Search results for: quantum convolutional neural networks

Authors: Md. Shah Alam

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Mushfiq Ahmad has defined a Mixed Number, which is the sum of a scalar and a Cartesian vector. He has also defined the elementary group operations of Mixed numbers i.e. the norm of Mixed numbers, the product of two Mixed numbers, the identity element and the inverse. It has been observed that Mixed Number is consistent with Pauli matrix algebra and a handy tool to work with Dirac electron theory. Its use as a mathematical method in Physics has been studied. (1) We have applied Mixed number in Quantum Mechanics: Mixed Number version of Displacement operator, Vector differential operator, and Angular momentum operator has been developed. Mixed Number method has also been applied to Klein-Gordon equation. (2) We have applied Mixed number in Electrodynamics: Mixed Number version of Maxwell’s equation, the Electric and Magnetic field quantities and Lorentz Force has been found. (3) An associative transformation of Mixed Number numbers fulfilling Lorentz invariance requirement is developed. (4) We have applied Mixed number algebra as an extension of Complex number. Mixed numbers and the Quaternions have isomorphic correspondence, but they are different in algebraic details. The multiplication of unit Mixed number and the multiplication of unit Quaternions are different. Since Mixed Number has properties similar to those of Pauli matrix algebra, Mixed Number algebra is a more convenient tool to deal with Dirac equation.

Keywords: mixed number, special relativity, quantum mechanics, electrodynamics, pauli matrix

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Authors: Babar Sultan, Kiran Sultan, Waseem Khan, Ijaz Mansoor Qureshi

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In this paper, a novel idea for the performance enhancement of secondary network is proposed for Underlay Cognitive Radio Networks (CRNs). In Underlay CRNs, primary users (PUs) impose strict interference constraints on the secondary users (SUs). The proposed scheme is based on Artificial Bee Colony (ABC) optimization for relay selection and power allocation to handle the highlighted primary challenge of Underlay CRNs. ABC is a simple, population-based optimization algorithm which attains global optimum solution by combining local search methods (Employed and Onlooker Bees) and global search methods (Scout Bees). The proposed two-phase relay selection and power allocation algorithm aims to maximize the signal-to-noise ratio (SNR) at the destination while operating in an underlying mode. The proposed algorithm has less computational complexity and its performance is verified through simulation results for a different number of potential relays, different interference threshold levels and different transmit power thresholds for the selected relays.

Keywords: artificial bee colony, underlay spectrum sharing, cognitive radio networks, amplify-and-forward

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Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

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Authors: Li Hui, Riyadh Hindi

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In the United States, bridge construction often employs overhang brackets to support the deck overhang, the weight of fresh concrete, and loads from construction equipment. This approach, however, can lead to significant torsional moments on the exterior girders, potentially causing excessive girder rotation. Such rotations can result in various safety and maintenance issues, including thinning of the deck, reduced concrete cover, and cracking during service. Traditionally, these issues are addressed by installing temporary lateral bracing systems and conducting comprehensive torsional analysis through detailed finite element analysis for the construction of bridge deck overhang. However, this process is often intricate and time-intensive, with the spacing between temporary lateral bracing systems usually relying on the field engineers’ expertise. In this study, a deep neural network model is introduced to limit exterior girder rotation during bridge deck construction. The model predicts the optimal spacing between temporary bracing systems. To train this model, over 10,000 finite element models were generated in SAP2000, incorporating varying parameters such as girder dimensions, span length, and types and spacing of lateral bracing systems. The findings demonstrate that the deep neural network provides an effective and efficient alternative for limiting the exterior girder rotation for bridge deck construction. By reducing dependence on extensive finite element analyses, this approach stands out as a significant advancement in improving safety and maintenance effectiveness in the construction of bridge decks.

