Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECTS OF CAPSAICINOIDS ON CHOLESTEROL METABOLISM AND ITS RELATED GENE EXPRESSION IN RATS

Objective To determine the potential hypocholesterolemic effect of capsaicinoids in Sprague-Dawley rats fed different diets and its mechanism.Methods Thirty-two rats were randomly divided into 4 groups and received one of the following diets: control diet(C);control diet with capsaicinoids(10 mg/kg·bw)(CAP);high-fat diet(HF) and high-fat diet with capsaicinoids(10 mg/kg·bw)(HF+CAP),After 4 w,all mice were anesthetized.Serum and hepatic lipids,the concentration of bile acids in small intestine and feces were determined.The genes expression involved in cholesterol metabolism were determined by real-time PCR methods.Results The concentration of cholesterol in plasma and lipid accumulation in liver of CAP and C+HF+CAP group rats were significantly reduced.The hypocholesterolemic effect of capsaicinoids in HF+CAP group was more obvious than that in CAP group.The mRNA levels of 3-hydroxyl-3-methylglutaryl CoA reductase(HMG-CoA R) and cholesterol 7α-hydroxylase(CYP7A1) were significantly down-regulated while the mRNA levels of transient receptor potential vanilloid type-1(TRPV1),farnesoid X receptor(FXR),ileum bile acid binding protein(IBABP) and ileal apical sodium-dependent bile acid transporter(ASBT) were significantly up-regulated by capsaicinoids in basic and high-fat diet group rats.The concentration of bile acids in small intestine and feces was significantly decreased by capsaicinoids in basic diet group rats.Conclusion It is conceivable that the cholesterol-lowering action of capsaicinoids in rats fed basal diet and high-fat diet may be due to inhibited synthesis of hepatic cholesterol.
International Journal of Recent Technology and Engineering (IJRTE)

The wide scale use of facial recognition systems has caused concerns about spoofing attacks. Security is essential requirement for a face recognition system to provide reliable protection against spoofing attacks. Spoofing happens in situations where someone tries to behave as an authorized user to obtain illicitly access the protected system to gain advantage over it. In order to identify spoofing attacks, face spoofing detection approaches have been used. Traditional face spoofing detection techniques are not good enough as most of them focus only on the gray scale information and discarding the color information. Here a face spoofing detection approach with color texture and edge analysis is presented. The approach for investigating the texture of input images, Local binary pattern and Edge Histogram descriptor are proposed. Experiments on a publicly available dataset, Replay attack, showed excellent results compared to existing works.
International Journal of Recent Technology and Engineering (IJRTE)

3226 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Retrieval Number: F7770038620/2020©BEIESP DOI:10.35940/ijrte.F7770. 038620 Abstract: Predicting the probability of hospital readmission is one of the most vital issues and is considered to be an important research area in the healthcare sector. For curing any of the diseases that might arise, there shall be some essential resources such as medical staff, expertise, beds and rooms. This secures getting excellent medical service. For example, heart failure (HF) or diabetes is a syndrome that could reduce the living quality of patients and has a serious influence on systems of healthcare. The previously mentioned diseases can result in high rate of readmission and hence high rate of costs as well. In this case, algorithms of machine learning are utilized to curb readmissions levels and improve the life quality of patients. Unluckily, a comparatively few numbers of researches in the literature endeavored to address this issue while a large proportion of researches were interested in predicting the probability of detecting diseases. Despite there is a plainly visible shortage on this topic, this paper seeks to spot most of the studies related to predict the probability of hospital readmission by the usage of machine learning techniques such as such as Logistic Regression (LR), Support Vector Machine (SVM), Artificial Neural Networks (ANNs), Linear Discriminant Analysis (LDA), Bayes algorithm, Random Forest (RF), Decision Trees (DTs), AdaBoost and Gradient Boosting (GB). Specifically, we explore the different techniques used in a medical area under the machine learning research field. In addition, we define four features that are used as criteria for an effective comparison among the employed techniques. These features include goal, data size, method, and performance. Furthermore, some recommendations are drawn from the comparison which is related to the selection of the best techniques in the medical field. Based on the outcomes of this research, it was found out that (bagging and DT) is the best technique to predict diabetes, whereas SVM is the best technique when it comes to prediction the breast cancer, and hospital readmission.
Transfer Learning for Classification of 2D Brain MRI Images and Tumor Segmentation

