Miracle-dz/UniFAR-FTUs huggingface dataset:
A SURVEY ON PRIVACY-ASSURED HEALTHCARE MONITORING BY ENABLING SECURE AND FAST INDEXING

- The proliferation of e-health technology, enormous amount of multimedia-based health data is being exponentially generated from medical devices and sensors. Coming with it are the challenges on how to efficiently acquire, index, and process such a huge amount of data for effective healthcare and related decision making, while respecting user’s data privacy. In this paper, propose a secure cloud-based framework for privacy -aware healthcare monitoring systems, which allows fast data acquisition and indexing with strong privacy assurance. For efficient data acquisition, adopt compressive sensing for easy data sampling, compression, and recovery. Then focus on how to secure and fast index the resulting large amount of continuously generated compressed samples, with the goal to achieve secure selected retrieval over compressed storage. One particular challenge is the practical demand to cope with the incoming data samples in high acquisition rates. For that problem, exploit recent efforts on encrypted search, efficient content-based indexing techniques, and fine -grained locking algorithms, to design a novel encrypted index. It achieves memory efficiency, provable security, as well as greatly improved building speed with nontrivial multithread support for Comprehensive evaluations on Amazon Cloud.
Toxicoproteomics: Current Trends in Analytical Techniques

Toxicoproteomics is one most recent field which is unambiguously positioned to elaborated the better understanding of protein expression during environmental disease and toxicity, genetic factor, toxic mechanism and mode of action in response to acute longrun advancement of disease caused or influenced by these types of exposure for public health. This is one of the challenging fields due to the shear size of the proteome and the massive data that are generated by it. Therefore, an immense improvement of methodologies needs to be applied in toxicoproteomics studies to mount the way for coming a new phase in the area of toxicology research.
ECONOMIC GROWTH AND UNEMPLOYMENT IN FIJI: A COINTEGRATION ANALYSIS

Fiji has been experiencing high unemployment rate since 1990s. Unemployment has become increasingly more pronounced from 2000 due to the political instability, expiry of land leases and operational problems in garment and gold industry. On the other hand, there was a surge in number of graduates following the establishment of two new universities. Additionally a sharp decline in investment in the post coup years of 2000 ensued, contributing to further increase in unemployment rate and sluggish economic growth. This paper seeks to investigate whether long run association among growth and unemployment is relevant for Fiji for the period 1982-2012. Johansen Cointegration test procedure has been applied to ascertain the assocaition among growth, investment and unemployment. Result confirmed the evidence of long-run association among unemployment and growth, with cointegration running from investment and unemployment to increase in economic output. Economic policies should gear towards improving investment.
Gaussian Distributive Stochastic Neighbor Embedding Based Feature Extraction for Medical Data Diagnosis

:- Feature extraction is a key process to reduce the dimensionality of medical dataset for efficient disease prediction. The feature extraction technique removes irrelevant features to acquire higher prediction accuracy during disease diagnosis. Few research works are developed to extract the relevant features from dataset using different data mining techniques. But, performance of conventional feature extraction technique was not efficient which reduces the accuracy of disease prediction and also feature extraction time was more. In order to solve these limitations, a Gaussian Distributive Stochastic Neighbor Embedding Feature Extraction (GDSNE-FE) technique is proposed. The GDSNE-FE technique is developed for dimensionality reduction of medical dataset with minimal time and space complexity. The GDSNE-FE technique comprises of two main steps. Initially, GDSNE-FE technique employs Gaussian probability distribution that finds the relationship between features in high dimensional space with help of distance metric. After determining the probability distribution, relevant features have high probability of being picked and irrelevant features have extremely lesser probability of being picked. From that, GDSNE-FE technique extracts only relevant features from medical dataset. After extracting relevant features, GDSNE-FE technique used Jensen–Shannon divergence to evaluate similarity between two feature probability distributions. This helps for GDSNE-FE technique to extracts the more significant features for accurate disease prediction with higher accuracy and minimum time. The GDSNE-FE technique conducts experimental evaluation on factors such as feature extraction accuracy, feature extraction time, true positive rate, false positive rate and space complexity with respect to number of features. The experimental result shows that the GDSNE-FE technique is able to improve the true positive rate and also reduces the time of feature extraction when compared to state-of-the-art-works
Feedback analysis using Sentiment Analysis for E-commerce

