Search results for: SIFT combination

Authors: Wei-Jong Yang, Wei-Hau Du, Pau-Choo Chang, Jar-Ferr Yang, Pi-Hsia Hung

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The demands of smart visual thing recognition in various devices have been increased rapidly for daily smart production, living and learning systems in recent years. This paper proposed a visual thing recognition system, which combines binary scale-invariant feature transform (SIFT), bag of words model (BoW), and support vector machine (SVM) by using color information. Since the traditional SIFT features and SVM classifiers only use the gray information, color information is still an important feature for visual thing recognition. With color-based SIFT features and SVM, we can discard unreliable matching pairs and increase the robustness of matching tasks. The experimental results show that the proposed object recognition system with color-assistant SIFT SVM classifier achieves higher recognition rate than that with the traditional gray SIFT and SVM classification in various situations.

Keywords: color moments, visual thing recognition system, SIFT, color SIFT

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Authors: Daliyah S. Aljutaili, Redna A. Almutlaq, Suha A. Alharbi, Dina M. Ibrahim

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All currencies around the world look very different from each other. For instance, the size, color, and pattern of the paper are different. With the development of modern banking services, automatic methods for paper currency recognition become important in many applications like vending machines. One of the currency recognition architecture’s phases is Feature detection and description. There are many algorithms that are used for this phase, but they still have some disadvantages. This paper proposes a feature detection algorithm, which merges the advantages given in the current SIFT and SURF algorithms, which we call, Speeded up Robust Scale-Invariant Feature Transform (SR-SIFT) algorithm. Our proposed SR-SIFT algorithm overcomes the problems of both the SIFT and SURF algorithms. The proposed algorithm aims to speed up the SIFT feature detection algorithm and keep it robust. Simulation results demonstrate that the proposed SR-SIFT algorithm decreases the average response time, especially in small and minimum number of best key points, increases the distribution of the number of best key points on the surface of the currency. Furthermore, the proposed algorithm increases the accuracy of the true best point distribution inside the currency edge than the other two algorithms.

Keywords: currency recognition, feature detection and description, SIFT algorithm, SURF algorithm, speeded up and robust features

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Authors: Simone B. K. Aires, Cinthia O. de A. Freitas, Luiz E. S. Oliveira

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This paper contributes to the CBIR systems applied to trademark retrieval. The proposed model includes aspects from visual perception of the shapes, by means of feature extractor associated to a non-symmetrical perceptual zoning mechanism based on the Principles of Gestalt. Thus, the feature set were performed using Scale Invariant Feature Transform (SIFT). We carried out experiments using four different zonings strategies (Z = 4, 5H, 5V, 7) for matching and retrieval tasks. Our proposal method achieved the normalized recall (Rn) equal to 0.84. Experiments show that the non-symmetrical zoning could be considered as a tool to build more reliable trademark retrieval systems.

Keywords: CBIR, Gestalt, matching, non-symmetrical zoning, SIFT

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Authors: Yawen Wang, Hongchang Chen, Shaomei Li, Chao Gao, Jiangpeng Zhang

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As the main challenge for target tracking is accounting for target scale change and real-time, we combine Mean-Shift and PCA-SIFT algorithm together to solve the problem. We introduce similarity comparison method to determine how the target scale changes, and taking different strategies according to different situation. For target scale getting larger will cause location error, we employ backward tracking to reduce the error. Mean-Shift algorithm has poor performance when tracking scale-changing target due to the fixed bandwidth of its kernel function. In order to overcome this problem, we introduce PCA-SIFT matching. Through key point matching between target and template, that adjusting the scale of tracking window adaptively can be achieved. Because this algorithm is sensitive to wrong match, we introduce RANSAC to reduce mismatch as far as possible. Furthermore target relocating will trigger when number of match is too small. In addition we take comprehensive consideration about target deformation and error accumulation to put forward a new template update method. Experiments on five image sequences and comparison with 6 kinds of other algorithm demonstrate favorable performance of the proposed tracking algorithm.

