socio-economic status(54%). Notable reasons for non-compliance were feelings of wellness(26%), paranoia to medication(22%),lack of insight to the illness(14%),medication side effects(10%),hopelessness of cure & poor support (8%) respectively. \,financial problems (6%), no improvement(4%), too much of medication(2%). The commonest psychiatric illnesses leading to non-compliance were schizophrenia (26%), BPAD (18%), MDD (14%), psychosis (10%), epilepsy & OCD (8%) each, GAD (6%), somatization disorder & substance induced psychosis (4%) each, panic attack with agoraphobia (1%). Data analyses explored significant associations between age, income and psychiatric illness with causes of noncompliance to drugs (P<0.01). CONCLUSION: Non-compliance is quite common in psychiatric patients. Medical practitioners need to be aware of it and address this problem because compliance is directly related to the prognosis of the illness.
RF and Solar Energy Scavenging For IoT: A Survey

IoT is causing a huge economic and technology wave in various sectors like automation industry, healthcare, physical security, building management, waste management, vehicle monitoring and many more. IoT connects billions of “things” which communicate with each other. Supplying continuous power to these “things” which are essentially devices is a big challenge. Major research is happening in this direction in terms of improving battery capacity, improving microcontroller clock cycles and so on. Self sustainable system is a key issue of IoT. Energy harvesting is fundamentally a different approach of addressing this problem. The development of efficient energy generation or harvesting method will prove instrumental for IoT. There are several energy harvesting techniques however RF and solar energy are more viable since they are available in abundance. So these sources are discussed in detail here. The energy harvested can then be stored using supercapacitors.
Noise-Immune ECG Classifier Using Wavelet Transform and Neural Networks

-This paper proposes a novel algorithm for automatic classification of electrocardiogram (ECG) beats recorded by Holter systems. The algorithm is based on a combination of neural network and discrete wavelet transform. Discrete wavelet transform coefficients are used as an input of the neural network to perform the classification task. The proposed classifier wastested by both real ECG signals andartificially generated signals. Five Hermite functionswereused in generating the ECG artificial testing signals. Different levels of noise were added to the signals to examine the noise immunity of the classifier. The main advantage of the proposed classifier is that it is noise immune and accurate. The testing results on the proposed classier show that it is capable of recognising 40 beats, and it works properly in the classification of the ECG signal with a classification ratio of 100% for an SNR of more than 6 dB.
International Journal of Advanced Research in Computer Science and Software Engineering

— The evolution of new mobile communication standards demand highly efficient multi-band power amplifiers (PAs) both in mobile equipment’s and base stations. The design of CMOS power amplifiers (PAs) is still a challenging task. In this paper, a dual-band amplifier for wireless applications in a 0.18 µm CMOS process is presented. The PA uses class-E topology to exploit its soft-switching property for high efficiency. The proposed PA is simulated by Advanced Design System (ADS) in TSMC 0.18 µm CMOS process and simulation results show that the designed PA can deliver 21.407/21.523 dBm output power to 50 Ω standard load at 2.4/5.15 GHz with 55.1/51.07% Power-Added-Efficiency (PAE) and drain efficiency (DE) 55.5/51.4 % from 1.8 V supply voltage.
Modelling Of Water Supply Cost For Offshore Platforms

An offshore platform like other industries requires water resources to support daily operation and activities. Supplying water to offshore platform mainly executed in two conventional ways: transported using offshore supply vessel and selfproduced onboard platform using seawater desalination process commonly known as a freshwater generator. An alternative method such as water reuse or water recycling also received growing attention as one of a promising integrated solution for improvement of water resources. However, each water supply options have different characteristics and limitations in term of supply capacity, cost associated and level of water quality. Hence, it’s difficult to identify which water supply sources that fulfil the demand and cost-effective. This paper proposes a new mathematical model namely Economic Water Supply Model (EWSM) for the operating cost function that includes the most representative variables in the process. This model enables decision-makers and owners to assess the economic value from the different sources of water supply for the offshore platform. The calculation of these extended cost function also enables a detailed comparison to be made of the various water sources from an economic of view in order to analyze the effects of supply capacity for costeffective of various suppliers. In this sense, a model of the structure costs associated with each of the available water supply technologies can be useful input for future design, planning and operational stage of offshore water supply process.
Does Public Health Insurance Increase Maternal Health Care Utilization in Egypt?

