Search results for: hybrid feature selection

Authors: P. Satish Kumar, K. Ramakrishna, Ch. Lokeshwar Reddy, G. Sridhar

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This paper presents two different sequential switching hybrid-modulation strategies and implemented for cascaded multilevel inverters. Hybrid modulation strategies represent the combinations of Fundamental-Frequency Pulse Width Modulation (FFPWM) and Multilevel Sinusoidal-Modulation (MSPWM) strategies, and are designed for performance of the well-known Alternative Phase Opposition Disposition (APOD), Phase Shifted Carrier (PSC). The main characteristics of these modulations are the reduction of switching losses with good harmonic performance, balanced power loss dissipation among the devices with in a cell, and among the series-connected cells. The feasibility of these modulations is verified through spectral analysis, power loss analysis and simulation.

Keywords: cascaded multilevel inverters, hybrid modulation, power loss analysis, pulse width modulation

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Authors: K. P. Sandesh, M. H. Suman

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Text processing plays an important role in information retrieval, data-mining, and web search. Measuring the similarity between the documents is an important operation in the text processing field. In this project, a new similarity measure is proposed. To compute the similarity between two documents with respect to a feature the proposed measure takes the following three cases into account: (1) The feature appears in both documents; (2) The feature appears in only one document and; (3) The feature appears in none of the documents. The proposed measure is extended to gauge the similarity between two sets of documents. The effectiveness of our measure is evaluated on several real-world data sets for text classification and clustering problems, especially in banking and health sectors. The results show that the performance obtained by the proposed measure is better than that achieved by the other measures.

Keywords: document classification, document clustering, entropy, accuracy, classifiers, clustering algorithms

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Authors: Siti Rahmah Shamsuri, Eiichiro Matsubara, Shohei Shiomi

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The 1D nanomaterial is far surpassed the 0D nanomaterial. It does not just offer most of the benefit of the 0D nanomaterial such as the large surface area, a great number of active site and an efficient interfacial charge transfer but also can assemble into free-standing and flexible electrode due to their high aspect ratio. Thus, it is essential to develop a simple and ease synthesis of this 1D nanomaterial for the practical application. Here, nickel nanowire/cellulose hybrid has been successfully fabricated via a simple liquid-phase method with the assist of the magnetic field. A finer nickel nanowire was heterogeneously nucleated on the surface of the cellulose fiber, which demonstrated the effect of the hydroxyl group on the cellulose structure. The result of the nickel nanowire size was found to vary from 66-114 nm. A detailed discussion on the mechanism of the nickel nanowire/ cellulose hybrid formation is also shown in this paper.

Keywords: cellulose nanofiber, liquid-phase reduction, metal nanowire, nano hybrid material

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Authors: Yildiz Stella Dak, Jale Tezcan

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Ground motion models that relate a strong motion parameter of interest to a set of predictive seismological variables describing the earthquake source, the propagation path of the seismic wave, and the local site conditions constitute a critical component of seismic hazard analyses. When a sufficient number of strong motion records are available, ground motion relations are developed using statistical analysis of the recorded ground motion data. In regions lacking a sufficient number of recordings, a synthetic database is developed using stochastic, theoretical or hybrid approaches. Regardless of the manner the database was developed, ground motion relations are developed using regression analysis. Development of a ground motion relation is a challenging process which inevitably requires the modeler to make subjective decisions regarding the inclusion criteria of the recordings, the functional form of the model and the set of seismological variables to be included in the model. Because these decisions are critically important to the validity and the applicability of the model, there is a continuous interest on procedures that will facilitate the development of ground motion models. This paper proposes the use of the Least Absolute Shrinkage and Selection Operator (LASSO) in selecting the set predictive seismological variables to be used in developing a ground motion relation. The LASSO can be described as a penalized regression technique with a built-in capability of variable selection. Similar to the ridge regression, the LASSO is based on the idea of shrinking the regression coefficients to reduce the variance of the model. Unlike ridge regression, where the coefficients are shrunk but never set equal to zero, the LASSO sets some of the coefficients exactly to zero, effectively performing variable selection. Given a set of candidate input variables and the output variable of interest, LASSO allows ranking the input variables in terms of their relative importance, thereby facilitating the selection of the set of variables to be included in the model. Because the risk of overfitting increases as the ratio of the number of predictors to the number of recordings increases, selection of a compact set of variables is important in cases where a small number of recordings are available. In addition, identification of a small set of variables can improve the interpretability of the resulting model, especially when there is a large number of candidate predictors. A practical application of the proposed approach is presented, using more than 600 recordings from the National Geospatial-Intelligence Agency (NGA) database, where the effect of a set of seismological predictors on the 5% damped maximum direction spectral acceleration is investigated. The set of candidate predictors considered are Magnitude, Rrup, Vs30. Using LASSO, the relative importance of the candidate predictors has been ranked. Regression models with increasing levels of complexity were constructed using one, two, three, and four best predictors, and the models’ ability to explain the observed variance in the target variable have been compared. The bias-variance trade-off in the context of model selection is discussed.