Keywords: target tracking, PCA-SIFT, mean-shift, scale-adaptive

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Authors: Henni Sid Ahmed, Belbachir Mohamed Faouzi, Jean Caelen

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Behavior recognition has been studied for realizing drivers assisting system and automated navigation and is an important studied field in the intelligent Building. In this paper, a recognition method of behavior recognition separated from a real image was studied. Images were divided into several categories according to the actual weather, distance and angle of view etc. SIFT was firstly used to detect key points and describe them because the SIFT (Scale Invariant Feature Transform) features were invariant to image scale and rotation and were robust to changes in the viewpoint and illumination. My goal is to develop a robust and reliable system which is composed of two fixed cameras in every room of intelligent building which are connected to a computer for acquisition of video sequences, with a program using these video sequences as inputs, we use SIFT represented different images of video sequences, and SVM (support vector machine) Lights as a programming tool for classification of images in order to classify people’s behaviors in the intelligent building in order to give maximum comfort with optimized energy consumption.

Keywords: video analysis, people behavior, intelligent building, classification

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Authors: Belaynesh Chekol, Numan Çelebi

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The main purpose of this work is to recognize individual characters extracted from natural scene images using scale invariant feature transform (SIFT) features as an input to K-nearest neighbor (KNN); a classification learner algorithm. For this task, 1,068 and 78 images of English alphabet characters taken from Chars74k data set is used to train and test the classifier respectively. For each character image, We have generated describing features by using SIFT algorithm. This set of features is fed to the learner so that it can recognize and label new images of English characters. Two types of KNN (fine KNN and weighted KNN) were trained and the resulted classification accuracy is 56.9% and 56.5% respectively. The training time taken was the same for both fine and weighted KNN.

Keywords: character recognition, KNN, natural scene image, SIFT

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Authors: Jae-Neung Lee, Keun-Chang Kwak

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This paper describes an analysis of Yacht Simulator international trends and also explains about Yacht. Examples of yacht Simulator using Yacht Simulator include image processing for totaling the total number of vehicles, edge/target detection, detection and evasion algorithm, image processing using SIFT (scale invariant features transform) matching, and application of median filter and thresholding.

Keywords: yacht simulator, simulator, trends analysis, SIFT

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Authors: Lan Du, Yan Wang, Hui Dai

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Synthetic aperture radar (SAR) image change detection has recently become a challenging problem owing to the existence of speckle noises. In this paper, an unsupervised distribution-free change detection for SAR image based on scale-invariant feature transform (SIFT) keypoints and segmentation is proposed. Firstly, the noise-robust SIFT keypoints which reveal the blob-like structures in an image are extracted in the log-ratio image to reduce the detection range. Then, different from the traditional change detection which directly obtains the change-detection map from the difference image, segmentation is made around the extracted keypoints in the two original multitemporal SAR images to obtain accurate changed region. At last, the change-detection map is generated by comparing the two segmentations. Experimental results on the real SAR image dataset demonstrate the effectiveness of the proposed method.

Keywords: change detection, Synthetic Aperture Radar (SAR), Scale-Invariant Feature Transformation (SIFT), segmentation

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Authors: Masoud Fathi Kazerouni, Jens Schlemper, Klaus-Dieter Kuhnert

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Green planet is one of the Earth’s names which is known as a terrestrial planet and also can be named the fifth largest planet of the solar system as another scientific interpretation. Plants do not have a constant and steady distribution all around the world, and even plant species’ variations are not the same in one specific region. Presence of plants is not only limited to one field like botany; they exist in different fields such as literature and mythology and they hold useful and inestimable historical records. No one can imagine the world without oxygen which is produced mostly by plants. Their influences become more manifest since no other live species can exist on earth without plants as they form the basic food staples too. Regulation of water cycle and oxygen production are the other roles of plants. The roles affect environment and climate. Plants are the main components of agricultural activities. Many countries benefit from these activities. Therefore, plants have impacts on political and economic situations and future of countries. Due to importance of plants and their roles, study of plants is essential in various fields. Consideration of their different applications leads to focus on details of them too. Automatic recognition of plants is a novel field to contribute other researches and future of studies. Moreover, plants can survive their life in different places and regions by means of adaptations. Therefore, adaptations are their special factors to help them in hard life situations. Weather condition is one of the parameters which affect plants life and their existence in one area. Recognition of plants in different weather conditions is a new window of research in the field. Only natural images are usable to consider weather conditions as new factors. Thus, it will be a generalized and useful system. In order to have a general system, distance from the camera to plants is considered as another factor. The other considered factor is change of light intensity in environment as it changes during the day. Adding these factors leads to a huge challenge to invent an accurate and secure system. Development of an efficient plant recognition system is essential and effective. One important component of plant is leaf which can be used to implement automatic systems for plant recognition without any human interface and interaction. Due to the nature of used images, characteristic investigation of plants is done. Leaves of plants are the first characteristics to select as trusty parts. Four different plant species are specified for the goal to classify them with an accurate system. The current paper is devoted to principal directions of the proposed methods and implemented system, image dataset, and results. The procedure of algorithm and classification is explained in details. First steps, feature detection and description of visual information, are outperformed by using Scale invariant feature transform (SIFT), HARRIS-SIFT, and FAST-SIFT methods. The accuracy of the implemented methods is computed. In addition to comparison, robustness and efficiency of results in different conditions are investigated and explained.