: In the current scenario existing systems typically use the star ratings of products to determine its quality, but ignore the tangible feedback. It is problematic because the star ratings may differ from the tangible feedback . We propose exploiting sentiment analysis algorithm (Sentiment analysis refers to the employment of linguistic communication processing, text analysis and computational linguistics to spot and extract subjective data in supply material) to judge product feedback. This issue is addressed by encoding sentiment data of feedbacks in conjunction with contexts of words in sentiment embeddings . Bycombining context and sentiment level evidences, we can identify the sentiment polarity of the product feedback provided by the customer.
Optimal Location of Doubly-Fed Induction Generator and Unified Power Flow Controller with Pso Algorithm

Power system operation can sometime operate under heavily loaded conditions which may lead to real power and reactive power loss, voltage deviation or even system collapse. This can occur mainly due to booming demand of electricity. This paper proposed a new methodology of integrating both Double Fed Induction Generator (DFIG) and Unified Power Flow Controller (UPFC) in a modified 30 bus system to overcome those problems. Particle Swarm Optimization technique (PSO) is used to find out the optimal location of DFIG and UPFC thereby reducing these losses and stabilizing the voltage levels (0.9 per unit-1.1 per unit). PSAT tool box is used in MATLAB platform to perform the power flow analysis. Final results show that the optimal location is achieved with sufficient reduction in power loss, better control over voltage profile and with better convergence from PSO algorithm.
DESIGN OF DADDA MULTIPLIER WITH OPTIMIZED POWER USING ANT ARCHITECTURE

: One of the most important hardware blocks for the DSP systems is multiplier block. In digital filtering, communication and analysis of the digital signals i.e in DSP applications the key role is played by the multiplier. In present day digital applications are focused for being portable and can be used as portable devices which means the devices are upcoming battery powered. Thus power dissipation becomes the important constraint in designing a system. Typically the multipliers are the complex systems and requires by clock rates, for reducing delay of the design for satisfying overall design performance. In this paper two different multipliers are designed based on ANT Architecture . The simulation and synthesis results are obtain by using XILINX ISE 12.3i . The modified Dadda multiplier and array multiplier are designed with combination of truncated multiplier . The multiplier circuit area in fixed width reduced precision replica can be lower by 39.99 % and the power can be reduced.
Antihyperglycemic, Antihyperlipidemic and Antioxidant effect of Atriplex farinosa and Atriplex nummularia in Streptozotocin- induced Diabetes in rats

The present study was designed to investigate the antihyperglycemic effect of Atriplex farinosa (A. farinosa) and Atriplex nummularia (A. nummularia) extracts in streptozotocin (STZ)-diabetic rats. Glibenclamide was taken as the standard drug. Diabetes was induced in adult male albino rats, weighing 160-175 g, by administration of STZ (45 mg/kg of body weight) intraperitoneally. Diabetic rats showed an increase in levels of fasting blood glucose (FBG) and glycosylated hemoglobin (HbA1c) in addition to serum levels of triglycerides (TG), total cholesterol (TC), low-density lipoprotein (LDL-C) and malondialdehyde (MDA) in their pancreas homogenates. They showed a decrease in levels of plasma insulin, hemoglobin (Hb), and serum level of high density lipoprotein (HDL-C)and activities of superoxide dismutase (SOD), glutathione peroxidase (GPx), catalase (CAT) and glutathione (GSH)in pancreas homogenates.Oral administration of A. farinosa and A. nummularia (200 and 400mg/kg) extracts and glibenclamide (0.6 mg/kg) decreased the FBGand elevated insulin levels after 4 and 8 weeks of treatment in STZ-diabetic rats in a dose-dependent manner. A meaningful reduction in the concentrations of HbA1c, TG, TC, LDL-C in serum and elevations in the activities of SOD, GPx, CAT and GSHin pancreas homogenates were observed in diabetic animals medicated with A. farinosa and A. nummularia extracts. Levels of HDL-Cin serum and MDA in pancreas homogenates were recovered significantly in A. farinosa and A. nummularia-medicated diabetic rats. Thus, our results show that A. farinosa and A. nummularia (200 and 400 mg/kg) possesses a promising antihyperglycemic effect that is comparable with glibenclamide.
Protective Effects of Hesperidin and Coenzyme Q10 on Experimental Gastrocnemius Muscle Ischemia/Reperfusion Model in Rats