Keywords: ground motion modeling, least absolute shrinkage and selection operator, penalized regression, variable selection

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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: Uswah Ahmad Khan, Muhammad Moazam Fraz, Humayoon Shafique Satti, Qasim Aziz

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Approximately 10-14% of the global population experiences a functional disorder known as irritable bowel syndrome (IBS). The disorder is defined by persistent abdominal pain and an irregular bowel pattern. IBS significantly impairs work productivity and disrupts patients' daily lives and activities. Although IBS is widespread, there is still an incomplete understanding of its underlying pathophysiology. This study aims to help characterize the phenotype of IBS patients by differentiating the comorbidities found in IBS patients from those in non-IBS patients using machine learning algorithms. In this study, we extracted samples coding for IBS from the UK BioBank cohort and randomly selected patients without a code for IBS to create a total sample size of 18,000. We selected the codes for comorbidities of these cases from 2 years before and after their IBS diagnosis and compared them to the comorbidities in the non-IBS cohort. Machine learning models, including Decision Trees, Gradient Boosting, Support Vector Machine (SVM), AdaBoost, Logistic Regression, and XGBoost, were employed to assess their accuracy in predicting IBS. The most accurate model was then chosen to identify the features associated with IBS. In our case, we used XGBoost feature importance as a feature selection method. We applied different models to the top 10% of features, which numbered 50. Gradient Boosting, Logistic Regression and XGBoost algorithms yielded a diagnosis of IBS with an optimal accuracy of 71.08%, 71.427%, and 71.53%, respectively. Among the comorbidities most closely associated with IBS included gut diseases (Haemorrhoids, diverticular diseases), atopic conditions(asthma), and psychiatric comorbidities (depressive episodes or disorder, anxiety). This finding emphasizes the need for a comprehensive approach when evaluating the phenotype of IBS, suggesting the possibility of identifying new subsets of IBS rather than relying solely on the conventional classification based on stool type. Additionally, our study demonstrates the potential of machine learning algorithms in predicting the development of IBS based on comorbidities, which may enhance diagnosis and facilitate better management of modifiable risk factors for IBS. Further research is necessary to confirm our findings and establish cause and effect. Alternative feature selection methods and even larger and more diverse datasets may lead to more accurate classification models. Despite these limitations, our findings highlight the effectiveness of Logistic Regression and XGBoost in predicting IBS diagnosis.

Keywords: comorbidities, disease association, irritable bowel syndrome (IBS), predictive analytics

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Authors: Rojin Bana Derakhshan, Abbas Toloie

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For the purpose of optimizing and developing energy efficiency in building, it is required to recognize key elements of success in optimization of energy consumption before performing any actions. Surveying Principal Components is one of the most valuable result of Linear Algebra because the simple and non-parametric methods are become confusing. So that energy management system implemented according to energy management system international standard ISO50001:2011 and all energy parameters in building to be measured through performing energy auditing. In this essay by simulating used of data mining, the key impressive elements on energy saving in buildings to be determined. This approach is based on data mining statistical techniques using feature selection method and fuzzy logic and convert data from massive to compressed type and used to increase the selected feature. On the other side, influence portion and amount of each energy consumption elements in energy dissipation in percent are recognized as separated norm while using obtained results from energy auditing and after measurement of all energy consuming parameters and identified variables. Accordingly, energy saving solution divided into 3 categories, low, medium and high expense solutions.