The focus of the paper is to classify the images into tumorous and non-tumorous and then locate the tumor. Amongst many medical imaging applications segmentation of Brain Tumors is an important and arduous task as the data acquired is disrupted due to artifacts being produced and acquisition time being very less, so classifying and finding the exact location of tumor is one of the most important jobs. In the paper, deep learning specifically the convolutional neural network is used to demonstrate its potential for image classification task. As the learning from available dataset will be low, so we use transfer learning [4] approach, as it is a developing AI strategy that overwhelms with the best outcomes on several image classification assignments because the pre-trained models have gained good knowledge about the features by training on a large number of images. Since, medical image datasets are hard to collect so transfer learning (Alexnet) [1] is used. Later on, after successful classification the aim is to find the exact location of the tumor and this is achieved using basics of image processing inspired by well-known technique of Mask R-CNN [9].
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Image segmentation is one of the important and useful techniques in medical image processing. As the image segmentation technique results robust and high degree of accuracy, it is very much useful for the analysis of different image modalities, such as computerized tomography (CT) and magnetic resonance imaging (MRI) in the medical field. CT imaging gives more importance than MRI because of its wider availability, inexpensive and sensitiveness. In most cases, CT offers information needed to make decisions during urgent situations.
Chi-Square and Entropy (CS-E):A Hybrid Method for DDoS Attack Detection and Trace Back

Internet becomes unavoidable and it provides us with a wealth of information and allows us to keep in touch with the outside world. However, there can also be risks on the internet that is, for example, even a naive hacker can access information and easily learn to generate a large scale DDoS attack with the help of downloadable user-friendly attacking tools. Nowadays, this has made even small businesses in trouble. One of the extensive DDoS attacks was done on October 2016 which is called “Mirai botnet”. In that, the attackers send 30 million packets per second to attack the financial department, industries, home system, etc. were affected. In the future, the attackers may hit the hardest even as banks, government sectors, and corporate sectors, etc. On DDoS attack time, the attackers are sending a lot of malicious packets to the server/victims. So the attacker’s throughput is increased and legitimate user throughput is decreased on time of the attack. In this paper, a novel approach is proposed to detect the DDoS attacks using Chi-Square method which compares the normal packets and current packets statistics to discriminate whether the particular flow is DDoS or not. Further; it identifies the IP address of attacking source using entropy statistic. The proposed method can be used to control internet crimes. The experimental results show that the proposed method outperforms the existing approaches by detecting the DDoS attack and also by identifying the wrongdoer IP address. In addition, it takes minimum time to perform the above.
International Journal of Recent Technology and Engineering (IJRTE)

Dementia is the brain disorder, that effects the mental cognitive function. Dementia has different stages. when the person is in the early stage they have short memory, losing or misplacing things, the person have more memory loss in moderate stage. During last stage patient completely depends on other for everyday activities. MCI has come to be recognized as an intermediate state of clinical impairment where an individual has mild cognitive symptoms but generally continue to function normally in the community. it is important to determine AD at an earlier stage which is Mild Cognitive Impairment (MCI). Toxic changes may start in the brain at the beginning stage of Alzheimer's disease. MRI Images are used to detect disease at the earlies stage. Wavelet Transform is applied on the MRI Images followed by any feature selection. Machine learning algorithm is used to make prediction. This paper goes through all those studies and techniques used by scientists to unravel the progression of Alzheimer's disease.
International Journal of Recent Technology and Engineering (IJRTE)