Keywords: bridge deck construction, exterior girder rotation, deep learning, finite element analysis

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Authors: Hamida Qumber Ali, Waseem Muhammad Arain, Shama Siddiqui, Sayeed Ghani

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We present an implementing of RMAC in TinyOS 1.x. RMAC is a cross layer and Duty-cycle MAC protocols that was proposed to provide energy efficient transmission services for wireless sensor networks. The protocol has a unique and efficient packet transmission scheduling mechanism that enables it to overcome delivery latency and overcome traffic congestion. Design details and implementation challenges are divulged. Experiments are conducted to show the correctness of our implementation with numerous assumptions. Simulations are performed to compare the performance of RMAC and SMAC. Our results show that RMAC outperforms SMAC in energy efficiency and delay.

Keywords: MAC protocol, performance, RMAC, wireless sensor networks

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Authors: Piyangkun Kukutapan, Siridech Boonsang

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The article proposes maximum power point tracking without mechanical sensor using Multilayer Perceptron Neural Network (MLPNN). The aim of article is to reduce the cost and complexity but still retain efficiency. The experimental is that duty cycle is generated maximum power, if it has suitable qualification. The measured data from DC generator, voltage (V), current (I), power (P), turnover rate of power (dP), and turnover rate of voltage (dV) are used as input for MLPNN model. The output of this model is duty cycle for driving the converter. The experiment implemented using Arduino Uno board. This diagram is compared to MPPT using MLPNN and P&O control (Perturbation and Observation control). The experimental results show that the proposed MLPNN based approach is more efficiency than P&O algorithm for this application.

Keywords: maximum power point tracking, multilayer perceptron netural network, optimal duty cycle, DC generator

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Authors: Ruchi Makani, B. V. R. Reddy

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Mobile Adhoc Networks (MANETs) are gaining popularity due to their potential of providing low-cost mobile connectivity solutions to real-world communication problems. Integrating Intrusion Detection Systems (IDS) in MANETs is a tedious task by reason of its distinctive features such as dynamic topology, de-centralized authority and highly controlled/limited resource environment. IDS primarily use automated soft-computing techniques to monitor the inflow/outflow of traffic packets in a given network to detect intrusion. Use of machine learning techniques in IDS enables system to make decisions on intrusion while continuous keep learning about their dynamic environment. An appropriate IDS model is essential to be selected to expedite this application challenges. Thus, this paper focused on fuzzy-logic based machine learning IDS technique for MANETs and presented their applicability for achieving effectiveness in identifying the intrusions. Further, the selection of appropriate protocol attributes and fuzzy rules generation plays significant role for accuracy of the fuzzy-logic based IDS, have been discussed. This paper also presents the critical attributes of MANET’s routing protocol and its applicability in fuzzy logic based IDS.

Keywords: AODV, mobile adhoc networks, intrusion detection, anomaly detection, fuzzy logic, fuzzy membership function, fuzzy inference system

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Authors: Souhir Ben Amor, Heni Boubaker, Lotfi Belkacem

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This aims of this paper is to forecast the electricity spot prices. First, we focus on modeling the conditional mean of the series so we adopt a generalized fractional -factor Gegenbauer process (k-factor GARMA). Secondly, the residual from the -factor GARMA model has used as a proxy for the conditional variance; these residuals were predicted using two different approaches. In the first approach, a local linear wavelet neural network model (LLWNN) has developed to predict the conditional variance using the Back Propagation learning algorithms. In the second approach, the Gegenbauer generalized autoregressive conditional heteroscedasticity process (G-GARCH) has adopted, and the parameters of the k-factor GARMA-G-GARCH model has estimated using the wavelet methodology based on the discrete wavelet packet transform (DWPT) approach. The empirical results have shown that the k-factor GARMA-G-GARCH model outperform the hybrid k-factor GARMA-LLWNN model, and find it is more appropriate for forecasts.

Keywords: electricity price, k-factor GARMA, LLWNN, G-GARCH, forecasting

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Authors: Elias Akoury

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Nuclear Magnetic Resonance (NMR) spectroscopy is an indispensable technique used in structure determination of small and macromolecules to study their physical properties, elucidation of characteristic interactions, dynamics and thermodynamic processes. Quantum mechanics defines the theoretical description of NMR spectroscopy and treatment of the dynamics of nuclear spin systems. The phenomenon of residual dipolar coupling (RDCs) has become a routine tool for accurate structure determination by providing global orientation information of magnetic dipole-dipole interaction vectors within a common reference frame. This offers accessibility of distance-independent angular information and insights to local relaxation. The measurement of RDCs requires an anisotropic orientation medium for the molecules to partially align along the magnetic field. This can be achieved by introduction of liquid crystals or attaching a paramagnetic center. Although anisotropic paramagnetic tags continue to mark achievements in the biomolecular NMR of large proteins, its application in small organic molecules remains unspread. Here, we propose a strategy for the synthesis of a lanthanide tag and the measurement of RDCs in organic molecules using paramagnetic lanthanide complexes.

