Search results for: fruit recognition

Authors: Daleesha M. Viswanathan, Sumam Mary Idicula

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Dynamic hand gestures are an intrinsic component in sign language communication. Extracting spatial temporal features of the hand gesture trajectory plays an important role in a dynamic gesture recognition system. Finding a discrete feature descriptor for the motion trajectory based on the orientation feature is the main concern of this paper. Kalman filter algorithm and Hidden Markov Models (HMM) models are incorporated with this recognition system for hand trajectory tracking and for spatial temporal classification, respectively.

Keywords: orientation features, discrete feature vector, HMM., Indian sign language

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Authors: Akanit Pisalwadcharin, Komate Satayawut, Virachnee Lohachoompol

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Green tea, mangosteen and pomegranate contain high levels of bioactive compounds which have antioxidant effects and great potential in food applications. The aim of this study was to produce and determine catechin contents, total phenolic contents, antioxidant activity and phenolic compounds of two instant fruit green tea powders which were green tea fortified with mangosteen juice and green tea fortified with pomegranate juice. Seventy percent of hot water extract of green tea was mixed with 30% of mangosteen juice or pomegranate juice, and then spray-dried using a spray dryer. The results showed that the drying conditions optimized for the highest total phenolic contents, catechin contents and antioxidant activity of both powders were the inlet air temperature of 170°C, outlet air temperatures of 90°C and maltodextrin concentration of 30%. The instant green tea with mangosteen powder had total phenolic contents, catechin contents and antioxidant activity of 19.18 (mg gallic acid/kg), 85.44 (mg/kg) and 4,334 (µmoles TE/100 g), respectively. The instant green tea with pomegranate powder had total phenolic contents, catechin contents and antioxidant activity of 32.72 (mg gallic acid/kg), 156.36 (mg/kg) and 6,283 (µmoles TE/100 g), respectively. The phenolic compounds in instant green tea with mangosteen powder comprised of tannic acid (2,156.87 mg/kg), epigallocatechin-3-gallate (898.23 mg/kg) and rutin (13.74 mg/kg). Also, the phenolic compounds in instant green tea with pomegranate powder comprised of tannic acid (2,275.82 mg/kg), epigallocatechin-3-gallate (981.23 mg/kg), rutin (14.97 mg/kg) and i-quercetin (5.86 mg/kg).

Keywords: green tea, mangosteen, pomegranate, antioxidant activity

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Authors: A. N. Paithane, D. S. Bormane, S. D. Shirbahadurkar

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It has been seen that emotion recognition is an important research topic in the field of Human and computer interface. A novel technique for Feature Extraction (FE) has been presented here, further a new method has been used for human emotion recognition which is based on HHT method. This method is feasible for analyzing the nonlinear and non-stationary signals. Each signal has been decomposed into the IMF using the EMD. These functions are used to extract the features using fission and fusion process. The decomposition technique which we adopt is a new technique for adaptively decomposing signals. In this perspective, we have reported here potential usefulness of EMD based techniques.We evaluated the algorithm on Augsburg University Database; the manually annotated database.

Keywords: intrinsic mode function (IMF), Hilbert-Huang transform (HHT), empirical mode decomposition (EMD), emotion detection, electrocardiogram (ECG)

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Authors: Alvine Sandrine Ndinchout, D. P. Chattopadhyay, Moundipa Fewou Paul, Nyegue Maximilienne Ascension, Varinder Kaur, Sukhraj Kaur, B. H. Patel

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Dacryodes macrophylla is a species of the Burseraceae family that is widespread in Cameroon, Equatorial Guinea, and Gabon. The only part of D. macrophylla known to use is the pulp contained in the fruit. This very juicy pulp is consumed directly and used in making juices. During consumption, these fruit leaves a dark blackish colour on fingers and garment. This observation means that D. macrophylla fruits must be a good source of natural dye with probably good fastness properties on textile materials. But D. macrophylla has not yet been investigated with reference as a potential source of natural dye to our best knowledge. Natural dye has been extracted using water as solvent by soxhlet extraction method. The extracted color was characterized by spectroscopic studies like UV/Visible and further tested for antimicrobial activity against gram-negative (Vibrio cholerae, Escherichia coli, Salmonella enterica serotype Typhi, Shigella flexneri) and gram-positive (Listeria monocytogenes, Staphylococcus aureus) bacteria. It was observed that the water extract of D. macrophylla showed antimicrobial activities against S. enterica. The results of fastness properties of the dyed fabrics were fair to good. Taken together, these results indicate that D. macrophylla can be used as natural dye not only in textile but also in other domains like food coloring.