Paedophile is an issue that can cause nightmare to children who have become victims as they are likely to suffer physically and emotionally for the rest of their lives. Paedophile awareness is still generally low among Malaysians, especially children, that a creative and attractive method is required to deliver the crucial message. This research focused on the integration of animated comic and interactive multimedia using storytelling elements to create awareness about paedophile. Scenes embedded with the message are put into comic form to attract children’s attention and increase their understanding. Therefore, this study aims to develop an interactive application, called ‘i-ComPedo’ (Interactive Animated Comic of Paedophile Awareness among Children. The study adapts 3-phases research activities for the research methodology, which are analysis and design, development and testing. In order to evaluate the usability of ‘i-ComPedo’, this study conducted usability that involved primary school children. The usability test revealed that 91.3% of the children perceived ‘i-ComPedo’ as an interesting, understandable, user-friendly courseware. As a conclusion, an interactive animated comic has been developed to offer a new and innovative approach in instilling awareness among children about paedophile.
International Journal of Recent Technology and Engineering (IJRTE)

6026 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Abstract: Osteoarthritis is the most broadly recognized disease in the knee joint that affects the cartilage, especially among the old age or overweight people. In the normal knee joint, the smooth and thin layer called cartilage covers the joint space of the bone and makes the joint smooth and prevents them from rubbing against one another, but can break, when the cartilage gets ruptured due to which bones start rubbing with one another, and this may cause severe pain, swelling and stiffness in the knee joint. The evaluation for osteoarthritis detection includes a clinical examination, and different medical imaging techniques are X-RAY images and MRI scans. There is developing method required for classification frameworks that can precisely distinguish and identify knee OA from plain radiographs. In this method we have examining the strategy of computer aided diagnosis for early identification of knee OA. Based on the procedure of x rays through computer image processing, segmentation, feature extraction and investigation by means of building a classifier, a viable computer aided detection method for knee was made to help specialists in their precise, convenient and identification of potential risk of OA. For this method a total of 126 knee x ray image were collected for assessing the knee OA. In this paper, we tried to diagnose about the normal or abnormal detection of cartilage depreciation. The HOG and DWT features are extracted from X-ray images of the knee joints. The extracted features are classified with two different machine learning classifiers, namely the SVM and ANN Patternet classifiers, and the results are demonstrated. The SVM classification is good when compared with ANN and provides a satisfactory accuracy rate of 85.33%. At last the classifier was superior both in time effectiveness and classification execution to the regularly utilized classifiers based on iterative learning. In this way it was suitable to utilize as a computer aided tool for the diagnosis of OA.
International Journal of Recent Technology and Engineering (IJRTE)

2277 Retrieval Number: E7008018520/2020©BEIESP DOI:10.35940/ijrte.E7008. 038620 Published By: Blue Eyes Intelligence Engineering & Sciences Publication Abstract: Big data has revolutionized every field of life, which accumulates human learning as well. The field of education has progressed in past couple of decades, and addition to that, rapid growth in the number of educational institutions has created a tough competition. The massive accumulation of data in the educational sector has created a great scope of EDM (Educational Data Mining) with the support of robust predictive models. It is quite necessary to regularly examine the performance of the students to make them perform better, thus helps to maintain the reputation of the institution. This study proposed a predictive model through which the performance of the student can be forecasted depending upon various characteristics. The KDD(Knowledge Discovery in Databases) methodology was followed stepwise in this study for developing predictive models to predict student performance. The data balancing techniques such as SMOTE (Synthetic Minority Over-sampling Technique) and ADASYN (Adaptive Synthetic Sampling) were employed to handle the unbalanced effect of data which causes bias predictions. Also, for the selection of significant features techniques, FCBF (Fast Correlation Based Feature selection) and RFE (Recursive Feature Elimination) were used. The EDM algorithms Random Forest (RF), Support Vector Machine (SVM) and Artificial Neural Network (ANN) were utilized for predicting student performance with suitable hyper-parameter tuning using random search to enhance the performance of the model. The results obtained were cross-validated using Ensemble Method and benchmarked with previous studies. The random forest model achieved the highest accuracy of 86% after data balancing and careful selection of significant features.
Dual-Band Quarter Mode SIW Cavity Back Slot Antenna

