Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Quality of Service Based Data Dissemination Scheduling Approach in Vanet

Data services for in-vehicle consumption are expected to become a primary driver in the development of future vehicular networks. Recent research shows the inter-vehicle data dissemination problem based on a Wireless Access in Vehicular Environment (WAVE) 1/ 802.11 physical vehicular ad-hoc network, using a network coding. But still the research needs to improve the Quality of Service (QoS) and maximization of the throughput to achieve the resource reservation and sufficient vehicle to vehicle infrastructure. In this work, an efficient packet scheduling scheme, where the trade off channel condition and QoS is factored according to the user traffic. It can be performed by adding the exponent term to the part of DRC (Data Rate Control) which indicates the channel condition, in the modified proportional fair algorithm. The proposed scheme significantly outperforms existing techniques in terms of the parameters like network throughput and energy consumption. An experimental result of the proposed schema shows the enhancement of overall throughput in Vehicle-to-Vehicle (V2V) communication without violating the QoS metrics.
A Modified Approach for Face Recognition using PSO and ABC Optimization

: . Face Recognition (FR) has established noteworthy attention in the recent years due to its wide applications in various fields. FR technology uses image processing algorithms for comparing and verifying the human faces. This paper proposes a FR system which employs a novel mixture of Fast Fourier Transform (FFT), Discrete Cosine Transform (DCT) and Swarm Intelligence (SI). FFT and DCT help in extracting the features efficiently. PSO and ABC are used for feature selection that finds the best solution for a given problem. Many hybrid approaches are used to overcome its weakness and more over fit for various applications. The usage of transformation techniques helps in reducing image information redundancy. Experimental results show that there is a notable reduction in the number of features and a considerable increase in the recognition rate.
WIRELESS SENSOR NETWORK FOR MOBILE HEALTHCARE SYSTEM: ISSUES SURVEY

: By utilizing the advance, recent technologies to improve and archive better health monitoring. Health monitoring is the new technology has been potentially to offers wide range of benefits to the patients such as continuous monitoring, early detection of abnormal conditions. A discussion is about issues and their solutions for improving healthcare system. Healthcare system still facing the challenges such as reliability, security and privacy, lack of resource sharing and computing process. With the pervasiveness of smart phone and wearable sensor nodes are extends the operations of healthcare providers in the pervasive environment. This paper discusses the implementing issues in healthcare system while patients in emergency conditions. This is suitable only for the particular environment. In future it will be globalize for improving healthcare for life-threatening sutiation.
Correlation of blood groups, Bleeding time and Clotting time in male and female students; an observational study

Stoppage of bleeding from damaged blood vessels is hemostasis, an essential factor to be checked before any surgical process. Hence blood test is a routine test for hospital prior to surgeries. The study was aimed to assess the distribution of blood groups and to check gender-based relationship among the blood groups, bleeding and clotting time. The study was conducted in the department of pharmacy, BZU. The available data of 122 students of 1st year of Pharm. D was analysed in every required aspects i.e. sex, age, ABO and Rh blood groups, bleeding and clotting times. The standard antisera (A, B, & D) were used to determine the blood group, Duke Method and Capillary tube method were used to find out bleeding and clotting time respectively. Finally, all the parameters were compared and analysed statistically. In the present study, it is found that B blood group is more prevalent in both the sexes than O, A and AB. Clotting time is found to be significantly higher in O and B blood groups in females and males respectively, whereas Bleeding time was found to be greater than others in AB blood group in females and ‘O’ blood group in males but the values are not significantly different from each other. In females, the values of bleeding and clotting time are slightly greater than in males, may be because of hormonal differences. Various cardiovascular and gastrointestinal diseases are associated with the blood groups, so people can take preventive measures according to their blood groups. Received: May 17, 2016 Revised: June 6, 2016 Accepted: June 13, 2016 Online: June 15, 2016 Faculty of Pharmacy, Bahauddin Zakariya University, Multan, Pakistan
Family Communication Dimensions, Separation-Individuation and Imaginary Audience in Iranian Adolescents

Adolescents ُ egocentrism is a controversial concept in cognitive developmental theories and there are little studies that investigated its antecedents. The present study examined a causal model of the relationships between dimensions of family communication, separation-individuation and imaginary audience in adolescence period. Participants were 356, boys and girls, high school and college students of Shiraz city. Participants completed the New Imaginary Audience Scale (NIAS), Pathology of separation-individuation scale (PATHSEP) and The Revised Family Communication Patterns instrument (RFCP). Results showed that conversation was a negative and conformity was a positive predictor of the imaginary audience (IA). When the dimensions of family communication patterns and PATHSEPas predictors of the imaginary audiencewere tested together, results clearly revealed the mediation role of PATHSEP between FCP and IA. This explanatory model is elaborated in the article.
PRIVACY-AND INTEGRITY-PRESERVING ENTIRE RANGE QUERIES IN SENSOR NETWORKS