Keywords: SIFT combination, feature extraction, feature detection, natural images, natural plant recognition, HARRIS-SIFT, FAST-SIFT

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Authors: Jong-Hwa Kim, Mu-Wook Pyeon, Yang-dam Eo, Ill-Woong Jang

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Construction of geo-spatial information recently tends to develop as multi-dimensional geo-spatial information. People constructing spatial information is also expanding its area to the general public from some experts. As well as, studies are in progress using a variety of devices, with the aim of near real-time update. In this paper, getting the stereo images using GoPro device used widely also to the general public as well as experts. And correcting the distortion of the images, then by using SIFT, DLT, is acquired the point clouds. It presented a possibility that on the basis of this experiment, using a video device that is readily available in real life, to create a real-time digital map.

Keywords: GoPro, SIFT, DLT, point clouds

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Authors: Rabab M. Ramadan, Elaraby A. Elgallad

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With extensive application, the performance of unimodal biometrics systems has to face a diversity of problems such as signal and background noise, distortion, and environment differences. Therefore, multimodal biometric systems are proposed to solve the above stated problems. This paper introduces a bimodal biometric recognition system based on the extracted features of the human palm print and iris. Palm print biometric is fairly a new evolving technology that is used to identify people by their palm features. The iris is a strong competitor together with face and fingerprints for presence in multimodal recognition systems. In this research, we introduced an algorithm to the combination of the palm and iris-extracted features using a texture-based descriptor, the Scale Invariant Feature Transform (SIFT). Since the feature sets are non-homogeneous as features of different biometric modalities are used, these features will be concatenated to form a single feature vector. Particle swarm optimization (PSO) is used as a feature selection technique to reduce the dimensionality of the feature. The proposed algorithm will be applied to the Institute of Technology of Delhi (IITD) database and its performance will be compared with various iris recognition algorithms found in the literature.

Keywords: iris recognition, particle swarm optimization, feature extraction, feature selection, palm print, the Scale Invariant Feature Transform (SIFT)

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Authors: Gargi Phadke, Mugdha Joshi, Shamal Salunkhe

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Facial sketches are used as a crucial clue by criminal investigators for identification of suspects when the description of eyewitness or victims are only available as evidence. A forensic artist develops a sketch as per the verbal description is given by an eyewitness that shows the facial look of the culprit. In this paper, the fusion of Scale Invariant Feature Transform (SIFT) and multiscale local binary patterns (MLBP) are proposed as a feature to recognize a forensic face sketch images from a gallery of mugshot photos. This work focuses on comparative analysis of proposed scheme with existing algorithms in different challenges like illumination change and rotation condition. Experimental results show that proposed scheme can lead to better performance for the defined problem.

Keywords: SIFT feature, MLBP, PCA, face sketch

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Authors: O. Abusaeeda, J. P. O. Evans, D. Downes

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We demonstrate the synthesis of intermediary views within a sequence of X-ray images that exhibit depth from motion or kinetic depth effect in a visual display. Each synthetic image replaces the requirement for a linear X-ray detector array during the image acquisition process. Scale invariant feature transform, SIFT, in combination with epipolar morphing is employed to produce synthetic imagery. Comparison between synthetic and ground truth images is reported to quantify the performance of the approach. Our work is a key aspect in the development of a 3D imaging modality for the screening of luggage at airport checkpoints. This programme of research is in collaboration with the UK Home Office and the US Dept. of Homeland Security.