Keywords: lanthanide tags, NMR spectroscopy, residual dipolar coupling, quantum mechanics of spin dynamics

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: Mansoureh Naderipour, Susan Bastani, Mohammad Fazel Zarandi

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Predicting links that may occur in the future and missing links in social networks is an attractive problem in social network analysis. Granular computing can help us to model the relationships between human-based system and social sciences in this field. In this paper, we present a model based on granular computing approach and Type-2 fuzzy logic to predict links regarding nodes’ activity and the relationship between two nodes. Our model is tested on collaboration networks. It is found that the accuracy of prediction is significantly higher than the Type-1 fuzzy and crisp approach.

Keywords: social network, link prediction, granular computing, type-2 fuzzy sets

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Authors: Leah Wright, Trevor Townsend

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Mode choice models are crucial instruments in the analysis of travel behaviour. These models show the relationship between an individual’s choice of transportation mode for a given O-D pair and the individual’s socioeconomic characteristics such as household size and income level, age and/or gender, and the features of the transportation system. The most popular functional forms of these models are based on Utility-Based Choice Theory, which addresses the uncertainty in the decision-making process with the use of an error term. However, with the development of artificial intelligence, many researchers have started to take a different approach to travel demand modelling. In recent times, researchers have looked at using neural networks, fuzzy logic and rough set theory to develop improved mode choice formulas. The concept of cloud theory has recently been introduced to model decision-making under uncertainty. Unlike the previously mentioned theories, cloud theory recognises a relationship between randomness and fuzziness, two of the most common types of uncertainty. This research aims to investigate the use of cloud theory in mode choice models. This paper highlights the conceptual framework of the mode choice model using cloud theory. Merging decision-making under uncertainty and mode choice models is state of the art. The cloud theory model is expected to address the issues and concerns with the nested logit and improve the design of mode choice models and their use in travel demand.

Keywords: Cloud theory, decision-making, mode choice models, travel behaviour, uncertainty

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Authors: I. Alhajri, G. Zahedi, R. Alazmi, A. Akbari

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In this paper, an Artificial Neural Network (ANN) was developed to predict Asphaltene Precipitation (AP) during the injection of carbon dioxide into crude oil reservoirs. In this study, the experimental data from six different oil fields were collected. Seventy percent of the data was used to develop the ANN model, and different ANN architectures were examined. A network with the Trainlm training algorithm was found to be the best network to estimate the AP. To check the validity of the proposed model, the model was used to predict the AP for the thirty percent of the data that was unevaluated. The Mean Square Error (MSE) of the prediction was 0.0018, which confirms the excellent prediction capability of the proposed model. In the second part of this study, the ANN model predictions were compared with modified Hirschberg model predictions. The ANN was found to provide more accurate estimates compared to the modified Hirschberg model. Finally, the proposed model was employed to examine the effect of different operating parameters during gas injection on the AP. It was found that the AP is mostly sensitive to the reservoir temperature. Furthermore, the carbon dioxide concentration in liquid phase increases the AP.