The architecture of two-tiered sensor networks, where storage nodes serve as an intermediate tier between sensors and a sink for storing data and processing queries has been widely adopted because of the beneﬁts of power and storage saving for sensors as well as the efficiency of query processing. However, the importance of storage nodes also makes them attractive to attackers. This paper we proposed Paillier Cryptosystem that prevents attackers from gaining information from both sensor collected data and sink issued queries. To preserve Privacy, A paillier cryptosystem is a modular, public key encryption scheme, using this scheme both data and query message are encrypted such that storage node can correctly decrypt messages of an encrypted query over encrypted data without knowing their value in the message. To preserve integrity , we propose two schemes—one using Merkle hash trees and another using a new data structure called neighborhood chains—to generate integrity veriﬁcation information so that a sink can use this information to verify whether the result of a query contains exactly the data items that satisfy the query.
Performance Evaluation of Intrusion Detection System Using Classification Algorithms

: Communication systems are vital for effective transfer of information over a network. However, there are lot of threats in maintaining confidentiality and promoting high level security of information to increase the quality of network communication and increase the trust of the people involved in it. The main aim of this paper is to analyze performance of Intrusion Detection System using various classification approaches. The objective of this paper is to analyze and predict the network attacks by classifying them as normal and anomaly and to implement and measure the performance of our system here we the standard KDD99 benchmark dataset. An implementation of an Intrusion Detection System is proposed using Statistical methods, Probabilistic method, Ensemble methods and Rule based classifiers. The experimental results clearly shows that system achieve high accuracy rate in identifying whether the records are normal or anomaly ones and lower false alarm with reduced number of features using rule based classifiers.
A Secure Outsourced Encryption Based Access Control Model for Cloud Based Services

: With the rapid development of computer technology, cloud-based services have become a hot topic. They not only provide users with convenience, but also bring many security issues, such as data sharing and privacy issue. In this paper, we present an access control system with privilege separation based on privacy protection (PS-ACS). In the PS-ACS scheme, we divide users into private domain (PRD) and public domain (PUD) logically. In PRD, to achieve read access permission and write access permission, we adopt the Key-Aggregate Encryption (KAE) and the Improved Attribute-based Signature (IABS) respectively. In PUD, we construct a new multi-authority ciphertext policy attribute-based encryption (CP-ABE) scheme with efficient decryption to avoid the issues of single point of failure and complicated key distribution, and design an efficient attribute revocation method for it. The analysis and simulation result show that our scheme is feasible and superior to protect users’ privacy in cloud-based services.
A Novel Framework for Satellite Image Resolution Enhancement Using Wavelet-Domain Approach Based On Dt-Cwt and NLM

In this paper, Resolution enhancement (RE) model has one of the biggest disadvantage (drawback) is losing high frequency contents (which results in blurring). Discrete wavelet transform based (DWT) RE scheme generates artifacts due to a DWT shift-variant property. For RE of the satellite images, here, new wavelet domain approach based on dual-tree complex wavelet transform (DT-CWT) and nonlocal means (NLM) is proposed. A satellite input image is decomposed by DT-CWT which is nearly shift invariant to obtain high-frequency sub bands. To interpolate the high-frequency sub bands and the low-resolution (LR) input images; in this paper the Lanczos interpolator is used. The high frequency sub bands are passed through an NLM filter to cater for the artifacts generated by DT-CWT. To obtain a resolution-enhanced image we are combining the filtered high-frequency sub bands and the LR input image by using inverse DT-CWT. Objective and subjective analyses show superiority of the new proposed technique over the conventional and state-of-the-art RE techniques.
An Automated Text Detection Algorithm for Unstructured Scene

: This paper presents an automated scene text detection algorithm based on Stroke Width Transform (SWT), Maximally Extremely Regions (MSER) and candidate classification. Firstly, utilize the SWT and MSER to extract the candidate characters at the same time. Secondly, preliminary filtering the candidate connected components based on heuristic rules. Thirdly, using mutual verification and integration to class all candidates into two categories: strong candidates, weak candidates. If the weak candidate has similar properties with strong candidate, then the weak candidate is changed into strong candidate. Finally, the text area is aggregated into text lines by text line aggregation algorithm. The experiment results on public datasets show that the proposed method can detect text lines effectively.
Enhanced Crop Yield Prediction and Soil Data Analysis Using Data Mining