Keywords: energy saving, key elements of success, optimization of energy consumption, data mining

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Authors: Nougrara Zineb

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The unique structure of the blood vessels in the retina has been used for biometric identification. The retina blood vessel pattern is a unique pattern in each individual and it is almost impossible to forge that pattern in a false individual. The retina biometrics’ advantages include high distinctiveness, universality, and stability overtime of the blood vessel pattern. Once the creases have been extracted from the images, a registration stage is necessary, since the position of the retinal vessel structure could change between acquisitions due to the movements of the eye. Image registration consists of following steps: Feature detection, feature matching, transform model estimation and image resembling and transformation. In this paper, we present an algorithm of registration; it is based on the characterization of retinal feature points. For experiments, retinal images from the DRIVE database have been tested. The proposed methodology achieves good results for registration in general.

Keywords: fovea, optic disc, registration, retinal images

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Authors: S. Mhamdi, O. Brendel, P. Montpied, K. Ben Yahia, N. Saouyah, B. Hasnaoui, E. Dreyer

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Leaf Life Span (LLS) is used to classify trees into two main groups: evergreen and deciduous species. It varies according to the forms of life between taxonomic groups. Co-occurrence of deciduous and evergreen oaks is common in some Mediterranean type climate areas. Nevertheless, in the Tunisian forests, there is no enough information about the functional inter-specific diversity among oak species, especially in the mixed stand marked by the simultaneous presence of Q. suber L., Q. canariensis Willd. and their hybrid (Q. afares), the latter being an endemic oak species threatened with extinction. This study has been conducted to estimate the LLS, the relative growth rate, and the count of different growth flushes of samplings in semi-controlled conditions. Our study took 17 months, with an observation's interval of 4 weeks. The aim is to characterize and compare the hybrid species to the parental ones. Differences were observed among species, both for phenology and growth. Indeed, Q. suber saplings reached higher total height and number of growth flushes then Q. canariensis, while Q. afares showed much less growth flushes than the parental species. The LLS of parental species has exceeded the duration of the experiment, but their hybrid lost all leaves on all cohorts. The short LLSs of hybrid species are in accordance with this phenology in the field, but for Q. canariensis there was a contrast with observations in the field where phenology is strictly annual. This study allowed us to differentiate the hybrid from both parental species.

Keywords: leaf life span, growth, hybrid, Q. afares Pomel, Q. suber L., Q.canariensis Willd

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Authors: Sooyoung Cho, In-Gun Kim, Hyun-Seok Hong, Dong-Woo Kang, Ju Lee

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Series hybrid UAV(Unmanned aerial vehicle) that is proposed in this paper performs VTOL(Vertical take-off and landing) using the battery and generator, and it applies the series hybrid system with combination of the small engine and generator when cruising flight. This system can be described as the next-generation system that can dramatically increase the UAV flight times. Also, UAV systems require a large energy at the time of VTOL to be conducted for a short time. Therefore, this paper designs PMSG(Permanent Magnet Synchronous Generator) having a high specific power considering VTOL through the FEA.

Keywords: PMSG, VTOL, UAV, high specific power density

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Han Xue, Zhang Lanyue

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In order to seek more effective feature extraction technology, feature extraction method based on MFCC combined with vector hydrophone is exposed in the paper. The sound pressure signal and particle velocity signal of two kinds of ships are extracted by using MFCC and its evolution form, and the extracted features are fused by using fisher-ratio and correlated distance criterion. The features are then identified by BP neural network. The results showed that MFCC, First-Order Differential MFCC and Second-Order Differential MFCC features can be used as effective features for recognition of underwater targets, and the fusion feature can improve the recognition rate. Moreover, the results also showed that the recognition rate of the particle velocity signal is higher than that of the sound pressure signal, and it reflects the superiority of vector signal processing.