We assess the impact of health insurance on the utilization of maternal health care services in Egypt. A propensity score matching is used to control for baseline differences in the characteristics of the insured and uninsured women, to determine the difference in health care utilization between the two groups that is attributed solely to the health insurance coverage. The results yield that the national health insurance has a strong positive impact on most of the maternal healthcare indicators. Public health insurance coverage increases the likelihood of receiving antenatal care by about 7%, delivering in a public health facility by 8%, and the likelihood that a newborn receive vitamin A dose after delivery by 8.2%. However, women who are less educated, from a poor household, and rural regions, are less likely to be covered by a health insurance. The findings of this study would guide intervention measures that aim at improving health care utilization especially among the poor and other vulnerable groups.
POTENTIAL APPLICATIONS OF ANTIOXIDANT COMPOUNDS DERIVED FROM ALGAE

Algae produce a variety of antioxidant compounds ranging from ß-carotene, astaxanthin, fucoxanthin, phycocyanin to sulfated polysaccharides. The commercial microalgae Spirulina, Chlorella, Dunaliella and Haematococcus, currently consumed as nutritional supplements, are rich in antioxidant compounds. For instance, astaxanthin from the green alga Haematococcus is known to be a very powerful antioxidant while the hypersaline alga Dunaliella salina is being mass-cultured for the commercial production of natural ß-carotene. The health benefits of antioxidant compounds such as sulfated polysaccharides and fucoxanthin from seaweeds (macroalgae) have also received much interest. Algae have great potential for nutraceuticals and functional food ingredients as studies have shown that antioxidants from algae may have preventive effects against diseases such diabetes, atherosclerosis and cancers. However, the therapeutic potential of antioxidant compounds from algae is yet to be fully explored. In addition, the antioxidant extracts and compounds from algae may have applications in prolonging the shelf life of food products by retarding oxidation and peroxidation processes, as well as a functional ingredient in the food industry. In this review, the potential applications of antioxidant compounds from the four commercial microalgae Spirulina, Chlorella, Haematococcus and Dunaliella, as well as those from seaweeds are highlighted.
Using Data Mining Techniques and R Software to Analyze Crime Data in Kenya

Crimes have been the most dangerous threat to peace, development, human right, social, political and economic stability in Kenya. There is a great need to eradicate crime to facilitate development and counter all vices that are caused by crime. Efficient management of crime requires an adequate understanding of the patterns in which crime occur to put the appropriate measures in place for crime prevention. Crime has been in existence since the beginning of time hence will remain, and one of the solutions is to identify the pattern in which it occurs to prevent or counter it effectively as it occurs. The main objective of the study was to find out how different crimes are related. The study considered a number of data mining techniques which included; clustering, specifically k-means algorithm, mapping and APRIORI algorithm to analyze how different crimes are related and how often they occur. Crime cases were found to be decreasing over the years under study and counties with a high population reported higher number of crimes as compared to those with low population. The study suggested that these crimes could be controlled by directing more resources in the highly populated counties. The study leaves a research gap where the same crime data could be analyzed using time series methods since observed crime offenses are recorded alongside the time they occur.
Effect of Similarity Measures on AGNES Based Hierarchical Clustering

The clustering is process of grouping objects based on some similarity measure. In hierarchical clustering, the objects can be clustered on the basis of single linkage, average linkage, median linkage or complete linkage. In this paper we have tried to analyze the effect of various similarity measures on AGNES, which is a hierarchical clustering algorithm. In the current paper, we have used single linkage, complete linkage, average linkage and median linkage as a similarity measure and their effect on the results of AGNES hierarchical clustering algorithm. For the implementation purpose, we have used JAVA programming language.
LVPLL with MCSS Charge Pump in 90nm CMOS for SoCs

---------------------------------------------------------------------***--------------------------------------------------------------------- Abstract - A Low voltage Power Efficient Phase Locked Loop is designed for SoCs. A MCSS Charge Pump is introduced to reduce leakage current and current mismatch. In order to achieve wide tuning range at low VCO gain Kvco. A Low voltage VCO (LV-VCO) for low voltage application is designed by a Ring Oscillator using 4-stage differential delay cells with low voltage segmented current mirror (LV-SCM) for biasing purpose. Proposed PLL is implemented in 90nm CMOS Technology and measures phase noise and power consumption for enhanced CP and RO-VCO.
Haralick Parameters for Texture feature Extraction