: Agriculture is a backbone of Indian economy that is the main income source for most of the population in India. So farmers are always curious about yield prediction. Crop yield depends on various factors like soil, weather, rain, fertilizers and pesticides. Several factors have different impacts on agriculture, which can be quantified using appropriate statistical methodologies. Applying such methodologies and techniques on historical yield of crops, it is possible to obtain information or knowledge which can be helpful to farmers and government organizations for making better decision and policies which lead to increased production. The objective of the work is to compare various data mining techniques which gives the maximum accuracy. Data mining is only the way that assists to convert huge data into technologies and make them available to the farmers. The huge amount of data can be utilized to mine nugget of knowledge that can be useful for farmers and decision makers to take effective and prompt decision. In this paper one of major parameter which is used to increase crop production is considered; that is soil. Different classification algorithms are applied to soil data set to predict its fertility. This paper focuses on classification of soil fertility rate using K-Means, Random Tree, and Apriori.
Metaboreflex activation by rhythmic exercise as a cardiovascular risk in metabolic syndrome and obesity

Introduction: There is increasing prevalence of metabolic syndrome (MS) and obesity. Dysregulation in a mechanism causing cardiovascular adjustments during exercise (Metaboreflex; a feed-back mechanism originating in skeletal muscle due to local metabolites) has been studied in MS and obesity. Aims & objective: To see the effect of metaboreflex by rhythmic handgrip exercise on hemodynamic parameters (Blood pressure (BP), cardiac output(CO), stroke volume(SV), pulse rate (PR), systemic vascular resistance(SVR)) in subjects with MS & obesity. Method & materials: 46 subjects, aged 25 to 45 years of both sex were enrolled & classified into a) subjects without MS with normal or overweight BMI b) subjects without MS with obese BMI c) subjects with MS with obese BMI and d) subjects with MS with morbid obese BMI. Hemodynamic parameters were evaluated by impedance cardiovasography in supine position. Maximum Voluntary Contraction was measured by Handgrip Dynamometer. Rhythmic handgrip exercise was done at 30% of the subjects MVC (for 2 mins), followed by post exercise cuff occlusion to enhance metaboreflex, for 2 mins (20 mm Hg above systolic). The parameters were assessed post-rhythmic exercise. Results & conclusion: Following exercise, there was vasoconstriction mediated pressor response (statistically significant increase in diastolic BP) and flow mediated pressor response (statistically significant increase in systolic BP) in normal or overweight subjects without MS. In subjects without MS with obese BMI and subjects with MS with obese BMI, statistically significant decrease in SVR was found. In subjects with metabolic syndrome with morbid obese BMI, decrease in SV and statistically significant increase in PR was found.
Evaluation of the biological effect of brahmi (Bacopa monnieri Linn) extract on the biodistribution of technetium-99m radiopharmaceuticals

Natural products have been widely used by human beings in different medical and domestic applications. However, sometimes the biological effects of these products are not fully known. We investigated the effect of the oral ingestion of Bacopa monniera Linn (Brahmi) extract (BM) on the biodistribution of the radiopharmaceuticals i.e., 99mTc labeled ethylene dicysteine diethyl ester (99mTc-ECD) and 99mTc labeled cystine dimethyl ester (99mTc-CDM) in female Sprague Dawley rats. The rats (n = 5) were treated with BM extract (1 ml) and 0.9% NaCl solution (control, n = 5) for 8 days. After this period, 99mTc-ECD and 99mTc-CDM were injected and the rats were sacrificed rapidly. The organs (brain, heart, liver, lung, spleen, muscle, kidney, intestines and urine) were isolated and radioactivity in each organ was counted. The results showed that BM extracts altered the percentage of radioactivity of 99mTc-ECD and 99mTc-CDM in brain, heart, liver, lung, spleen, muscle, kidney, intestines and urine with respect to control. This finding was probably an example of drug interaction with a radiopharmaceutical, and a fact that could lead to misdiagnosis of the examination in clinical practice with unexpected consequences for the patient. [Life Science Journal. 2008; 5(2): 45 – 49] (ISSN: 1097 – 8135).
A Fire Fly Optimization Based Video Object Co-Segmentation

-Video object co-segmentation is a method of segmenting and discovering primary object from multiple videos without human annotation. In this paper, we are presenting a firefly based video object co-segmentation method. Here we are using SIFT flow method for object segmentation and firefly algorithm for object optimization. SIFT flow descriptor discovers the corresponding object using inter-frame motion flow from optical flow. But it will not capture the optimal motion of inter-frame. So we are using Firefly approach in addition to SIFT flow. Firefly approach captures optimal inter-frame motion based on the velocity updation and position of the particle for better results. SIFT flow produces segmented primary objects over the entire video data set. Finally we combine this segmented object to get high quality original videos. The results that we obtained from conducting the experiment show that the Firefly based co-segmentation method achieves high accuracy compared with the existing co-segmentation method.
Extending the Multilevel Security for Pervasive computing by using UPnP-UP Networks