Keywords: X-ray, kinetic depth, KDE, view synthesis

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Authors: Jia-Hong Lee, Mei-Yi Wu, Hsien-Tsung Kuo

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Texture is an important characteristic in real and synthetic scenes. Texture analysis plays a critical role in inspecting surfaces and provides important techniques in a variety of applications. Although several descriptors have been presented to extract texture features, the development of object recognition is still a difficult task due to the complex aspects of texture. Recently, many robust and scaling-invariant image features such as SIFT, SURF and ORB have been successfully used in image retrieval and object recognition. In this paper, we have tried to compare the performance for texture classification using these feature descriptors with k-means clustering. Different classifiers including K-NN, Naive Bayes, Back Propagation Neural Network , Decision Tree and Kstar were applied in three texture image sets － UIUCTex, KTH-TIPS and Brodatz, respectively. Experimental results reveal SIFTS as the best average accuracy rate holder in UIUCTex, KTH-TIPS and SURF is advantaged in Brodatz texture set. BP neuro network works best in the test set classification among all used classifiers.

Keywords: texture classification, texture descriptor, SIFT, SURF, ORB

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Authors: Fatma Abdedayem

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We propose an extension to the famous method called Bag of words (BOW) which proved a successful role in the field of image categorization. Practically, this method based on representing image with visual words. In this work, firstly, we extract features from images using Spatial Pyramid Representation (SPR) and two dissimilar color descriptors which are opponent-SIFT and transformed-color-SIFT. Secondly, we fuse color local features by joining the two histograms coming from these descriptors. Thirdly, after collecting of all features, we generate multi-dictionaries coming from n random feature subsets that obtained by dividing all features into n random groups. Then, by using these dictionaries separately each image can be represented by n histograms which are lately concatenated horizontally and form the final histogram, that allows to combine Multiple Dictionaries (MDBoW). In the final step, in order to classify image we have applied Support Vector Machine (SVM) on the generated histograms. Experimentally, we have used two dissimilar image datasets in order to test our proposition: Caltech 256 and PASCAL VOC 2007.

Keywords: bag of words (BOW), color descriptors, multi-dictionaries, MDBoW

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Authors: António J. R. Neves, Rui Garcia, Paulo Dias, Alina Trifan

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With the aging of the world population and the continuous growth in technology, service robots are more and more explored nowadays as alternatives to healthcare givers or personal assistants for the elderly or disabled people. Any service robot should be capable of interacting with the human companion, receive commands, navigate through the environment, either known or unknown, and recognize objects. This paper proposes an approach for object recognition based on the use of depth information and color images for a service robot. We present a study on two of the most used methods for object detection, where 3D data is used to detect the position of objects to classify that are found on horizontal surfaces. Since most of the objects of interest accessible for service robots are on these surfaces, the proposed 3D segmentation reduces the processing time and simplifies the scene for object recognition. The first approach for object recognition is based on color histograms, while the second is based on the use of the SIFT and SURF feature descriptors. We present comparative experimental results obtained with a real service robot.

Keywords: object detection, feature, descriptors, SIFT, SURF, depth images, service robots

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Authors: Phanida Phukoetphim, Asaad Y. Shamseldin

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In this study, the novel Stochastic Gradient Boosting (SGB) combination method is addressed for producing daily river flows from four different rain-runoff models of Ohinemuri catchment, New Zealand. The selected rainfall-runoff models are two empirical black-box models: linear perturbation model and linear varying gain factor model, two conceptual models: soil moisture accounting and routing model and Nedbør-Afrstrømnings model. In this study, the simple average combination method and the weighted average combination method were used as a benchmark for comparing the results of the novel SGB combination method. The models and combination results are evaluated using statistical and graphical criteria. Overall results of this study show that the use of combination technique can certainly improve the simulated river flows of four selected models for Ohinemuri catchment, New Zealand. The results also indicate that the novel SGB combination method is capable of accurate prediction when used in a combination method of the simulated river flows in New Zealand.

Keywords: multi-model combination, rainfall-runoff modeling, stochastic gradient boosting, bioinformatics

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Authors: Zuhal Kurt, Meral Kurt, Bilge T. Bal, Kemal Ozkan