Keywords: vector information, MFCC, differential MFCC, fusion feature, BP neural network

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Authors: Mais Wa'ad

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The effect of transmission distance on the performance of hybrid configuration H 10-40 Gb/s with Non-Return to Zero (NRZ) pulse format, 100 GHz channel spacing, and Multiplexer/De-Multiplexer Band width (MUX/DEMUX BW) of 60 GHz has been investigated in this study. The laser Continuous Wave (CW) power launched into the modulator is set to 4 dBm. Eight neighboring DWDM channels are selected around 1550.12 nm carrying different data rates in hybrid optical communication systems travel through the same optical fiber and use the same passive and active optical modules. The simulation has been done using Optiwave Inc Optisys software. Usually, increasing distance will lead to decrease in performance; however this is not always the case, as the simulation conducted in this work, shows different system performance for each channel. This is due to differences in interaction between dispersion and non-linearity, and the differences in residual dispersion for each channel.

Keywords: dispersion and non-linearity interaction, optical hybrid configuration, multiplexer/de multiplexer bandwidth, non-return to zero, optical transmission distance, optisys

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Authors: Lori Timmis

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The COVID-19 pandemic precipitated significant organizational change in employee turnover, retirements, and burnout exacerbated by enrollment declines in higher education, especially community colleges. To counter this downturn, community college leaders must thoughtfully examine meaningful work opportunities to retain an engaged and productive workforce. Higher education led fully remote teams during the pandemic, which highlighted the benefits and weaknesses of building and leading remote teams. Hybrid remote teams offer possibility to reimagine community college structures, though leading remote teams requires specific e-leadership competencies. This paper examines the literature of studies on e-leadership conducted during the pandemic and from several higher education studies, pre-pandemic, against an e-leadership competency framework. The e-leadership studies conducted pre-pandemic and from the pandemic complement the e-leadership competency framework, comprising six e-leadership competencies performed via information technology communications, which provides community college (and higher education) leaders to consider hybrid remote team structures and the necessary leadership skills to lead hybrid remote teams.

Keywords: community college, e-leadership, great resignation, hybrid remote teams

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Authors: Rai Mansoor Imtiaz

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Guardians in hybrid regimes fragment opposition parties and ban their political leaders, and disenfranchise their voters' political participation. When guardians in hybrid regimes are so powerful that they remain decisive on electoral politics of states, and have powers to ban political parties and their leadership, then "why do political parties backed by those powerful guardians lose elections" and "how do anti-establishment parties make electoral inroads at the local and national levels." These two questions are interrelated with the key research question of my research "why do people vote for political parties rejected by powerful guardians in establishmentarian democracies." Furthermore, this research question is important to be explored for two reasons. First, existing literature only reflects the electoral victories of opposition parties or defeats of military-sponsored parties (see Thailand and Turkey) but remains silent on political change that led the anti-military parties to win the elections. Second, why is it a case that people belonging to the countries where militaries remain popular among the public (see Turkey and Pakistan) have started putting their trust in anti-establishment politicians who criticise the military against their intervention in politics? For instance, in Pakistan, where commenting against the military is meant to comment against the state –– an anti-military narrative is getting popular support. The conceptual framework of hybrid states in this research relies on the concept of a 'reserved domain/tutelary body' (guardians of hybrid states). However, this research makes a case that hybrid states are not consolidated separate political entities but rather vacillated states that fluctuate between democratic and authoritarian practices. This paper, therefore, uses the term establishmentarian democracy as a subtype of the hybrid regime, which is more consolidated than a hybrid democracy.