Keywords: artificial neural network, asphaltene, CO2 injection, Hirschberg model, oil reservoirs

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Authors: Sumaya Iqbal, Aijaz Ahmad Reshi

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The advances in sensor and embedded technologies have led to rapid developments in Wireless Sensor Networks (WSNs). Presently researchers focus on the integration of WSNs to Internet for their pervasive availability to access these network resources as the interoperable subsystems. The recent computing technologies like cloud computing has made the resource sharing as a converged infrastructure with required service interfaces for the shared resources over the Internet. This paper presents application architecture for wireless Sensor and Actuator Networks (WSANS) following web of things, which allows easy integration of each node to the Internet in order to provide them web accessibility. The architecture enables the sensors and actuator nodes accessed and controlled using cloud interface on WWW. The application architecture was implemented using existing web and its emerging technologies. In particular Representational State Transfer protocol (REST) was extended for the specific requirements of the application. Cloud computing environment has been used as a development platform for the application to assess the possibility of integrating the WSAN nodes to Cloud services. The mushroom farm environment monitoring and control using WSANs has been taken as a research use case.

Keywords: WSAN, REST, web of things, ZigBee, cloud interface, PaaS, sensor gateway

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Authors: Abbas Ahmadi, Bijan Raie, Mohammad Reza Neyshabouri, Mohammad Ali Ghorbani, Farrokh Asadzadeh

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In recent years, vast areas of Urmia Lake in Dasht-e-Tabriz has dried up leading to saline sediments exposure on the surface lake coastal areas being highly susceptible to wind erosion. This study was conducted to investigate wind erosion and its relevance to soil physicochemical properties and also modeling of wind erodibility (WE) using artificial intelligence techniques. For this purpose, 96 soil samples were collected from 0-5 cm depth in 414000 hectares using stratified random sampling method. To measure the WE, all samples (<8 mm) were exposed to 5 different wind velocities (9.5, 11, 12.5, 14.1 and 15 m s-1 at the height of 20 cm) in wind tunnel and its relationship with soil physicochemical properties was evaluated. According to the results, WE varied within the range of 76.69-9.98 (g m-2 min-1)/(m s-1) with a mean of 10.21 and coefficient of variation of 94.5% showing a relatively high variation in the studied area. WE was significantly (P<0.01) affected by soil physical properties, including mean weight diameter, erodible fraction (secondary particles smaller than 0.85 mm) and percentage of the secondary particle size classes 2-4.75, 1.7-2 and 0.1-0.25 mm. Results showed that the mean weight diameter, erodible fraction and percentage of size class 0.1-0.25 mm demonstrated stronger relationship with WE (coefficients of determination were 0.69, 0.67 and 0.68, respectively). This study also compared efficiency of multiple linear regression (MLR), gene expression programming (GEP), artificial neural network (MLP), artificial neural network based on genetic algorithm (MLP-GA) and artificial neural network based on whale optimization algorithm (MLP-WOA) in predicting of soil wind erodibility in Dasht-e-Tabriz. Among 32 measured soil variable, percentages of fine sand, size classes of 1.7-2.0 and 0.1-0.25 mm (secondary particles) and organic carbon were selected as the model inputs by step-wise regression. Findings showed MLP-WOA as the most powerful artificial intelligence techniques (R2=0.87, NSE=0.87, ME=0.11 and RMSE=2.9) to predict soil wind erodibility in the study area; followed by MLP-GA, MLP, GEP and MLR and the difference between these methods were significant according to the MGN test. Based on the above finding MLP-WOA may be used as a promising method to predict soil wind erodibility in the study area.

Keywords: wind erosion, erodible fraction, gene expression programming, artificial neural network

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Authors: Mahmoud M. Othman, Y. G. Hegazy, A. Y. Abdelaziz

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This paper proposes a novel heuristic algorithm that aims to determine the best size and location of distributed generators in unbalanced distribution networks. The proposed heuristic algorithm can deal with the planning cases where power loss is to be optimized without violating the system practical constraints. The distributed generation units in the proposed algorithm is modeled as voltage controlled node with the flexibility to be converted to constant power factor node in case of reactive power limit violation. The proposed algorithm is implemented in MATLAB and tested on the IEEE 37 -node feeder. The results obtained show the effectiveness of the proposed algorithm.