— The Universal Plug and Play (UPnP) specification defines a set of protocols for promoting pervasive network connectivity of computers and intelligent devices or appliances.Nowadays, the UPnP technology is becoming popular due to its robustness to connect devices and the large number of developed applications. One of the major drawbacks of UPnP is the lack of user authentication and authorization in multilevel security. Thus, control points, those devices acting as clients on behalf of a user, and UPnP devices cannot communicate based on user information. This paper introduces an extension of the UPnP specification called UPnP-UP, which allows user authentication and authorization mechanisms for UPnP devices and applications. These mechanisms provide the basis to develop customized and secure UPnP pervasive services
Climate Changes and Human Infectious Diseases

Because of being partially attributable to the varying effects of climate change, spatio-temporal scale, and different types of hostpathogen systems, the debate on the potential human health impacts remains polarizing, in spite of a growing progress in determining climate change effects on human infectious diseases. While regions geographically experiencing higher temperature anomalies have been provided more study attention, the Earth’ s most vulnerable regions to climate variability and extreme events unfortunately have been less studied. Agreements on the response of human infectious diseases to climate change tend to converge from local to global scales. Then the number of mechanistic studies are slowly growing, with abundance of findings of rapidly growing of statistical methods, for examples, using seasonal auto-regressive integrated moving average (SARIMA) model with local weather conditions in forecasting hand-foot-mouth disease, using generalized estimating equation models/multivariate random-effects meta-regression analyses in quantifying the city-specific climate change-malaria associations, etc. The authors extracted the published evidence and demonstrated that over the past three years (2016 to early 2018), the negative or uncertain reports on responses of human infectious diseases to climate change has been increasing. Research gaps and trends found in this study should be addressed in the near future, including impact of climate change on respiratory infectious diseases and human infectious diseases other than vector-borne diseases.
Pattern Recognition using Two-Dimensional Kernel Non-negative Matrix Factorization Algorithm

: Pattern recognition and classification tasks use machine learning that require a suitable representation of the pattern. Non-negative matrix factorization (NMF) has to be a useful decomposition and representation of multivariate data pattern. Two different multiplicative algorithms for NMF are analyzed. In this paper, we extend the original NMF to 2-Dimensional kernel non-negative matrix factorization (2DKNMF) to improve its performance. 2DKNMF algorithm is derived based on nonlinear kernel function to map patterns to feature space and stepwise method is applied on each individual pattern in feature space to avoid complex computations. The advantages of 2DKNMF algorithm are based on extract more useful feature hidden in the original pattern and it can process data with negative values by using some specific distribution kernel functions. Experimental results on several face databases show that 2DFNMF has better image reconstruction quality than NMF. Also the running time of 2DFNMF is less, and the recognition accuracy higher than that of NMF.
Betterment of Fingerprint Template Protection Schemes – A Review

—Biometric security threat is an issue of dynamic attack against weakness in a biometric system. While biometrics may enhance security detached situations, in the same way as some other security system, it has vulnerabilities and it is vulnerable to threats. They are defenseless to outer vulnerabilities of biometric systems so that their shortcomings can be discovered and valuable counter measures against predictable attacks can be created. In this review, we have taken different categories of template protection methods and stated the evolution, usage, merits and demerits of each category along with comparison of the metrics GAR, FAR, FRR and EER in tabular form. This paper also suggests an ideology of a hybrid scheme of fingerprint template protection that will overcome the issues faced in prior approaches.
Gain and Bandwidth Enhancement in CMOS Low-Voltage Low-Power Operational Amplifiers

In this paper, a low-voltage low-power CMOS operational amplifier using the composite cascode technique is presented. This technique has been employed in the differential input pair and output transistors to enhance the gain of op-amp. Also, indirect compensation is used to improve the frequency response of the op-amp and avoids instability when a large capacitive load at the output of the op-amp must be handled. Two-stage op-amp is designed and simulated in a TSMC 0.18 μm CMOS technology, to evaluate the proposed technique. The sub-threshold region is employed in the design to use the low supply voltage and reduce power consumption effectively. The op-amp operates at a 0.7 V power supply with 891 nW power consumption. The open-loop gain is 90.1 dB, the unity gain-bandwidth (UGBW) is 309 kHz, and the phase margin is 57.6 degree under 15 pF load.
Scope of Deep learning in medical image analysis: A survey

 Abstract — Deep learning algorithms, in particular convolutional neural networks, have rapidly become a methodology of choice for analyzing medical science images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes various contributions to the field, most of which appeared in the last year. Survey includes the use of deep learning for object detection, image classification, segmentation, registration, and other tasks. Brief overviews are provided of studies per application area: neuro, retinal, digital pathology, cardiac, and breast. We end with a summary of the present state-of-the-art, an acute discussion of open challenges and directions for future research.