Keywords: antimicrobial activity, natural dye, silk, wash fastness, wool

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Authors: Elina Brahma Hazarika, Jeuti Basumatary, Deepanka Saikia, Jaydeep Das, Micky Moni D'mary, Fungkha Basumatary

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This study includes development of two varieties of biscuits incorporated with: the seeds of Jack fruit (Artocarpus heterophyllus), which post-consumption of it’s pulp, is discarded as a waste, and Cassava (Manihot esculenta) tubers.The jack fruit seeds and cassava were first ground into flour and its proximate and physiochemical properties were studied. The biscuits that were developed incorporating them had 50% wheat flour and 50% jackfruit seed flour and 50% cassava flours as the major composition, apart from the other general ingredients use in making biscuits. Various trials of compositions were made for baking to get the overall desirable acceptability in biscuits through sensory evaluation. Finally, the best composition of ingredients was selected to make the biscuits, and hence studies were done accordingly to compare it with the properties of their respective raw flours. The results showed that the proximate composition of the biscuits fared better than that of their respective flours: There was a decrease in the Moisture content of both Jackfruit Seed Biscuits and Cassava Biscuits to 4.5% and 6.7% than that of their respective raw flours (8 and 12%). Post-baking, there is increase in the percentages of ash, protein, and fibre contents in both Jackfruit Seed Biscuits and Cassava Biscuits; the values being 3% and 3.8%, 13.2% and 3.3%, and 3.2 and 4.1% respectively. Also the total carbohydrate content in Jackfruit Seed Biscuits and Cassava Biscuits were 66.7% and 71.7% respectively. Their sensory evaluation and texture study also yielded a clear review that they have an overall good acceptability.

Keywords: baking, proximate, sensory, texture

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Authors: Aishwarya Ravindra Fursule, Shruti Kshirsagar

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In the intersection of artificial intelligence and human-centered computing, this paper delves into speech emotion recognition (SER). It presents a comparative analysis of machine learning models such as K-Nearest Neighbors (KNN),logistic regression, support vector machines (SVM), decision trees, ensemble classifiers, and random forests, applied to SER. The research employs four datasets: Crema D, SAVEE, TESS, and RAVDESS. It focuses on extracting salient audio signal features like Zero Crossing Rate (ZCR), Chroma_stft, Mel Frequency Cepstral Coefficients (MFCC), root mean square (RMS) value, and MelSpectogram. These features are used to train and evaluate the models’ ability to recognize eight types of emotions from speech: happy, sad, neutral, angry, calm, disgust, fear, and surprise. Among the models, the Random Forest algorithm demonstrated superior performance, achieving approximately 79% accuracy. This suggests its suitability for SER within the parameters of this study. The research contributes to SER by showcasing the effectiveness of various machine learning algorithms and feature extraction techniques. The findings hold promise for the development of more precise emotion recognition systems in the future. This abstract provides a succinct overview of the paper’s content, methods, and results.

Keywords: comparison, ML classifiers, KNN, decision tree, SVM, random forest, logistic regression, ensemble classifiers

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Authors: Kobus-Cisowska Joanna, Flaczyk Ewa, Gramza-Michalowska Anna, Kmiecik Dominik, Przeor Monika, Marcinkowska Agata

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In recent years, proceed to the attractiveness of typical bakery products. Expanding the education and nutrition knowledge society will develop the production of functional foods, which has a positive impact on human health. Therefore, the aim of the present study was to evaluate the effect of the addition of white mulberry fruit on the content of biologically active compounds in the new designed functional bread premixes designed for selected disease: anemia, diabetes, obesity and cardiovascular disease. For flavonols and phenolic acids content UPLC was conducted, using an NovaPack C18 column and a gradient elution system. It was found that all attempts bread characterized by a high content of biologically active compounds: polyphenols, phenolic acids, and flavonoids. The highest total content of polyphenolic compounds found in the samples of bread for anemia, diabetes and cardiovascular disease both before and after storage. The analyzed sample differed in content of phenolic acids. The highest content of these compounds were found in samples of bread for anemia and diabetes. It was found that the analyzed sample contained phenolic acids that are derivatives of hydroxybenzoic and hydroxycinnamic acid. The new designed bread contained significant amounts of flavonols, of which the dominant was routine.

Keywords: mulberry, antioxidant, polyphenols, phenolic acids, flavonols

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Authors: Parvin Mirmiran, Zahra Bahadoran, Nazanin Moslehi, Fereidoun Azizi