Ischemia is called as the insufficiency or stopped of blood flow to an organ for various reasons (such as surgical procedures, thrombosis, hypovolemia, transplantation). Ischemia is the first part of ischemic reperfusion injury characterized by the shift of the metabolism to the anaerobic side. The restart of oxygenation is called reperfusion. Metabolites resulting from anaerobic metabolism are oxidised by reperfusion and are confused with circulation and are responsible for distant organ damage. The destructive effects of the reperfusion process, which occurs after resumption of blood flow in the living tissue of ischemic remainder, is wider and more severe than ischemic damage. Continuous leukocyte migration to ischemic tissue after reperfusion allows the continuation and enlargement of tissue damage due to ischemia reperfusion (I/R) [15]. Reperfusion related damage in the cell is created by many factors, mostly including oxygen-derived free radicals, which are rapidly generated in the tissue as a result of reperfusion. Owing to physiological or pathological alterations, oxidative damage takes place with changes in favor of the oxidation process [38].
The impact of individualism-collectivism and uncertainty avoidance on online shopping in Iran

Recently, due to increasing developments in the IT and communication sector, developing countries have improved their economic and social growth to reduce the gap between themselves and developed countries. In these circumstances, electronic commerce as a new field of commerce plays an important role in the advancement of developing countries. Customers in different countries with various cultures behave differently when faced with electronic commerce. Hence, cultural factors of electronic commerce users must be investigated in order to understand their different behaviour. The model of this research is based on Hofstede’s national culture and attempts to explore the effect of national culture covering individualism collectivism and uncertainty avoidance in increasing the internet shopping adoption rate in Iran. The research method is a combination of both qualitative and quantitative approaches. Data are collected via questionnaires from 60 people in libraries and universities in Tehran. Also, the interview has been conducted with 15 people with different interests in online shopping. The collected data are analysed through descriptive statistics. “Pearson” is used to test the hypotheses of the study. The outcomes show that users with low uncertainty avoidance culture carry out online shopping more than users with high uncertainty avoidance culture in Iran. However, there is no significant difference in online shopping according to individualism and collectivism culture in Iran. Furthermore, the interaction between uncertainty avoidance and the individualism-collectivism culture is not significant with regard to online
Text To Speech Conversion Using Different Speech Synthesis

: Text to speech (TTS) synthesis is the automatic conversion of text into speech. Generally, TTS system consists of two phases. The first is text analysis, where the input text is transcribed into a phonetic or some other linguistic representation. The second one is the generation of speech waveforms. In this TTS system, text to phoneme conversion depends on dictionary based approach to get the exact phonetic transcription. Speech synthesis such as domain specific, phoneme based synthesis and unit selection synthesis are used for concatenating speech. For numerical text to speech system, domain specific synthesis is applied. In phoneme based synthesis, the input text is considered as word to produce sound. For input sentence , unit selection speech synthesis is applied. This TTS system is mainly used for visual impairments and handicapped people.
A LITERATURE REVIEW ON TEXTURE CLASSIFICATION USING WAVELET TRANSFORM

Classification of image textures is an important task in image processing and pattern recognition. Over the years, extensive researches have been done for the classification of the texture images. The wavelet transform is an important multi-resolution analysis tool has been commonly applied to image analysis and various classification systems. The texture is characterized by a set of channel variances estimated at the output of the corresponding filter bank.
Bioremediation potential of Comamonas acidovorans MTCC 3364 for the removal of sulfonated di-azo dye Reactive Black B