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Shade matching and reproduction is the most important element of success in prosthetic dentistry. Until recently, shade matching procedure was implemented by dentists visual perception with the help of shade guides. Since many factors influence visual perception; tooth shade matching using visual devices (shade guides) is highly subjective and inconsistent. Subjective nature of this process has lead to the development of instrumental devices. Nowadays, colorimeters, spectrophotometers, spectroradiometers and digital image analysing systems are used for instrumental shade selection. Instrumental devices have advantages that readings are quantifiable, can obtain more rapidly and simply, objectively and precisely. However, these devices have noticeable drawbacks. For example, translucent structure and irregular surfaces of teeth lead to defects on measurement with these devices. Also between the results acquired by devices with different measurement principles may make inconsistencies. So, its obligatory to search for new methods for dental shade matching process. A computer-aided system device; digital camera has developed rapidly upon today. Currently, advances in image processing and computing have resulted in the extensive use of digital cameras for color imaging. This procedure has a much cheaper process than the use of traditional contact-type color measurement devices. Digital cameras can be taken by the place of contact-type instruments for shade selection and overcome their disadvantages. Images taken from teeth show morphology and color texture of teeth. In last decades, a new method was recommended to compare the color of shade tabs taken by a digital camera using color features. This method showed that visual and computer-aided shade matching systems should be used as concatenated. Recently using methods of feature extraction techniques are based on shape description and not used color information. However, color is mostly experienced as an essential property in depicting and extracting features from objects in the world around us. When local feature descriptors with color information are extended by concatenating color descriptor with the shape descriptor, that descriptor will be effective on visual object recognition and classification task. Therefore, the color descriptor is to be used in combination with a shape descriptor it does not need to contain any spatial information, which leads us to use local histograms. This local color histogram method is remain reliable under variation of photometric changes, geometrical changes and variation of image quality. So, coloring local feature extraction methods are used to extract features, and also the Scale Invariant Feature Transform (SIFT) descriptor used to for shape description in the proposed method. After the combination of these descriptors, the state-of-art descriptor named by Color-SIFT will be used in this study. Finally, the image feature vectors obtained from quantization algorithm are fed to classifiers such as Nearest Neighbor (KNN), Naive Bayes or Support Vector Machines (SVM) to determine label(s) of the visual object category or matching. In this study, SVM are used as classifiers for color determination and shade matching. Finally, experimental results of this method will be compared with other recent studies. It is concluded from the study that the proposed method is remarkable development on computer aided tooth shade determination system.

Keywords: classifiers, color determination, computer-aided system, tooth shade matching, feature extraction

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Authors: Jean Marie Tshimula, Atsushi Togashi

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We develop a data-science approach for providing an interactive visualization and predictive models to find insights into the projects' historical data in order for stakeholders understand some unseen opportunities in the African market that might escape them behind the online project portfolio of the African Development Bank. This machine learning-based web application identifies the market trend of the fastest growing economies across the continent as well skyrocketing sectors which have a significant impact on the future of business in Africa. Owing to this, the approach is tailored to predict where the investment needs are the most required. Moreover, we create a corpus that includes the descriptions of over more than 1,200 projects that approximately cover 14 sectors designed for some of 53 African countries. Then, we sift out this large amount of semi-structured data for extracting tiny details susceptible to contain some directions to follow. In the light of the foregoing, we have applied the combination of Latent Dirichlet Allocation and Random Forests at the level of the analysis module of our methodology to highlight the most relevant topics that investors may focus on for investing in Africa.

Keywords: machine learning, topic modeling, natural language processing, big data

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Authors: Nabila N. El-Maraghy, Amany Essa, Yousra Abdel–Mottaleb, Nada Ismail

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The use of combination of chemotherapy and natural products to influence the cell death with low doses of chemotherapeutic agents and few side effects has recently emerged as a new method of cancer therapy. Aim: Evaluation the modulatory effect of Thymoquinone on HepG2 cells treated with Sorafenib. Methods: Hepatocellular Carcinoma HepG2 cell line was treated with Sorafenib and TQ individually and in combination. The effect of these treatments on cell viability (MTT assay), apoptosis (Expression of Caspase-3) and oxidative markers (GSH content and extent of lipid peroxidation) was determined. Results: When compared the effect of both agents alone and the combination of the IC50 of Sorafenib and the IC50 TQ, the combination resulted in reduction of cell inhibition and apoptosis and antagonize their actions on GSH content and extent of lipid peroxidation which are increased. This study showed potent anti-tumor activity of both TQ and Sorafenib separately on HepG2 but upon combination surprisingly they interacted and give antagonistic effect. Conclusion: Co-treatment resulted in antagonistic interaction between Sorafenib and Thymoquinone.