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Background and aim: We aimed to investigate the effects of green fruits and vegetables (green FV) consumption on the 3-year changes of cardiometabolic risk factors. Methods: This longitudinal study was conducted in the framework of Tehran Lipid and Glucose Study, between 2006-2008 and 2009-2011, on 1272 adults. Dietary intake of green FV, including green cabbage, broccoli, lettuce, celery, green beans, green peas, cucumber, leafy vegetables, zucchini, green chili and bell pepper, and kiwi fruit, has been assessed by a validated semi-quantitative food frequency questionnaire at baseline and second examination. Demographics, anthropometrics and biochemical measures were evaluated at baseline and 3 years later. The associations of cardiometabolic risk changes with mean intake of green FV were estimated. Results: The mean age of men and women at baseline was 39.8±12.7 and 37.3±12.1 years, respectively. Mean intake of green FV was 152±77 g/d. More intake from green FV was accompanied to more intake of vitamin A, α and β-carotene, lutein, β-criptoxanthine, potassium, magnesium and fiber. Consumption of green FV was inversely associated with 3-year change of waist circumference (β= -0.07, P=0.01), total cholesterol (β= -0.11, P=0.01) and triglycerides (β= -0.13, P=0.01). Each 25 g/d increase in consumption of green FV decreased the incidence of hyper-triglyceridemia by 12% (OR:0.88, 95%CI:0.71-0.99) in men. In women, no significant association was observed between consumption of green FV with cardiometabolic risk factors. Conclusion: Higher consumption of green FV could have preventive effects against abdominal fat gain and lipid disorders.

Keywords: cardiometabolic risk factors, abdominal obesity, lipid disorders, fruits, vegetables

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Authors: S. Kherchaoui, A. Houacine

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This paper presents a facial expression recognition system. It performs identification and classification of the seven basic expressions; happy, surprise, fear, disgust, sadness, anger, and neutral states. It consists of three main parts. The first one is the detection of a face and the corresponding facial features to extract the most expressive portion of the face, followed by a normalization of the region of interest. Then calculus of curvelet coefficients is performed with dimensionality reduction through principal component analysis. The resulting coefficients are combined with two ratios; mouth ratio and face edge ratio to constitute the whole feature vector. The third step is the classification of the emotional state using the SVM method in the feature space.

Keywords: facial expression identification, curvelet coefficient, support vector machine (SVM), recognition system

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Authors: Aswini M. S.

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Tomato (Solanum lycopersicum L.) is one of the most significant vegetable crops in terms of its economic benefits. Both fresh and processed tomatoes are consumed. Tomatoes have a limited genetic base, which makes breeding extremely challenging. Plant breeding has become much simpler and more effective with genome editing tools of CRISPR and CRISPR-associated 9 protein (CRISPR/Cas9), which address the problems with traditional breeding, chemical/physical mutagenesis, and transgenics. With the use of CRISPR/Cas9, a number of tomato traits have been functionally distinguished and edited. These traits include plant architecture as well as flower characters (leaf, flower, male sterility, and parthenocarpy), fruit ripening, quality and nutrition (lycopene, carotenoid, GABA, TSS, and shelf-life), disease resistance (late blight, TYLCV, and powdery mildew), tolerance to abiotic stress (heat, drought, and salinity) and resistance to herbicides. This study explores the potential of CRISPR/Cas9 genome editing for enhancing yield in tomato plants. The study utilized the CRISPR/Cas9 genome editing technology to functionally edit various traits in tomatoes. The de novo domestication of elite features from wild cousins to cultivated tomatoes and vice versa has been demonstrated by the introgression of CRISPR/Cas9. The CycB (Lycopene beta someri) gene-mediated Cas9 editing increased the lycopene content in tomato. Also, Cas9-mediated editing of the AGL6 (Agamous-like 6) gene resulted in parthenocarpic fruit development under heat-stress conditions. The advent of CRISPR/Cas has rendered it possible to use digital resources for single guide RNA design and multiplexing, cloning (such as Golden Gate cloning, GoldenBraid, etc.), creating robust CRISPR/Cas constructs, and implementing effective transformation protocols like the Agrobacterium and DNA free protoplast method for Cas9-gRNAs ribonucleoproteins (RNPs) complex. Additionally, homologous recombination (HR)-based gene knock-in (HKI) via geminivirus replicon and base/prime editing (Target-AID technology) remains possible. Hence, CRISPR/Cas facilitates fast and efficient breeding in the improvement of tomatoes.

Keywords: CRISPR-Cas, biotic and abiotic stress, flower and fruit traits, genome editing, polygenic trait, tomato and trait introgression

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Authors: Mohammed Arif, Abdessalam Kifouche

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Speech is a very convenient way of communication between people and machines. It conveys information about the identity of the talker. Since speaker recognition technology is increasingly securing our everyday lives, the objective of this paper is to develop two automatic text-independent speaker verification systems (TI SV) using low-level spectral features and machine learning methods. (i) The first system is based on a support vector machine (SVM), which was widely used in voice signal processing with the aim of speaker recognition involving verifying the identity of the speaker based on its voice characteristics, and (ii) the second is based on Gaussian Mixture Model (GMM) and Universal Background Model (UBM) to combine different functions from different resources to implement the SVM based.