Azo dyes are a chief class of synthetic colorants, which are released by the majority of the textile industries. The effluents of dyes disrupt the ecosystem so removal of these dyes is major concerned by using cost-competitive and eco-friendly method. The present study was aimed to study the decolorization efficiency of the textile azo dyes by Comamonas acidovorans MTCC 3364 and optimize the environmental condition for maximum decolorization and degradation of Reactive Black B (RBB) dye. Optimization of various environmental parameters like pH and temperature was studied in which maximum decolorization was obtained at 37°C, pH 7.0 under static condition within 24 hours. The addition of co-substrates lactose and yeast extract increased the rate of decolorization. The bacterial strain was able to decolorize high concentration of RBB dye (1 g l-1) up to 8th cycle. Vanillin was added as a redox mediator which showed the highest rate of decolorization (1.062 mg l-1 h-1) and thiourea was added as an inhibitor, which showed highest inhibition (0.246 mg l-1 h-1). Incubation of dye with a non-growing (free) cells and dead cells resulted in removal of dye from the buffer, indicating the biosorption and adsorption mechanism. Immobilization cell studies revealed that activated immobilized cell preparations decolorized RBB dye up to 10 cycles showing remarkable operational stability. The degradation analysis of RBB was further confirmed by HPTLC and FTIR techniques.
RAGE as a regulatory molecule in cutaneous chronic inflammation

Psoriasis is a complex genetic, chronic inflammatory disease of the skin with a prevalence of 2 % among the population. It is characterized by a multi-faceted interplay of keratinocytes, dendritic and T cells, as well as downstream transcription factors and proinflammatory cytokines. The IL-23/TH17 axis is thought to play an important role in the pathogenesis of psoriasis. However, the relative contributions of keratinocytes and immune cells to disease initiation and maintenance remain unclear. Recent studies implicate a role of the receptor for advanced glycation end-products (aptly known as RAGE) in the development of chronic disorders due to its expression on involved cell types and its constitutive signaling in inflammatory conditions. The here presented thesis revealed a role for RAGE in psoriasis by demonstrating its upregulation in human and murine psoriatic specimens. In addition, RAGE drives and maintains chronic inflammation as the initial phase and the chronic stage of the inflammation were found to be diminished in Rage-deficient mice upon topical Imiquimod treatment. Moreover, RAGE was expressed on psoriatic effector cells such as keratinocytes and dermal inflammatory cells including CD11c+ dendritic cells. While the inflammation-associated activation of keratinocytes resulting in a release of the alarmins S100B and HMGB1 was demonstrated to be independent of RAGE, the functionality of plasmacytoid dendritic cells, which are major activators of dermal dendritic cells, strongly relied on active RAGE signaling. Indeed, both HMGB1 and S100B in complex with self-DNA activated plasmacytoid dendritic cells via RAGE. The finding that RAGE additionally controls the transcription of inflammatory response genes involved in the differentiation of naive T cells into the T helper cell lineage further supported the hypothesis that RAGE signaling is essential for the activation of the IL-23/TH17 axis in psoriasis. This was underlined by the rescue of the defective inflammatory phenotype of Rage-deficient mice by intradermal injections of recombinant IL-23. Taken together, these findings illustrate an epidermal-innate immune crosstalk mediated by RAGE signaling that in turn affects adaptive immune responses and points towards a central role of RAGE in the IL-23/TH17 axis of psoriasis. Therefore, this thesis highlights RAGE signaling as a potential target for new therapeutic strategies intervening ‘upstream’ of IL-23.
An Exploration of the Lived Experiences of the Caregiving Role and Life Satisfaction in Caregivers Following Traumatic Spinal Cord Injury