Keywords: Guardians, Hybrid Regimes, Voters, Elections, Democracy, South Asia

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Authors: Bubai Maji, Monorama Swain

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Speech is considered an essential and most natural medium for the interaction between machines and humans. However, extracting effective features for speech emotion recognition (SER) is remains challenging. The present studies show that the temporal information captured but high-level temporal-feature learning is yet to be investigated. In this paper, we present an efficient novel method using the Self-attention (SA) mechanism in a combination of Convolutional Neural Network (CNN) and Bi-directional Gated Recurrent Unit (Bi-GRU) network to learn high-level temporal-feature. In order to further enhance the representation of the high-level temporal-feature, we integrate a Bi-GRU output with learnable weights features by SA, and improve the performance. We evaluate our proposed method on our created SITB-OSED and IEMOCAP databases. We report that the experimental results of our proposed method achieve state-of-the-art performance on both databases.

Keywords: Bi-GRU, 1D-CNNs, self-attention, speech emotion recognition

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Authors: Soufiane Benoumhani, Nadia Saifi, Boubekeur Dokkar, Mohamed Cherif Benzid

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This work presents the design and simulation of electric equipment for a hybrid solar vehicle. The new drive train of this vehicle is a parallel hybrid system which means a vehicle driven by a great percentage of an internal combustion engine with 49.35 kW as maximal power and electric motor only as assistance when is needed. This assistance is carried out on the rear axle by a single electric motor of 7.22 kW as nominal power. The motor is driven by 12 batteries connecting in series, which are charged by three PV panels (300 W) installed on the roof and hood of the vehicle. The individual components are modeled and simulated by using the Matlab Simulink environment. The whole system is examined under different load conditions. The reduction of CO₂ emission is obtained by reducing fuel consumption. With the use of this hybrid system, fuel consumption can be reduced from 6.74 kg/h to 5.56 kg/h when the electric motor works at 100 % of its power. The net benefit of the system reaches 1.18 kg/h as fuel reduction at high values of power and torque.

Keywords: light car, hybrid system, PV panel, electric motor

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Authors: Indranil Bayal, Pradipta Panchadhyayee

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Path-spin hybrid entanglement generated and confined in a single spin-1/2 particle is converted to spin-spin hybrid interparticle entanglement, which finds its important applications in quantum information processing. This protocol uses beam splitter, spin flipper, spin measurement, classical channel, unitary transformations, etc., and requires no collective operation on the pair of particles whose spin variables share complete entanglement after the accomplishment of the protocol. The specialty of the protocol lies in the fact that the path-spin entanglement is transferred between spin degrees of freedom of two separate particles initially possessed by a single party.

Keywords: entanglement, path-spin entanglement, spin-spin entanglement, CNOT operation

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Authors: Mohd Iz'aan Paiz Bin Zamri, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, Rubiyah Yusof

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Effective statistical feature extraction and classification are important in image-based automatic inspection and analysis. An automatic wood species recognition system is designed to perform wood inspection at custom checkpoints to avoid mislabeling of timber which will results to loss of income to the timber industry. The system focuses on analyzing the statistical pores properties of the wood images. This paper proposed a fuzzy-based feature extractor which mimics the experts’ knowledge on wood texture to extract the properties of pores distribution from the wood surface texture. The proposed feature extractor consists of two steps namely pores extraction and fuzzy pores management. The total number of statistical features extracted from each wood image is 38 features. Then, a backpropagation neural network is used to classify the wood species based on the statistical features. A comprehensive set of experiments on a database composed of 5200 macroscopic images from 52 tropical wood species was used to evaluate the performance of the proposed feature extractor. The advantage of the proposed feature extraction technique is that it mimics the experts’ interpretation on wood texture which allows human involvement when analyzing the wood texture. Experimental results show the efficiency of the proposed method.

Keywords: classification, feature extraction, fuzzy, inspection system, image analysis, macroscopic images

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Authors: Wael I. Alnahhal

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It is a major challenge to build a bridge superstructure that has long-term durability and low maintenance requirements. A solution to this challenge may be to use new materials or to implement new structural systems. Fiber reinforced polymer (FRP) composites have continued to play an important role in solving some of persistent problems in infrastructure applications because of its high specific strength, light weight, and durability. In this study, the concept of the hybrid FRP-concrete structural systems is applied to a bridge superstructure. The hybrid FRP-concrete bridge superstructure is intended to have durable, structurally sound, and cost effective hybrid system that will take full advantage of the inherent properties of both FRP materials and concrete. In this study, two hybrid FRP-concrete bridge systems were investigated. The first system consists of trapezoidal cell units forming a bridge superstructure. The second one is formed by arch cells. The two systems rely on using cellular components to form the core of the bridge superstructure, and an outer shell to warp around those cells to form the integral unit of the bridge. Both systems were investigated analytically by using finite element (FE) analysis. From the rigorous FE studies, it was concluded that first system is more efficient than the second.