Keywords: speaker verification, text-independent, support vector machine, Gaussian mixture model, cepstral analysis

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Authors: Khoi A. Nguyen, Rodney A. Stewart, Hong Zhang

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‘Water-related energy’ is energy use which is directly or indirectly influenced by changes to water use. Informatics applying a range of mathematical, statistical and rule-based approaches can be used to reveal important information on demand from the available data provided at second, minute or hourly intervals. This study aims to combine these two concepts to improve the current water end use disaggregation problem through applying a wide range of most advanced pattern recognition techniques to analyse the concurrent high-resolution water-energy consumption data. The obtained results have shown that recognition accuracies of all end-uses have significantly increased, especially for mechanised categories, including clothes washer, dishwasher and evaporative air cooler where over 95% of events were correctly classified.

Keywords: deep learning network, smart metering, water end use, water-energy data

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Authors: J. K. Adedeji, S. T. Ijatuyi

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The application of neural network using pattern recognition to study the fluid dynamics and predict the groundwater reservoirs properties has been used in this research. The essential of geophysical survey using the manual methods has failed in basement environment, hence the need for an intelligent computing such as predicted from neural network is inevitable. A non-linear neural network with an XOR (exclusive OR) output of 8-bits configuration has been used in this research to predict the nature of groundwater reservoirs and fluid dynamics of a typical basement crystalline rock. The control variables are the apparent resistivity of weathered layer (p₁), fractured layer (p₂), and the depth (h), while the dependent variable is the flow parameter (F=λ). The algorithm that was used in training the neural network is the back-propagation coded in C++ language with 300 epoch runs. The neural network was very intelligent to map out the flow channels and detect how they behave to form viable storage within the strata. The neural network model showed that an important variable g_r (gravitational resistance) can be deduced from the elevation and apparent resistivity p_a. The model results from SPSS showed that the coefficients, a, b and c are statistically significant with reduced standard error at 5%.

Keywords: gravitational resistance, neural network, non-linear, pattern recognition

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Authors: Fariba Ebrahimi, Mehdi Ghorbani, Ali Salajegheh

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Comprehensively water management considers economic, environmental, technical and social and also sustainability of water resources for future generations. Grassland management implies cooperative approach and involves all stakeholders and also introduces issues to managers, decision and policy makers. Solving these issues needs integrated and system approach. According to the recognition of actors or key persons in necessary to apply cooperative management of Water. Therefore, based on stakeholder analysis and social network analysis can be used to demonstrate the most effective actors for environmental decisions. In this research, social powers according are specified to social network approach at Water utilizers’ level of Natural in Jirood catchment of Latian basin. In this paper, utilizers of water resources were recognized using field trips and then, trust and collaboration matrix produced using questionnaires. In the next step, degree centrality index were Examined. Finally, geometric position of each actor was illustrated in the network. The results of the research based on centrality index have a key role in recognition of cooperative management of Water in Jirood and also will help managers and planners of water in the case of recognition of social powers in order to organization and implementation of sustainable management of Water.

Keywords: social network analysis, water co-management, social power, centrality index, local stakeholders network, Jirood catchment

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Authors: Naghmeh Moradpoor Sheykhkanloo

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Structured Query Language Injection (SQLI) attack is a code injection technique in which malicious SQL statements are inserted into a given SQL database by simply using a web browser. Losing data, disclosing confidential information or even changing the value of data are the severe damages that SQLI attack can cause on a given database. SQLI attack has also been rated as the number-one attack among top ten web application threats on Open Web Application Security Project (OWASP). OWASP is an open community dedicated to enabling organisations to consider, develop, obtain, function, and preserve applications that can be trusted. In this paper, we propose an effective pattern recognition neural network model for detection and classification of SQLI attacks. The proposed model is built from three main elements of: a Uniform Resource Locator (URL) generator in order to generate thousands of malicious and benign URLs, a URL classifier in order to: 1) classify each generated URL to either a benign URL or a malicious URL and 2) classify the malicious URLs into different SQLI attack categories, and an NN model in order to: 1) detect either a given URL is a malicious URL or a benign URL and 2) identify the type of SQLI attack for each malicious URL. The model is first trained and then evaluated by employing thousands of benign and malicious URLs. The results of the experiments are presented in order to demonstrate the effectiveness of the proposed approach.