Keywords: distributed generation, heuristic approach, optimization, planning

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Authors: Amir Farouk Ahmed Ali Hussin

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Social media, such as Facebook, LinkedIn and Ex-Twitter have experienced exponential growth and a remarkable adoption rate in recent years. They give fantastic means of online social interactions and communications with family, friends, and colleagues from around the corner or across the globe, and they have become an important part of daily digital interactions for more than one and a half billion users around the world. The personal information sharing practices that social network providers encourage have led to their success as innovative social interaction platforms. Moreover, these practices have outcome in concerns with respect to privacy and security from different stakeholders. Guiding these privacy and security concerns in social networks is a must for these networks to be sustainable. Real security and privacy tools may not be enough to address existing concerns. Some points should be followed to protect users from the existing risks. In this research, we have checked the various privacy and security issues and concerns pertaining to social media. However, we have classified these privacy and security issues and presented a thorough discussion of the effects of these issues and concerns on the future of the social networks. In addition, we have presented a set of points as precaution measures that users can consider to address these issues.

Keywords: international legal, consultation mix, legal research, small and medium-sized enterprises, strategic International law, strategy alignment, house of laws, deployment, production strategy, legal strategy, business strategy

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Authors: Mrinmoy Majumder, Apu Kumar Saha

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The ever-increasing human population and its demand for energy is placing stress upon conventional energy sources; and as demand for power continues to outstrip supply, the need to optimize energy distribution and utilization is emerging as an important focus for various stakeholders. The distribution of available energy must be achieved in such a way that the needs of the consumer are satisfied. However, if the availability of resources is not sufficient to satisfy consumer demand, it is necessary to find a method to select consumers based on factors such as their socio-economic or environmental impacts. Weighting consumer types in this way can help separate them based on their relative importance, and cognitive optimization of the allocation process can then be carried out so that, even on days of particularly scarce supply, the socio-economic impacts of not satisfying the needs of consumers can be minimized. In this context, the present study utilized fuzzy logic to assign weightage to different types of consumers based at an educational campus in India, and then established optimal allocation by applying the non-linear mapping capability of neuro-genetic algorithms. The outputs of the algorithms were compared with similar outputs from particle swarm optimization and differential evolution algorithms. The results of the study demonstrate an option for the optimal utilization of available energy based on the socio-economic importance of consumers.

Keywords: power allocation, optimization problem, neural networks, environmental and ecological engineering

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Authors: Mazarine Roquet, Pierre Dewallef

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The building sector, both residential and tertiary, accounts for a significant share of greenhouse gas emissions. In Belgium, partly due to poor insulation of the building stock, but certainly because of the massive use of fossil fuels for heating buildings, this share reaches almost 30%. To reduce carbon emissions from urban building heating, district heating networks emerge as a promising solution as they offer various assets such as improving the load factor, integrating combined heat and power systems, and enabling energy source diversification, including renewable sources and waste heat recovery. However, mainly for sake of simple operation, most existing district heating networks still operate at high or medium temperatures ranging between 120°C and 60°C (the socalled second and third-generations district heating networks). Although these district heating networks offer energy savings in comparison with individual boilers, such temperature levels generally require the use of fossil fuels (mainly natural gas) with combined heat and power. The fourth-generation district heating networks improve the transport and energy conversion efficiency by decreasing the operating temperature between 50°C and 30°C. Yet, to decarbonise the building heating one must increase the waste heat recovery and use mainly wind, solar or geothermal sources for the remaining heat supply. Fifth-generation networks operating between 35°C and 15°C offer the possibility to decrease even more the transport losses, to increase the share of waste heat recovery and to use electricity from renewable resources through the use of heat pumps to generate low temperature heat. The main objective of this contribution is to exhibit on a real-life test case the benefits of replacing an existing third-generation network by a fifth-generation one and to decarbonise the heat supply of the building stock. The second objective of the study is to highlight the difficulties resulting from the use of a fifth-generation, low-temperature, district heating network. To do so, a simulation model of the district heating network including its regulation is implemented in the modelling language Modelica. This model is applied to the test case of the heating network on the University of Liège's Sart Tilman campus, consisting of around sixty buildings. This model is validated with monitoring data and then adapted for low-temperature networks. A comparison of primary energy consumptions as well as CO2 emissions is done between the two cases to underline the benefits in term of energy independency and GHG emissions. To highlight the complexity of operating a lowtemperature network, the difficulty of adapting the mass flow rate to the heat demand is considered. This shows the difficult balance between the thermal comfort and the electrical consumption of the circulation pumps. Several control strategies are considered and compared to the global energy savings. The developed model can be used to assess the potential for energy and CO2 emissions savings retrofitting an existing network or when designing a new one.