— Texture, the pattern of information or arrangement of the structure found in an image, is an important feature of many image types. In a general sense, texture refers to surface characteristics and appearance of an object given by the size, shape, density, arrangement, proportion of its elementary parts. Due to the signification of texture information, texture feature extraction is a key function in various image processing applications, remote sensing and content based image retrieval. Texture features can be extracted in several methods, using statistical, structural, model-based and transform information, in which the most common way is using the Gray Level Co-occurrence Matrix (GLCM). GLCM contains the second-order statistical information of spatial relationship of pixels of an image. From GLCM, many useful textural properties can be calculated to expose details about the image content.
Intelligent Classifier to Determine the Type of Erythemato-Squamous Disease

The differential diagnosis of erythemato-squamous diseases is a difficult problem in dermatology. Artificial Neural Network (ANN) classifies the given samples when trained and nearly 98 % classification accuracy is achieved. Generalized Feed Forward Neural Network (FFNN) can solve the multivariable classification problem of determination of skin disease. ANN approach is studied to determine the type of Erythemato-Squamous Disease, which all share the clinical features of erythema and scaling, with very little differences. The diseases are classified into six classes, namely psoriasis, seboreic dermatitis, lichen planus, pityriasis rosea, chronic dermatitis, and pityriasis rubra pilaris.
Improvement of Routing Protocol in Wireless Sensor Networks with Use of Reinforcement Learning

— Routing of information is one of the most challenging issues in the field of Wireless Sensor Networks. In this paper we tried to show how implementing AI techniques specially reinforcement learning algorithms can be efficient in finding the optimal route and maximizing network life time; data packets in a wireless sensor network can be transmitted from every node as a source to the sink via the optimal route with use of reinforcement learning technique.
Sexual Behavior and HIV Testing Practices among Men who have Sex with Men in Portugal

In Portugal there are an estimated 41,035 HIV-infected individuals [1], and one of the highest prevalence rates in Western Europe [2]. In addition, 81.7% of the people diagnosed with HIV were men, and although the highest rate of HIV infection is through heterosexual unprotected sex, approximately 15% of all infections were transmitted via homosexual and/or bisexual unprotected sex [1]. This is particularly important to mention given that this rate has doubled since 2001 among man who have sex with men (MSM) in Portugal, and these estimates only regard the notified cases, not considering the unreported cases. Therefore, MSM remain one of the most at risk groups for HIV transmission [3]. Despite these worrisome statistics, little systematic research has been conducted in Portugal on the contextual factors associated with unsafe sex among MSM, namely HIV testing practices and its relation to demographic variables and sexual behavior.
SPAM E-MAIL DETECTION USING CLASSIFIERS AND ADABOOST TECHNIQUE

ABSTRACT: The Internet has turned our existence upside down and has dramatically revolutionalized many different fields. Emails, social networking sites, surfing all have become a part and parcel of our lives. Emails are the fastest way to send information from one place to another. Whatever may be the work on the Internet, emails are involved everywhere. But, nowadays, emails are getting more prone to exploitation due to malicious attacks which include spam mails. Spam is flooding the whole internet with many copies of the same messages so as to force people who would not otherwise choose to receive such messages. So, in this study, Analyzing the performance of certain classifiers like Naive Bayes, Support Vector Machine, K-Nearest Neighbor and Decision Tree with the help of a dataset is done. These classifiers help in detecting spam emails with the help of keywords. Boosting grants power to these classifiers to improve their accuracy of prediction in most of the cases. Adaptive boosting also known as Adaboosting is used after the result of every classifier. This is implemented with the help of WEKA interface. The results have shown a comparison of all the three algorithms with boosting on the same dataset.
Facial Expression Recognition using PCA and Gabor with JAFFE Database

— In this paper I discussed Facial Expression Recognition System in two different ways and with two different databases. Principal Component Analysis is used here for feature extraction. I used JAFFE (Japanese Female Facial Expression). I implemented system with JAFFE database, I got accuracy of the algorithm is about 70-71% which gives quite poor Efficiency of the system. Then I implemented facial expression recognition system with Gabor filter and PCA. Here Gabor filter selected because of its good feature extraction property. The output of the Gabor filter was used as an input for the PCA. PCA has a good feature of dimension reduction so it was choose for that purpose.