Keywords: neural networks, pattern recognition, SQL injection attacks, SQL injection attack classification, SQL injection attack detection

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Authors: Thi Thao Ninh, Yuri Trusov, José Ramón Botella

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Heterotrimeric G proteins, comprised of three subunits, α, β and γ, are involved in signal transduction pathways that mediate a vast number of processes across the eukaryotic kingdom. 23 Gα subunits are present in humans whereas most plant genomes encode for only one canonical Gα. The disparity observed between Arabidopsis, rice, and maize Gα-deficient mutant phenotypes suggest that Gα functions have diversified between eudicots and monocots during evolution. Alternatively, since the only Gα mutations available in dicots have been produced in Arabidopsis, the possibility exists that this species might be an exception to the rule. In order to test this hypothesis, we studied the G protein α subunit (TGA1) in tomato. Four tga1 knockout lines were generated in tomato cultivar Moneymaker using CRISPR/Cas9. The tga1 mutants exhibit a number of auxin-related phenotypes including changes in leaf shape, reduced plant height, fruit size and number of seeds per fruit. In addition, tga1 mutants have increased sensitivity to abscisic acid during seed germination, reduced sensitivity to exogenous auxin during adventitious root formation from cotyledons and excised hypocotyl explants. Our results suggest that Gα mutant phenotypes in tomato are very similar to those observed in monocots, i.e. rice and maize, and cast doubts about the validity of using Arabidopsis as a model system for plant G protein studies.

Keywords: auxin-related phenotypes, CRISPR/Cas9, G protein α subunit, heterotrimeric G proteins, tomato

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Authors: Martha Serrano-Arias, Martha Frías-Armenta

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Significant results have been found both supporting universality of emotion recognition and cultural background influence. Thus, the aim of this research was to test a Mexican version of the MTSI in different cultures to find differences in their performance. The MTSI-Mx assesses through a scenario approach were subjects must evaluate real persons. Two target persons were used for the construction, a man (FS) and a woman (AD). The items were grouped in four variables: Picture, Video, and FS and AD scenarios. The test was applied to 201 students from Mexico and Germany. T-test for picture and FS scenario show no significance. Video and AD had a significance at the 5% level. Results show slight differences between cultures, although a more comprehensive research is needed to conclude which culture can perform better in this kind of assessments.

Keywords: emotion recognition, MTSI, social intelligence, transcultural study

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Authors: Darshna Sharma, Suban K. Sahoo

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The detection of intracellular fluoride in human cancer cell HeLa was achieved by chemosensors derived from vitamin B6 cofactors using fluorescence imaging technique. These sensors were first synthesized by condensation of pyridoxal/pyridoxal phosphate with 2-amino(thio)phenol. The anion recognition ability was explored by experimental (UV-VIS, fluorescence and 1H NMR) and theoretical DFT [(B3LYP/6-31G(d,p)] methods in DMSO and mixed DMSO-H2O system. All the developed sensors showed both naked-eye detectable color change and remarkable fluorescence enhancement in the presence of F- and AcO-. The anion recognition was occurred through the formation of hydrogen bonded complexes between these anions and sensor, followed by the partial deprotonation of sensor. The detection limit of these sensors were down to micro(nano) molar level of F- and AcO-.

Keywords: chemosensors, fluoride, acetate, turn-on, live cells imaging, DFT

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Authors: Ajay Kadam, Ramesh Kagalkar

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The proposed research objective is to add to a framework for programmed recognition of sound. In this framework the real errand is to distinguish any information sound stream investigate it & anticipate the likelihood of diverse sounds show up in it. To create and industrially conveyed an adaptable sound web crawler a flexible sound search engine. The calculation is clamor and contortion safe, computationally productive, and hugely adaptable, equipped for rapidly recognizing a short portion of sound stream caught through a phone microphone in the presence of frontal area voices and other predominant commotion, and through voice codec pressure, out of a database of over accessible tracks. The algorithm utilizes a combinatorial hashed time-recurrence group of stars examination of the sound, yielding ordinary properties, for example, transparency, in which numerous tracks combined may each be distinguished.

Keywords: fingerprinting, pure tone, white noise, hash function

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Authors: Tagouelbe Tiho, Amissa Augustin Adima, Yao Casimir Brou, Nabayo Traore, Gouha Firmin Kouassi, Thierry Roland Kouame, Maryline Kouba

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The work aimed to study Borassus aethiopum Mart (B.a) dried pulp nutritional value for its incorporation in human and poultry diets. Firstly, the mature fruit macro-composition was assessed. Secondly, the pulp was dried at 40, 50, 60, 70, and 80ᵒC. Thereafter, the analysis was performed for fat, protein, total sugars, Ca, P, Mg, and fatty acid profile monitoring. As a result, the fruits weighed 1,591.35, delivered 516.73, and 677.82 grams of pulp and seeds, respectively. Mainly, increasing heat adversely affected the outputs. Consequently, the fat results were 14.12, 12.97, 8.93, 8.89ᶜ, and 5.56%; protein contents were 11.64, 10.15, 8.97, 8.84, and 8.42%; total sugar deliveries were 6.28, 6.05, 5.26, 5.02, and 4.76% (P < 0.01). Thereafter, the metabolizable energies were 3,785.22; 3,834.28; 3,616.62; 3,667.03; and 3,608.33 kcal/kg (DM). Additionally, Calcium (Ca) contents were 0.51, 0.55, 0.69, 0.77, and 0.81%, while phosphorus (P) mean was 0.17%, and the differences were not significant (P < 0.01). So, the Ca/P ratios were 2.79, 3.04, 4.10, 4.71, and 4.95. Finally, fatty acids (FA) assessments revealed 22.33 saturated (SFA), 77.67 unsaturated (UFA), within which 67.59% were monounsaturated (MUFA). Interestingly, the rising heat depressed n-6/n-3 ratios that were 1.1, 1.1, 0.45 and 0.38, respectively at 40, 50, 70 and 80ᵒC. In short, drying did not only enhance the product shelf life but it also improved the nutritional value. Thus, B.a mature fruit pulps dried at 70ᵒC are good functional foods, with more than 66% MUFA, and energy source for human and poultry nutrition.