Keywords: building simulation, fifth-generation district heating network, low-temperature district heating network, urban building heating

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Authors: Armin Nabaei, M. Omair Ahmad, M. N. S. Swamy

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Many facial expression and emotion recognition methods in the traditional approaches of using LDA, PCA, and EBGM have been proposed. In recent years deep learning models have provided a unique platform addressing by automatically extracting the features for the detection of facial expression and emotions. However, deep networks require large training datasets to extract automatic features effectively. In this work, we propose an efficient emotion detection algorithm using face images when only small datasets are available for training. We design a deep network whose feature extraction capability is enhanced by utilizing several parallel modules between the input and output of the network, each focusing on the extraction of different types of coarse features with fined grained details to break the symmetry of produced information. In fact, we leverage long range dependencies, which is one of the main drawback of CNNs. We develop this work by introducing a Dynamic Soft-Margin SoftMax.The conventional SoftMax suffers from reaching to gold labels very soon, which take the model to over-fitting. Because it’s not able to determine adequately discriminant feature vectors for some variant class labels. We reduced the risk of over-fitting by using a dynamic shape of input tensor instead of static in SoftMax layer with specifying a desired Soft- Margin. In fact, it acts as a controller to how hard the model should work to push dissimilar embedding vectors apart. For the proposed Categorical Loss, by the objective of compacting the same class labels and separating different class labels in the normalized log domain.We select penalty for those predictions with high divergence from ground-truth labels.So, we shorten correct feature vectors and enlarge false prediction tensors, it means we assign more weights for those classes with conjunction to each other (namely, “hard labels to learn”). By doing this work, we constrain the model to generate more discriminate feature vectors for variant class labels. Finally, for the proposed optimizer, our focus is on solving weak convergence of Adam optimizer for a non-convex problem. Our noteworthy optimizer is working by an alternative updating gradient procedure with an exponential weighted moving average function for faster convergence and exploiting a weight decay method to help drastically reducing the learning rate near optima to reach the dominant local minimum. We demonstrate the superiority of our proposed work by surpassing the first rank of three widely used Facial Expression Recognition datasets with 93.30% on FER-2013, and 16% improvement compare to the first rank after 10 years, reaching to 90.73% on RAF-DB, and 100% k-fold average accuracy for CK+ dataset, and shown to provide a top performance to that provided by other networks, which require much larger training datasets.

Keywords: computer vision, facial expression recognition, machine learning, algorithms, depp learning, neural networks

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Authors: Tom Nakotte, Hongmei Luo

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Lead chalcogenide-based (PbS, PbSe, and PbTe) quantum dots (QDs) were synthesized for the purpose of implementing them in radiation detectors. Pb based materials have long been of interest for gamma and x-ray detection due to its high absorption cross section and Z number. The emphasis of the studies was on exploring how to control charge carrier transport within thin films containing the QDs. The properties of QDs itself can be altered by changing the size, shape, composition, and surface chemistry of the dots, while the properties of carrier transport within QD films are affected by post-deposition treatment of the films. The QDs were synthesized using colloidal synthesis methods and films were grown using multiple film coating techniques, such as spin coating and doctor blading. Current QD radiation detectors are based on the QD acting as fluorophores in a scintillation detector. Here the viability of using QDs in solid-state radiation detectors, for which the incident detectable radiation causes a direct electronic response within the QD film is explored. Achieving high sensitivity and accurate energy quantification in QD radiation detectors requires a large carrier mobility and diffusion lengths in the QD films. Pb chalcogenides-based QDs were synthesized with both traditional oleic acid ligands as well as more weakly binding oleylamine ligands, allowing for in-solution ligand exchange making the deposition of thick films in a single step possible. The PbS and PbSe QDs showed better air stability than PbTe. After precipitation the QDs passivated with the shorter ligand are dispersed in 2,6-difloupyridine resulting in colloidal solutions with concentrations anywhere from 10-100 mg/mL for film processing applications, More concentrated colloidal solutions produce thicker films during spin-coating, while an extremely concentrated solution (100 mg/mL) can be used to produce several micrometer thick films using doctor blading. Film thicknesses of micrometer or even millimeters are needed for radiation detector for high-energy gamma rays, which are of interest for astrophysics or nuclear security, in order to provide sufficient stopping power.