Keywords: antagonism, hepatocellular carcinoma, sorafenib, thymoquinone

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Authors: Rayhana Begum, HongBin Wang, Nur Alam Siddiquee, Md.Yasin Ahmed

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Biotin treatment has significant effects on blood glucose, and pharmacological doses of biotin improve hyperglycemia. The present study was aimed to investigate the efficacy and safety of biotin in combination with canagliflozin in improving glycemic control on High Fat Diet-induced diabetes in Rats. Thirty male rats were divided into five groups (six rats /group): control, high fat diet (HFD), canagliflozin (CAG), biotin (BIO), and CAG + BIO. The treatments with CAG and /or BIO significantly reduced the body weight gain, blood glucose and HbA1c levels, whereas CAG in combination with BIO revealed greater glycemic improvement than CAG monotherapy. The treatment with CAG and /or BIO causes significant change in lipid profile and CK level while the treatment with CAG in combination with BIO showed better results as compared with CAG monotherapy. Furthermore, combination of biotin with CAG improved the pancreatic and cardiac damage when compared with other treated groups.

Keywords: canagliflozin, biotin, HbA1c, lipid profile

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Authors: Agung A. Ginanjar, Lilitasari, Indra Prasetya, Rizal R. Hanif, Yusrina Rismandini, Atina Hussaana, Nurita P. Sari

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Hyperlipidemia is an increase of lipids and cholesterol in the blood that causes the formation of atherosklerosis. The recent pharmacological therapy nowadays is statin. Many Indonesian people use of medicinal plants. There are several medical plants that people always use to cure hyperlipidemia such as bulbs onion sabrang, areca nuts, and seed of fenugreek. Most people often use a combination therapy of conventional medicine and herbs to achieve the desired therapeutic effect of combination therapy. The use of combination therapy might cause the interaction of pharmacodynamic from those medicines so that it influences the pharmacological effect of one of medicine. The aim of this study is to know the interaction of simvastatin and a cholesterol-lowering herb seen in rats pharmacodynamic simvastatin phase. This research used post-test only controlled group design. Analysis of statistical data normality and homogenity were tested by Kolmogorov Smirnov. The ANOVA test is used when the data is obtained homogeneous but if it is found that the data are not homogeneous then kruskal-wallis test is used. Normal (63.196 mg/dl), negative (70.604 mg/dl), positive (62.512 mg/dl), areca nuts (56.564 mg/dl), fenugreek seed (47.538 ,g/dl), onion sabrang (62.312 mg/dl). The results prove that the combination of herbs and simvastatin did not have a significant difference (P>0,05). The conclusion of this study is that the combination of simvastatin and a cholesterol-lowering herb can cause some pharmacodynamic interactions such as a synergistic effect, antagonist, and a powerful additive, so that combination therapy is not more effective than single simvastatin therapy. The use of the combination therapy is not given in the same time. It would be better if there are some period of time when the combination therapy is applied.

Keywords: onion bulb sabrang, areca nuts, seed of fenugreek, interaction medicine, hyperlipidemia

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Authors: Hireni Mankodi

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The textile preforms for thermoplastic composite play a key role in providing the mechanical properties and gives the idea about preparing combination of yarn from Glass, Basalt, Carbon as reinforcement and PP, PET, Nylon as thermoplastic matrix at yarn stage for preforms to improve the quality and performance of laminates. The main objectives of this work are to develop the hybrid yarn using different yarn manufacturing process and prepare different performs using hybrid yarns. It has been observed that the glass/pp combination give homogeneous distribution in yarn. The proportion varied to optimize the glass/pp composition. The different preform has been prepared with combination of hybrid yarn, PP, glass combination. Further studies will investigate the effect of glass content in fabric, effect of weave, warps and filling density, number of layer plays significant role in deciding mechanical properties of thermoplastic laminates.

Keywords: thermoplastic, preform, laminates, hybrid yarn, glass

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Authors: Shajida M., Sakthiyadharshini N. P., Kamalesh S., Aswitha B.

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Natural Language Processing (NLP) continues to play a strategic part in complaint discovery and medicine discovery during the current epidemic. This abstract provides an overview of performance analysis with a combination of visualization and classification techniques of NLP for a medical chatbot. Sentiment analysis is an important aspect of NLP that is used to determine the emotional tone behind a piece of text. This technique has been applied to various domains, including medical chatbots. In this, we have compared the combination of the decision tree with heatmap and Naïve Bayes with Word Cloud. The performance of the chatbot was evaluated using accuracy, and the results indicate that the combination of visualization and classification techniques significantly improves the chatbot's performance.