Keywords: bridge superstructure, hybrid system, fiber reinforced polymer, finite element analysis

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Authors: Ergham Al Bachir

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This study adopts strategic leadership (Upper Echelons) as the core theory and contextual leadership theory as the research lens. This research asks how the external context impacts strategic leadership effectiveness to achieve the outcomes in pluralistic organizations (PO). The study explores how the context influences the selection of CEOs, top management teams (TMT), and their leadership effectiveness. POs are characterized by the multiple objectives of their top management teams, divergent objectives, multiple strategies, and multiple governing authorities. The research question is explored by means of a qualitative multiple-case study focusing on healthcare, real estate, and financial services organizations. The data sources are semi-structured interviews, documents, and direct observations. The data analysis strategy is inductive and deploys thematic analysis and cross-case synthesis. The findings differentiate between national and international CEOs' delegation of authority and relationship with the Board of Directors. The findings identify the elements of the dynamic context that influence TMT and PO outcomes. The emergent hybrid strategic and contextual leadership framework shows how the different contextual factors influence strategic direction, PO context, selection of CEOs and TMT, and the outcomes in four pluralistic organizations. The study offers seven theoretical contributions to Upper Echelons, strategic leadership, and contextual leadership research. (1) The integration of two theories revealed how CEO’s impact on the organization is complementary to the contextual impact. (2) Conducting this study in the Middle East contributes to strategic leadership and contextual leadership research. (3) The demonstration of the significant contextual effects on the selection of CEOs. (4 and 5) Two contributions revealed new links between the context, the Board role, internal versus external CEOs, and national versus international CEOs. (6 and 7) This study offered two definitions: what accounts for CEO leadership effectiveness and organizational outcomes. Two methodological contributions were also identified: (1) Previous strategic leadership and Upper Echelons research are mainly quantitative, while this study adopts qualitative multiple-case research with face-to-face interviews. (2) The extrication of the CEO from the TMT advanced the data analysis in strategic leadership research. Four contributions are offered to practice: (1) The CEO's leadership effectiveness inside and outside the organization. (2) Rapid turnover of predecessor CEOs signifies the need for a strategic and contextual approach to CEOs' succession. (3) TMT composition and education impact on TMT-CEO and TMT-TMT interface. (4) Multilevel strategic contextual leadership development framework.

Keywords: strategic leadership, contextual leadership, upper echelons, pluralistic organizations, cross-cultural leadership

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Authors: Elyes Zarrouk, Yassine Benayed, Faiez Gargouri

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This paper presents a new hybrid model for isolated Arabic words recognition. To do this, we apply Support Vectors Machine (SVM) as an estimator of posterior probabilities within the Dynamic Bayesian networks (DBN). This paper deals a comparative study between DBN and SVM/DBN systems for multi-dialect isolated Arabic words. Performance using SVM/DBN is found to exceed that of DBNs trained on an identical task, giving higher recognition accuracy for four different Arabic dialects. In fact, the average of recognition rates for the four dialects with SVM/DBN was 87.67% while 83.01% with DBN.