Keywords: Borassus aethiopum Mart, fatty acids, metabolizable energy, minerals, protein

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Authors: T. Hari Prasath, P. Ithaya Rani

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In the world of visual technology, recognizing emotions from the face images is a challenging task. Several related methods have not utilized the dynamic facial features effectively for high performance. This paper proposes a method for emotions recognition using dynamic facial features with high performance. Initially, local features are captured by Gabor filter with different scale and orientations in each frame for finding the position and scale of face part from different backgrounds. The Gabor features are sent to the ensemble classifier for detecting Gabor facial features. The region of dynamic features is captured from the Gabor facial features in the consecutive frames which represent the dynamic variations of facial appearances. In each region of dynamic features is normalized using Z-score normalization method which is further encoded into binary pattern features with the help of threshold values. The binary features are passed to Multi-class AdaBoost classifier algorithm with the well-trained database contain happiness, sadness, surprise, fear, anger, disgust, and neutral expressions to classify the discriminative dynamic features for emotions recognition. The developed method is deployed on the Ryerson Multimedia Research Lab and Cohn-Kanade databases and they show significant performance improvement owing to their dynamic features when compared with the existing methods.

Keywords: detecting face, Gabor filter, multi-class AdaBoost classifier, Z-score normalization

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Authors: Hee-Choul Kwon, Heeyong Kwon

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Image rotation is one of main pre-processing steps for image processing or image pattern recognition. It is implemented with a rotation matrix multiplication. It requires a lot of floating point arithmetic operations and trigonometric calculations, so it takes a long time to execute. Therefore, there has been a need for a high speed image rotation algorithm without two major time-consuming operations. However, the rotated image has a drawback, i.e. distortions. We solved the problem using an augmented two-step shear transform. We compare the presented algorithm with the conventional rotation with images of various sizes. Experimental results show that the presented algorithm is superior to the conventional rotation one.

Keywords: high-speed rotation operation, image rotation, transform matrix, image processing, pattern recognition

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Authors: Rujia Chen, Akbar Ghobakhlou, Ajit Narayanan

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This study investigates the task of identifying musical instruments in polyphonic compositions using Convolutional Neural Networks (CNNs) from spectrogram inputs, focusing on binary classification. The model showed promising results, with an accuracy of 97% on solo instrument recognition. When applied to polyphonic combinations of 1 to 10 instruments, the overall accuracy was 64%, reflecting the increasing challenge with larger ensembles. These findings contribute to the field of Music Information Retrieval (MIR) by highlighting the potential and limitations of current approaches in handling complex musical arrangements. Future work aims to include a broader range of musical sounds, including electronic and synthetic sounds, to improve the model's robustness and applicability in real-time MIR systems.

Keywords: binary classifier, CNN, spectrogram, instrument

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Authors: Renata Shitakubo, Hanyu Yangcheng, Jay-lin Jane, Fernanda Peroni Okita, Beatriz Cordenunsi

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In the degradation transitory starch model, the enzymatic activity of glucan/water dikinase (GWD) and phosphoglucan/water dikinase (PWD) are essential for the granule degradation. GWD and PWD phosphorylate glucose molecules in the positions C6 and C3, respectively, in the amylopectin chains. This action is essential to allow that β-amylase degrade starch granules without previous action of α-amylase. During banana starch degradation, as part of banana ripening, both α- and β-amylases activities and proteins were already detected and, it is also known that there is a GWD and PWD protein bounded to the starch granule. Therefore, the aim of this study was to quantify both Gluc-6P and Gluc-3P in order to estimate the importance of the GWD-PWD-β-amylase pathway in banana starch degradation. Starch granules were isolated as described by Peroni-Okita et al (Carbohydrate Polymers, 81:291-299, 2010), from banana fruit at different stages of ripening, green (20.7%), intermediate (18.2%) and ripe (6.2%). Total phosphorus content was determinate following the Smith and Caruso method (1964). Gluc-6P and Gluc-3P quantifications were performed as described by Lim et al (Cereal Chemistry, 71(5):488-493, 1994). Total phosphorous content in green banana starch is found as 0.009%, intermediary banana starch 0.006% and ripe banana starch 0.004%, both by the colorimetric method and 31P-NMR. The NMR analysis showed the phosphorus content in C6 and C3. The results by NMR indicate that the amylopectin is phosphorylate by GWD and PWD before the bananas become ripen. Since both the total content of phosphorus and phosphorylated glucose molecules at positions C3 and C6 decrease with the starch degradation, it can be concluded that this phosphorylation occurs only in the surface of the starch granule and before the fruit be harvested.