Keywords: sentimental analysis, NLP, medical chatbot, decision tree, heatmap, naïve bayes, word cloud

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Authors: Xing Lv, Hui-Qin Xu

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The present study aimed to reveal the mechanism in aggravating STZ induced diabetic nephropathy (DN) by AGEs (advanced glycation end products). At the eighth day, 20 diabetic mice were randomly divided into STZ group and combination (combine AGEs with STZ) group. Simultaneously, AGEs group and normal group were set. Only mice in AGEs group, combination group were fed with high-AGEs diets. Mice diabetic conventional indicators, biochemical analysis were measured. Among the indictors, food consumptions, water intake, urine output, blood glucose, urine protein, urine creatinine, serum urea nitrogen were increased significantly in STZ, combination groups. The AGEs levels in combination group increased significantly when compared with STZ group. Weights and insulin levels in the STZ, combination groups were decreased significantly when compared with normal group, and the difference was significantly between AGEs group and STZ group. As a conclusion, AGEs play an important role in the DN development, inducing kidney damages.

Keywords: AGEs, diabetic nephropathy, serum urea nitrogen, urine protein

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Authors: Bunjirtluk Jintaridth

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Utilization of silicon for sustainable rice cultivation in acid sulfate soils was studied for 2 years. The study was conducted on Rungsit soils in Amphoe Tanyaburi, Pathumtani Province. The objectives of this study were to assess the effect of high quality organic fertilizer in combination with silicon and chemical fertilizer on rice yield, chemical soil properties after using soil amendments, and also to assess the economic return. A Randomized Complete Block Design (RCBD) with 10 treatments and 3 replications were employed. The treatments were as follows: 1) control 2) chemical fertilizer (recommended by Land Development Department, LDD 3) silicon 312 kg/ha 4) high quality organic fertilizer at 1875 kg/ha (the recommendation rate by LDD) 5) silicon 156 kg/ha in combination with high quality organic fertilizer 1875 kg/ha 6) silicon at the 312 kg/ha in combination with high quality organic fertilizer 1875 kg/ha 7) silicon 156 kg/ha in combination with chemical fertilizer 8) silicon at the 312 kg/ha in combination with chemical fertilizer 9) silicon 156 kg/ha in combination with ½ chemical fertilizer rate, and 10) silicon 312 kg/ha in combination with ½ chemical fertilizer rate. The results of 2 years indicated the treatment tended to increase soil pH (from 5.1 to 4.7-5.5), percentage of organic matter (from 2.43 to 2.54 - 2.94%); avail. P (from 7.5 to 7-21 mg kg-1 P; ext. K (from 616 to 451-572 mg kg-1 K), ext Ca (from 1962 to 2042.3-4339.7 mg kg-1 Ca); ext Mg (from 1586 to 808.7-900 mg kg-1 Mg); but decrease the ext. Al (from 2.56 to 0.89-2.54 cmol kg-1 Al. Two years average of rice yield, the highest yield was obtained from silicon 156 kg/ha application in combination with high quality organic fertilizer 300 kg/rai (3770 kg/ha), or using silicon at the 312 kg/ha combination with high quality organic fertilizer 300 kg/rai. (3,750 kg/ha). It was noted that chemical fertilizer application with 156 and 312 kg/ha silicon gave only 3,260 และ 3,133 kg/ha, respectively. On the other hand, half rate of chemical fertilizer with 156 and 312 kg/ha with silicon gave the yield of 2,934 และ 3,218 kg/ha, respectively. While high quality organic fertilizer only can produce 3,318 kg/ha as compare to rice yield of 2,812 kg/ha from control. It was noted that the highest economic return was obtained from chemical fertilizer treated plots (886 dollars/ha). Silicon application at the rate of 156 kg/ha in combination with high quality organic fertilizer 1875 kg/ha gave the economic return of 846 dollars/ha, while 312 kg/ha of silicon with chemical fertilizer gave the lowest economic return (697 dollars/ha).

Keywords: rice, high quality organic fertilizer, acid sulfate soil, silicon

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Authors: Kedar S. Prabhavalkar, Nimmy Baby Poovanpallil

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Lamotrigine is approved for maintenance treatment of bipolar I disorder. However, its role in the treatment of acute bipolar depression is not well clear. Its efficacy in the treatment of major depressive disorders including refractory unipolar depression suggested the use of lamotrigine as an augmentation drug for acute bipolar depression. The present study aims to evaluate and perform a comparative analysis of the therapeutic effects of lamotrigine, an epileptic mood stabilizer, when used alone and in combination with duloxetine in treating acute bipolar depression at different doses of lamotrigine. Male swiss albino mice were used. For evaluation of efficacy of combination, immobility period was analyzed 30 min after the treatment from forced swim and tail suspension tests. Further amount of sucrose consumed in sucrose preference test was estimated. The combination of duloxetine and lamotrigine showed potentiation of antidepressant activity in acute models. Decrease in immobility time and increase in the amount of sucrose consumption in stressed mice were higher in combined group compared to lamotrigine monotherapy group. Brain monoamine levels were also attenuated more with combination compared to monotherapy. Results of the present study suggest potential role of lamotrigine and duloxetine combination in the treatment of acute bipolar depression.