Keywords: dynamic Bayesian networks, hybrid models, supports vectors machine, Arabic isolated words

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Authors: Shaode Yu, Jiajian Meng, Bing Zhu, Hang Yu, Qiurui Sun

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Speech emotion recognition (SER) is for recognizing human subjective emotions through audio data in-depth analysis. From speech audios, how to comprehensively extract emotional information and how to effectively fuse extracted features remain challenging. This paper presents a dual-stream SER framework that embraces both full training and transfer learning of different networks for thorough feature encoding. Besides, a plug-and-play cross-attention fusion (CAF) module is implemented for the valid integration of the dual-stream encoder output. The effectiveness of the proposed CAF module is compared to the other three fusion modules (feature summation, feature concatenation, and feature-wise linear modulation) on two databases (RAVDESS and IEMO-CAP) using different dual-stream encoders (full training network, DPCNN or TextRCNN; transfer learning network, HuBERT or Wav2Vec2). Experimental results suggest that the CAF module can effectively reconcile conflicts between features from different encoders and outperform the other three feature fusion modules on the SER task. In the future, the plug-and-play CAF module can be extended for multi-branch feature fusion, and the dual-stream SER framework can be widened for multi-stream data representation to improve the recognition performance and generalization capacity.

Keywords: speech emotion recognition, cross-attention fusion, dual-stream, pre-trained

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Authors: Arash Rahmani, Ahmad Ghanbari, Abbas Baghernezhad, Babak Safaei

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In the design of parallel manipulators, usually mean value of a dexterity measure over the workspace volume is considered as the objective function to be used in optimization algorithms. The mentioned indexes in a hybrid parallel manipulator (HPM) are quite complicated to solve thanks to infinite solutions for every point within the workspace of the redundant manipulators. In this paper, spatial isotropic design axioms are extended as a well-known method for optimum design of manipulators. An upper limit for the isotropy measure of HPM is calculated and instead of computing and minimizing isotropy measure, minimizing the obtained limit is considered. To this end, two different objective functions are suggested which are obtained from objective functions of comprising modules. Finally, by using genetic algorithm (GA), the best geometric parameters for a specific hybrid parallel robot which is composed of two modified Gough-Stewart platforms (MGSP) are achieved.

Keywords: hybrid manipulator, spatial isotropy, genetic algorithm, optimum design

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Authors: Krzysztof Rafał, Weronika Radziszewska, Hubert Biedka, Oskar Grabowski, Krzysztof Mik

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We present a method to choose energy storage technologies and their parameters for the economic operation of a microgrid. A grid-connected system with local loads and PV generation is assumed, where an energy storage system (ESS) is attached to minimize energy cost by providing energy balancing and arbitrage functionalities. The ESS operates in a hybrid configuration and consists of two unique technologies operated in a coordinated way. Based on given energy profiles and economical data a model calculates financial flow for ESS investment, including energy cost and ESS depreciation resulting from degradation. The optimization strategy proposes a hybrid set of two technologies with their respective power and energy ratings to minimize overall system cost in a given timeframe. Results are validated through microgrid simulations using real-life input profiles.

Keywords: energy storage, hybrid energy storage, cost-benefit analysis, microgrid, battery sizing

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Authors: Antonios Paraskevas, Michael Madas

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For a university to maintain its international competitiveness in education, it is essential to recruit qualitative academic staff as it constitutes its most valuable asset. This selection demonstrates a significant role in achieving strategic objectives, particularly by emphasizing a firm commitment to the exceptional student experience and innovative teaching and learning practices of high quality. In this vein, the appropriate selection of academic staff establishes a very important factor of competitiveness, efficiency and reputation of an academic institute. Within this framework, our work demonstrates a comprehensive methodological concept that emphasizes the multi-criteria nature of the problem and how decision-makers could utilize our approach in order to proceed to the appropriate judgment. The conceptual framework introduced in this paper is built upon a hybrid neutrosophic method based on the Neutrosophic Analytical Hierarchy Process (N-AHP), which uses the theory of neutrosophy sets and is considered suitable in terms of a significant degree of ambiguity and indeterminacy observed in the decision-making process. To this end, our framework extends the N-AHP by incorporating the Neutrosophic Delphi Method (N-DM). By applying the N-DM, we can take into consideration the importance of each decision-maker and their preferences per evaluation criterion. To the best of our knowledge, the proposed model is the first which applies the Neutrosophic Delphi Method in the selection of academic staff. As a case study, it was decided to use our method for a real problem of academic personnel selection, having as the main goal to enhance the algorithm proposed in previous scholars’ work, and thus taking care of the inherent ineffectiveness which becomes apparent in traditional multi-criteria decision-making methods when dealing with situations alike. As a further result, we prove that our method demonstrates greater applicability and reliability when compared to other decision models.