A miniaturized quadrant slot antenna backed by the cavity using QMSIW technique is developed for dual-frequency applications. The design process is begun with FMSIW circular cavity which has been cut along magnetic wall twice to get quadrant sector of a circular cavity that is said to be a QMSIW cavity. The QMSIW cavity excited in TM210 and TM020 modes is loaded by quadrant slot which helps to decrease the resonant frequencies. The CST microwave studio is used to study the operating mechanism of various modes in the cavity. The antenna has been fabricated and measured the S11 and principle pattern and also compared with simulated results. The experimental results prove that the antenna S11 in dB is low at dual-frequency. One of the resonant frequencies is at 8.05 GHz and other 9.95 GHz with peak gains are around 6.25 dBi and 6.45 dBi respectively.
Efficient and Reliable Communication to Reduce Broadcast Storm in Rural and Urban Scenario using RGHN and UGHN Protocol

In order to reduce overhead and to improve throughput an effective in Rural Group Head Node(RGHN) and Urban Group Head Node (UGHN) protocol can be implemented in the respective rural and urban scenarios. The first section deals with the efficient implementation of RGHN and UGHN protocols in VANET. In the next phase, working of RGHN in rural areas and UGHN protocol in urban areas are described and the performance analyzed. The developed protocol RGHN and UGHN needs comparison with the standard protocols under both the scenarios. The performance of the proposed protocol RGHN in rural scenario is compared with that of Dynamic Source Routing (DSR) and UGHN in urban scenario is compared with Ad-hoc On demand Distance Vector (AODV) shows the proposed technique providing a better result in terms of throughput, packet delivery ratio, delay and packet loss ratio in the network.
Research Trends in Hand Gesture Recognition Techniques

Over years’ key board and mouse are among the most effective input devices to the computers. Not much improvement has happened for other modes of inputs like voice and gestures. These kinds of inputs explore the limitations caused by the key board and mouse, which needs actual physical contact. With gestures and voice the physical proximity can be little relaxed and can be used by specially-abled people. Exploring effective Human Computer Interactions (HCI) has always been of great interest for development of new techniques and methods. Gestures are considered as more easy, natural, and intuitive method for communicating with systems. Hand recognition and analysis forms the core of these systems. This paper provides comparative study on state of art technologies that work in this area. The different representation, detection and recognition techniques are discussed. The strength and challenges of different techniques are highlighted. The main aim of this research is to summarize the research progress done till date with different technologies for gesture recognition algorithms, identify areas which need further research.
International Journal of Recent Technology and Engineering (IJRTE)

Mortality rate of lung cancer is increasing very day all over the world. Early stage lung nodules detection and proper treatment is solution to reduce the deaths due to lung cancer. In this research work proposed integrated CADe/CADx system segments and classifies lung nodules into benign or malignant. CADe phase segments Well Circumscribed Nodules (WCN), Juxta Vascular Nodules (JVN) and Juxta Pleural Nodules (JPN) of different size in diameter. This part uses algorithms proposed in our previous WCN, JVN and JPN lung nodules segmentation work. CADx performance classification of segmented WCNs, JVNs and JPNs nodules into benign or malignant. In first part of CADx system hybrid features of segmented lung nodules are extracted and features dimension vector is reduced with Linear Discrimination Analysis. Finally, Probabilistic Neural Network uses reduced hybrid features of segmented nodules to classify segmented nodules as benign or malignant. Proposed integrated system achieved high classification accuracy of 94.85 for WCNs, 97.65 for JVNs and 97.96 for JPNs of different size in diameter (nodules diameter< 10mm, nodules diameter >10mm and < 30mm, nodules diameter >30mm and <70mm). For small nodules achieved classification performance values are, accuracy of 94.85, sensitivity of 90 and specificity of 95.85. And nodules of size 10mm to 30mm obtained accuracy, sensitivity and specificity are 97.85, 97.65 and 94.15 respectively.
International Journal of Recent Technology and Engineering (IJRTE)