Keywords: lamotrigine, duloxetine, acute bipolar depression, augmentation

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Authors: Gesti Prastiti, Maylina Adani, Yuyun darma A. N., M. Khilmi F., Yunita Wahyu Pratiwi

Abstract:

Combination therapy may allow interaction on two drugs or more that can give adverse effects on patients. Simvastatin is a drug of antihyperlipidemia it can interact with drugs which work on cytochrome P450 CYP3A4 because it can interfere the performance of simvastatin. Flavonoid found in plants can inhibit the cytochrome P450 CYP3A4 if taken with simvastatin and can increase simvastatin levels in the body and increases the potential side effects of simvastatin such as myopati and rhabdomyolysis. Green tea leaves and mint are herbal medicine which has the effect of antihiperlipidemia. This study aims to determine the potential interaction of simvastatin with herbal drinks (green tea leaves and mint). This research method are experimental post-test only control design. Test subjects were divided into 5 groups: normal group, negative control group, simvastatin group, a combination of green tea group and the combination group mint leaves. The study was conducted over 32 days and total cholesterol levels were analyzed by enzymatic colorimetric test method. Results of this study is the obtainment of average value of total cholesterol in each group, the normal group (65.92 mg/dL), the negative control group the average total cholesterol test in the normal group was (69.86 mg/dL), simvastatin group (58.96 mg/dL), the combination of green tea group (58.96 mg/dL), and the combination of mint leaves (63.68 mg/dL). The conclusion is between simvastatin combination therapy with herbal drinks have the potential for pharmacodynamic interactions with a synergistic effect, antagonist, and a powerful additive, so the combination therapy are no more effective than a single administration of simvastatin therapy.

Keywords: hyperlipidemia, simvastatin, herbal drinks, green tea leaves, mint leaves, drug interactions

Procedia PDF Downloads 395

Authors: Lubomir Machuca, Vit Fara

Abstract:

Ammonium nitrate (AN) is produced by the reaction of ammonia and nitric acid, and a waste condensate is obtained. The condensate contains pure AN in concentration up to 10g/L. The salt content in the condensate is too high to discharge immediately into the river thus it must be treated. This study is concerned with the treatment of condensates from an industrial AN production by combination of electrodialysis (ED) and electrodeionization (EDI). The condensate concentration was in range 1.9–2.5g/L of AN. A pilot ED module with 25 membrane pairs following by a laboratory EDI module with 10 membrane pairs operated continuously during 800 hours. Results confirmed that the combination of ED and EDI is suitable for the condensate treatment.

Keywords: desalination, electrodialysis, electrodeionization, fertilizer industry

Procedia PDF Downloads 240

Authors: Babatunde Olumide Olawale, Oyebode Olumide Oyediran

Abstract:

The security safe is a place or building where classified document and precious items are kept. To prevent unauthorised persons from gaining access to this safe a lot of technologies had been used. But frequent reports of an unauthorised person gaining access into security safes with the aim of removing document and items from the safes are pointers to the fact that there is still security gap in the recent technologies used as access control for the security safe. In this paper we try to solve this problem by developing a multimodal biometric system for physical access control into a security safe using face and voice recognition. The safe is accessed by the combination of face and speech pattern recognition and also in that sequential order. User authentication is achieved through the use of camera/sensor unit and a microphone unit both attached to the door of the safe. The user face was captured by the camera/sensor while the speech was captured by the use of the microphone unit. The Scale Invariance Feature Transform (SIFT) algorithm was used to train images to form templates for the face recognition system while the Mel-Frequency Cepitral Coefficients (MFCC) algorithm was used to train the speech recognition system to recognise authorise user’s speech. Both algorithms were hosted in two separate web based servers and for automatic analysis of our work; our developed system was simulated in a MATLAB environment. The results obtained shows that the developed system was able to give access to authorise users while declining unauthorised person access to the security safe.

Keywords: access control, multimodal biometrics, pattern recognition, security safe

Procedia PDF Downloads 334