Keywords: starch, GWD, PWD, 31P-NMR

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Authors: Ng Liang Shen, Hau Yuan Wen

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Arrhythmia analysis of ECG signal plays a major role in diagnosing most of the cardiac diseases. Therefore, a single arrhythmia detection of an electrocardiographic (ECG) record can determine multiple pattern of various algorithms and match accordingly each ECG beats based on Machine Learning supervised learning. These researchers used different features and classification methods to classify different arrhythmia types. A major problem in these studies is the fact that the symptoms of the disease do not show all the time in the ECG record. Hence, a successful diagnosis might require the manual investigation of several hours of ECG records. The point of this paper presents investigations cardiovascular ailment in Electrocardiogram (ECG) Signals for Cardiac Arrhythmia utilizing examination of ECG irregular wave frames via heart beat as correspond arrhythmia which with Machine Learning Pattern Recognition.

Keywords: electrocardiogram, ECG, classification, machine learning, pattern recognition, detection, QRS

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Authors: Sean W. T. Bayly, Daniel Glover, Don Horrell, Simon Horrocks, Barnes Callum, Stuart Gibson, Mac Misuira

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We assess suitability of recent, ∼7B parameter, instruction-tuned Language Models for Generative Named Entity Recognition (GNER). Alongside Retrieval Augmented Generation (RAG), and supported by task-specific prompting, our proposed Multi-Level Information Retrieval method achieves notable improvements over finetuned entity-level and sentence-level methods. We conclude that language models directed toward this task are highly capable when distinguishing between positive classes (precision). However, smaller models seem to struggle to find all entities (recall). Poorly defined classes such as ”Miscellaneous” exhibit substantial declines in performance, likely due to the ambiguity it introduces to the prompt. This is partially resolved through a self-verification method using engineered prompts containing knowledge of the stricter class definitions, particularly in areas where their boundaries are in danger of overlapping, such as the conflation between the location ”Britain” and the nationality ”British”. Finally, we explore correlations between model performance on the GNER task with performance on relevant academic benchmarks.

Keywords: generative named entity recognition, information retrieval, lightweight artificial intelligence, prompt engineering, personal information identification, retrieval augmented generation, self verification

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Authors: Bruce X. B. Yu, Yan Liu, Keith C. C. Chan

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We propose to record Activities of Daily Living (ADLs) of elderly people using a vision-based system so as to provide better assistive and personalization technologies. Current ADL-related research is based on data collected with help from non-elderly subjects in laboratory environments and the activities performed are predetermined for the sole purpose of data collection. To obtain more realistic datasets for the application, we recorded ADLs for the elderly with data collected from real-world environment involving real elderly subjects. Motivated by the need to collect data for more effective research related to elderly care, we chose to collect data in the room of an elderly person. Specifically, we installed Kinect, a vision-based sensor on the ceiling, to capture the activities that the elderly subject performs in the morning every day. Based on the data, we identified 12 morning activities that the elderly person performs daily. To recognize these activities, we created a HARELCARE framework to investigate into the effectiveness of existing Human Activity Recognition (HAR) algorithms and propose the use of a transfer learning algorithm for HAR. We compared the performance, in terms of accuracy, and training progress. Although the collected dataset is relatively small, the proposed algorithm has a good potential to be applied to all daily routine activities for healthcare purposes such as evidence-based diagnosis and treatment.

Keywords: daily activity recognition, healthcare, IoT sensors, transfer learning

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Authors: K. Larysz, E. Grochowska-Niedworok, M. Kardas, K. Brukalo, B. Calyniuk, R. Polaniak

Abstract:

Eating behaviours and habits are one of the main factors affecting health. Abnormal nutritional status is a growing problem related to nutritional errors. The number of adolescents presenting excess body weight is also rising. The body's demand for all nutrients increases in the period of intensive development, i.e., during puberty. A varied, well-balanced diet and elimination of unhealthy habits are two of the key factors that contribute to the proper development of a young body. The aim of the study was to assess the nutritional status and selected eating behaviours/habits in adolescents attending middle school. An original questionnaire including 24 questions was conducted. A total of 401 correctly completed questionnaires were qualified for the assessment. Body mass index (BMI) was calculated. Furthermore, the frequency of breakfast consumption, the number of meals per day, types of snacks and sweetened beverages, as well as the frequency of consuming fruit and vegetables, dairy products and fast-foods were assessed. The obtained results were analysed statistically. The study showed that malnutrition was more of a problem than overweight or obesity among middle school students. More than 71% of middle school students have breakfast, whereas almost 30% of adolescents skip this meal. Up to 57.6% of respondents most often consume sweets at school. A total of 37% of adolescents consume sweetened beverages daily or almost every day. Most of the respondents consume an optimal number of meals daily. Only 24.7% of respondents consume fruit and vegetables more than once daily. The majority of respondents (49.40%) declared that they consumed fast food several times a month. Satisfactory frequency of consuming dairy products was reported by 32.7% of middle school students. Conclusions of our study: 1. Malnutrition is more of a problem than overweight or obesity among middle school students. They consume excessive amounts of sweets, sweetened beverages, and fast foods. 2. The consumption of fruit and vegetables was too low in the study group. The intake of dairy products was also low in some cases. 3. A statistically significant correlation was found between the frequency of fast food consumption and the intake of sweetened beverages. A low correlation was found between nutritional status and the number of meals per day. The number of meals consumed by these individuals decreased with increasing nutritional status.

Keywords: adolescent, malnutrition, nutrition, nutritional status, obesity

Procedia PDF Downloads 135

Authors: Chinwe Pearl Poly-Mbah, Evelyn Obioma, Juliet Amajuoyi

Abstract:

The investigation here reported examined growth and yield responses of Cucumber to manure rates in Owerri, Southeastern Nigeria. Fruit vegetables are widely cultivated and produced in Northern Nigeria but greatly consumed in Southern Nigeria where cucumbers command high demand and price but are minimally cultivated. Unfortunately, farmers in northern Nigeria incur lots of losses because cucumber is a perishable vegetable and is transported all the way from the northern Nigeria where cucumbers are produced to Southern Nigeria where cucumbers are consumed, hence the high cost of cucumber fruits in Southern Nigeria. There is a need, therefore, to evolve packages that will enhance cucumber production in Southern Nigeria. The main objective of this study was to examine the effects of poultry manure rates on the growth and yield of cucumber in Owerri, South Eastern Nigeria. Specifically, this study was designed to assess the effect of poultry manure rates on number of days to 50% seedling emergence, vine length/plant, leaf area per plant and the number of leaves produced per plant. The design used for the experiment was Randomized Complete Block Design (RCBD) with three blocks (replications). Treatment consisted of four rates of well-decomposed poultry manure at the rate of 0 tons/ha, 2 tons/ha, 4 tons/ha and 6 tons/ha. Data were collected on number of days to 50% seedling emergence, vine length per plant at two weeks interval, leaf number per plant at two weeks interval, leaf area per plant at two weeks interval, number of fruits produced per plant, and fresh weight of fruits per plant at harvest. Results from the analysis of variance (ANOVA) showed that there were highly significant effects (P=0.05) of poultry manure on growth and yield parameters studied which include number of days to 50% seedling emergence, vine length per plant, leaf number per plant, leaf area per plant, fruit number and fruit weight per plant such that increase in poultry manure rates lead to increase in growth and yield parameters studied. Therefore, the null hypothesis (Ho) was rejected, while the alternative hypothesis was accepted. Farmers should be made to know that growing cucumber with poultry manure in southeastern Nigeria agro ecology is a successful enterprise

Keywords: cucumber, effects, growth and yield, manure

Procedia PDF Downloads 239

Authors: Fatima Muhammad Shitu

Abstract:

This study seeks to investigate the effect of the keyword strategy on lexico–semantic acquisition, recognition, retention and comprehension in an ESL context. The aim of the study is to determine whether the keyword strategy can be used to enhance acquisition. As a quasi- experimental research, the objectives of the study include: To determine the extent to which the scores obtained by the subjects, who were trained on the use of the keyword strategy for acquisition, differ at the pre-tests and the post–tests and also to find out the relationship in the scores obtained at these tests levels. The sample for the study consists of 300 hundred undergraduate ESL Students in the Federal College of Education, Kano. The seventy-five lexical items for acquisition belong to the lexical field category known as register, and they include Medical, Agriculture and Photography registers (MAP). These were divided in the ratio twenty-five (25) lexical items in each lexical field. The testing technique was used to collect the data while the descriptive and inferential statistics were employed for data analysis. For the purpose of testing, the two kinds of tests administered at each test level include the WARRT (Word Acquisition, Recognition, and Retention Test) and the CCPT (Cloze Comprehension Passage Test). The results of the study revealed that there are significant differences in the scores obtained between the pre-tests, and the post–tests and there are no correlations in the scores obtained as well. This implies that the keyword strategy has effectively enhanced the acquisition of the lexical items studied.

Keywords: keyword, lexical, semantics, strategy

Procedia PDF Downloads 311