Keywords: multi-criteria decision making methods, analytical hierarchy process, delphi method, personnel recruitment, neutrosophic set theory

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Authors: Mewa Singh

Abstract:

Nanomedicine, a fusion of nanotechnology and medicine, is an emerging technology ideally suited to the targeted therapies. Nanoparticles overcome the low selectivity of anti-cancer drugs toward the tumor as compared to normal tissue and hence result-in less severe side-effects. Our new technology, HYBRID-NANOENGINEERING™, uses a new molecule (MR007) in the creation of nanoparticles that not only helps in nanonizing the medicine but also provides synergy to the medicine. The simplified manufacturing process will result in reduced manufacturing costs. Treatment is made more convenient because hybrid nanomedicines can be produced in oral, injectable or transdermal formulations. The manufacturing process uses no protein, oil or detergents. The particle size is below 180 nm with a narrow distribution of size. Importantly, these properties confer great stability of the structure. The formulation does not aggregate in plasma and is stable over a wide range of pH. The final hybrid formulation is stable for at least 18 months as a powder. More than 97 drugs, including paclitaxel, docetaxel, tamoxifen, doxorubicinm prednisone, and artemisinin have been nanonized in water soluble formulations. Preclinical studies on cell cultures of tumors show promising results. Our HYBRID-NANOENGINEERING™ platform enables the design and development of hybrid nano-pharmaceuticals that combine efficacy with tolerability, giving patients hope for both extended overall survival and improved quality of life. This study would discuss or present this new discovery of HYBRID-NANOENGINEERING™ which targets drug delivery, synergistic, and potentiating effects, and barriers of drug delivery and advanced drug delivery systems.

Keywords: nano-medicine, nano-particles, drug delivery system, pharmaceuticals

Procedia PDF Downloads 461

Authors: Umesh Kumar Soni, Suresh Kumar Soni, S. R. Awasthi

Abstract:

Worldwide biggest difficulties are climate change, future availability of fossil fuels, and economical feasibility of renewable energy. They force us to use to a greater extent renewable energy and develop suitable hybrid renewable systems. Building heating/cooling consumes significant amount of energy. It can be conserved by use of proper heating/cooling techniques. This paper reviews and critically analyzes various active, passive and hybrid heating/cooling techniques used in green buildings.

Keywords: natural ventilation, energy conservation, hybrid ventilation techniques, climate change

Procedia PDF Downloads 576

Authors: J. K. Alhassan, C. S. Actsu

Abstract:

This study is on the prediction of feature performance of undergraduate students with Artificial Neural Networks (ANN). With the growing decline in the quality academic performance of undergraduate students, it has become essential to predict the students’ feature academic performance early in their courses of first and second years and to take the necessary precautions using such prediction-based information. The feed forward multilayer neural network model was used to train and develop a network and the test carried out with some of the input variables. A result of 80% accuracy was obtained from the test which was carried out, with an average error of 0.009781.

Keywords: academic performance, artificial neural network, prediction, students

Procedia PDF Downloads 433

Authors: Shah Muhammad Butt, Ahmed Masaud Ansair

Abstract:

Present financial crisis in Higher Educational Institutes (HEIs) is causing lots of problems such as considerable budget cuts, which makes it difficult to meet the ever growing IT based research and learning needs. Institutions are rapidly planning and promoting cloud based approaches for their academic and research needs. A cost-effective hybrid cloud framework for HEIs will provide educational services for campus or intercampus communication. Hybrid cloud framework comprises private and public cloud approaches. This paper will propose the framework based on the Open Source Cloud (OpenNebula for Virtualization, Eucalyptus for Infrastructure and Aneka for programming development environment) combined with CSPs services which are delivered to the end-user via the internet from public clouds such as Google, Microsoft, Zoho, and Salesforce.

Keywords: educational services, hybrid campus cloud, open source, higher educational institutes

Procedia PDF Downloads 451