Awareness towards the enviroment by the university community has started since 1970 through various activities such as ‘Earth Day’celebration. The aim of such activities was to make sure that the university emphasized on the sustainanble practices among the community in preserving the enviroment. The initiative to transform the local university to a sustainable university has been carried out by a number of research universities, namely; UTM, UKM, USM, UPM, and UM. This can be seen in various efforts carried out by the university such as setting up research centre solely for the enviroment; carrying out activities for sustainable university, forming sustainability policy and etc. However all these efforts would be furtile if a metric was not used to measure the level of sustainability to evaluate through practices at the university. A research has been done to develop a metric evaluation for sustainable university that has characteristics of sustainability. This research is carried out by interviewing 13 sustainability experts who are well-werse in the field of social economy and enviroment. The outcome of the research are the characteristics and sub elements of a sustainable university together with a comprehensive metric evaluation. This metric evaluation will function as a bench mark to observe the initiative level of sustainability at UTM as well as other local universities. This metric would guide universities to assess the level of sustainability of the university.
Securing the Network by the Intruder using Predictive Accession

Today, it is very crucial to deliver a immense level security to protect highly delicate and private information. Intrusion Detection System is an essential technology in Network Security. So my aim is to use IDS system and meliorate the performance of the IDS. The main target of Intrusion detection and prevention systems (IDPS) is to determine the feasible incidents, logging clue about them and in report attempts. Intruder is the person who tries to interrupt the network. So, it is essential to present a high level security to defend extremely susceptible and confidential information. Key aim of this thesis is to secure the network from the intruders identifying them also predicting them for the next transactions within the network. For that modified genetic algorithm is used to rank the parents and produce better individuals for the prediction of the intruders and provide better security to the network. Also improve the performance of genetic parameters.
International Journal of Recent Technology and Engineering (IJRTE)

Researchers’ are taking keen interest in Optimization algorithms due to their heuristic and meta-heuristic nature. Heuristic algorithms find the arrangement by utilizing the experimentation strategy. Then again, meta-heuristic algorithms discover the response at a more elevated tier. Several nature-based metaheuristic algorithms are easily accessible. Askarzadeh has introduced the Crow search algorithm and stated that it is meta-heuristic optimization algorithm. The astute conduct of the crow moves CSA. Crows are keen on putting away the abundance nourishment at concealing spots and recuperating it at whatever point it is needed. CSA's previous outcomes show that it can unravel different complex building related optimization issues. There are six compelled building plan issues, and CSA is utilized to upgrade these issues. This paper focuses on a far-reaching investigation of CSA in the diverse application is given with the examination just as the exhibitions of the CSA in the different structure is talked about.
International Journal of Recent Technology and Engineering (IJRTE)

Experiments were conducted to investigate the cooling of processor to increase the thermal performance by employing a mini channel instead of conventional heat sinks. Now a day’s aluminium fin with fans is used for cooling the processor. Constant speed of the fans is found to be not enough to remove the heat generated by the processor. The experimental investigations were carried out in the channels with the hydraulic diameter of about 1.5x10 -3 m for the Reynolds number varying from 80 to 1150. The water is allowed to pass through the channel by virtue of which heat is rejected from the processor. The influence of Reynolds number on heat transfer enhancement from the microprocessor is discussed in details. Comparison between heat transfer by air and by water is presented. From the experiment it is disclosed that further increase in heat transfer was observed when compared to air.