Research shows that approximately 12,500 new spinal cord injuries occur each year, which poses a drastic life change for the person who sustained the injury and their closest family members and friends who often times become the ones who care for them in the community. Due to this new, unexpected caregiving role, research shows that many are often unprepared to care for the spinal cord injured person once they are discharged from one of the inpatient settings. The purpose of this study was to explore and examine the lived experiences of caregivers of spinal cord injury individuals. The results of this inquiry may benefit clinicians, patients, and caregivers with shared experiences and may help educate and guide future caregiver relationships. This study was guided utilizing van Manen’s six research activities of qualitative inquiry. Participants were recruited for this study through purposeful sampling until data saturation occurred. A total of six participants were recruited for this study; five completed the study. Face-to-face, audio-recorded interviews were conducted. Colaizzi’s 7-step approach was used for data analysis. Six main themes and two subthemes were identified during the interviews, which provided the overall essence to this qualitative study.
Scattering Wavelet Hash Fingerprints for Musical Audio Recognition

Fingerprint design is the cornerstone of the audio recognition systems in which aims robustness and fast retrieval. Short-term Fourier transform and Mel-spectral representations are common for the task in mind, however these extraction methods suffer from being unstable and having limited spectral-spatial resolution. Scattering wavelet transform (SWT) provides another approach to these limitations by recovering information loss, while ensuring translation invariance and stability. We propose a two-stage feature extraction framework using SWT coupled with deep Siamese hashing model for musical audio recognition. Similarity-preserving hashes are the final fingerprints and in the projected embedding space, similarity is defined by a distance metric. Hashing model is trained by roughly aligned and non-matching audio snippets to model musical audio data via two-layer scattering spectrum. Our proposed framework provides competitive performance results to identify audio signals superimposed with environmental noise which can be modeled as real-world obstacles for music recognition. With a very compact storage footprint (256 bytes/sec.), we achieve 98.2% ROC AUC score on GTZAN dataset
LAND RESOURCE EVALUATION FOR SUSTAINABLE LAND RESOURCE MANAGEMENT IN WESTERN PART OF WEST BENGAL, INDIA

Article History: Received 5, December, 2014 Received in revised form 12, December, 2014 Accepted 6, January, 2015 Published online 28, January, 2015 Land resources provide basic amenities to human society. Puruliya is the western most district of West Bengal falling plateau area and having undulating terrain. The main objective of the study is to integrate the functional attributes of land for land evaluation for sustainable land management. Functional attributes of land are integrated in GIS environment and generated three land management units: unit-I (good quality land), unit-II (moderate quality land), and unit-III (poor quality land). Management unit-II & unit-III cover almost equal area (about 45% area of the total geographical area) and unit-I cover nearly 10% area. Land management unit-I(good) & unit-II (moderate quality land) has highest priority for sustainable management as it has enough potentiality to develop the land for agriculture, agro-forestry and related activities through efficient use of water, use of bio-fertiliser and phosphorus rich fertiliser along with lime due to lack of phosphorus content in soil.
Image Super-Resolution Based on Structure-Modulated Sparse Representation

: The Super Resolution (SR) reconstruction has become a hot research topic in the field of Image Processing. Super resolution is all about generating high-resolution image from low-resolution image. High resolution image provides a high pixel density therefore provides more information about the original image. High resolution images are very much important for computer vision applications for better performance for pattern recognition and analysis of images. It is useful in medical imaging for diagnosis. It is very much useful for processing of satellite images. Also it is useful for other applications. In this paper we have discussed the techniques used for obtaining super resolutions. It is implemented using MATLAB.
Mitigation of SSR using Sub-Synchronous Current Control Technique – Analysis and Simulation using Matlab/Simulink

In this paper, the mitigation of sub–synchronous resonance using three single phase VSC based bridges (SSSC) will be presented. The sub-synchronous current will be separated using estimation algorithm as well as resonant filter and made zero by Injecting sub-synchronous voltage using novel control strategy as it is responsible for Sub-Synchronous resonance. The results obtained in both the cases are same. IEEE first benchmark model has been taken to analyse the effectiveness of the proposed control algorithm when mitigating SSR due to torsional interaction. The simulation has been done using MATLAB/SIMLINK software and results have been shown. As using three single phase bridges the cost of transformer is reduced so that overall cost decreases. As the voltage is being injected at sub-synchronous frequency generally the sub-synchronous voltage magnitude is very less the rating of SSSC is reduced.