Keywords: colloidal synthesis, lead chalcogenide, radiation detectors, quantum dots

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Authors: Shima Soltanzadeh, Mohammad Hosain Fazel Zarandi, Mojtaba Barzegar Astanjin

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Diagnosis of male infertility by the laboratory tests is expensive and, sometimes it is intolerable for patients. Filling out the questionnaire and then using classification method can be the first step in decision-making process, so only in the cases with a high probability of infertility we can use the laboratory tests. In this paper, we evaluated the performance of four classification methods including naive Bayesian, neural network, logistic regression and fuzzy c-means clustering as a classification, in the diagnosis of male infertility due to environmental factors. Since the data are unbalanced, the ROC curves are most suitable method for the comparison. In this paper, we also have selected the more important features using a filtering method and examined the impact of this feature reduction on the performance of each methods; generally, most of the methods had better performance after applying the filter. We have showed that using fuzzy c-means clustering as a classification has a good performance according to the ROC curves and its performance is comparable to other classification methods like logistic regression.

Keywords: classification, fuzzy c-means, logistic regression, Naive Bayesian, neural network, ROC curve

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Authors: Mahshid Arabi

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With the rapid expansion of digital technologies and the internet, information security has become a critical priority for organizations and individuals. The widespread use of digital tools such as smartphones and internet networks facilitates the storage of vast amounts of data, but simultaneously, vulnerabilities and security threats have significantly increased. The aim of this study is to examine and analyze modern methods of information security management and to develop a comprehensive model to counteract threats and information misuse. This study employs a mixed-methods approach, including both qualitative and quantitative analyses. Initially, a systematic review of previous articles and research in the field of information security was conducted. Then, using the Delphi method, interviews with 30 information security experts were conducted to gather their insights on security challenges and solutions. Based on the results of these interviews, a comprehensive model for information security management was developed. The proposed model includes advanced encryption techniques, machine learning-based intrusion detection systems, and network security protocols. AES and RSA encryption algorithms were used for data protection, and machine learning models such as Random Forest and Neural Networks were utilized for intrusion detection. Statistical analyses were performed using SPSS software. To evaluate the effectiveness of the proposed model, T-Test and ANOVA statistical tests were employed, and results were measured using accuracy, sensitivity, and specificity indicators of the models. Additionally, multiple regression analysis was conducted to examine the impact of various variables on information security. The findings of this study indicate that the comprehensive proposed model reduced cyber-attacks by an average of 85%. Statistical analysis showed that the combined use of encryption techniques and intrusion detection systems significantly improves information security. Based on the obtained results, it is recommended that organizations continuously update their information security systems and use a combination of multiple security methods to protect their data. Additionally, educating employees and raising public awareness about information security can serve as an effective tool in reducing security risks. This research demonstrates that effective and up-to-date information security management requires a comprehensive and coordinated approach, including the development and implementation of advanced techniques and continuous training of human resources.

Keywords: data protection, digital technologies, information security, modern management

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Authors: Mehmet Erdi Korkmaz, Mustafa Günay

Abstract:

Nowadays, grinding is frequently replaced with hard turning for reducing set up time and higher accuracy. This paper focused on mathematical modeling of average surface roughness (Ra) in hard turning of AISI L2 grade (DIN 1.2210) cold work tool steel with ceramic tools. The steel was hardened to 60±1 HRC after the heat treatment process. Cutting speed, feed rate, depth of cut and tool nose radius was chosen as the cutting conditions. The uncoated ceramic cutting tools were used in the machining experiments. The machining experiments were performed according to Taguchi L27 orthogonal array on CNC lathe. Ra values were calculated by averaging three roughness values obtained from three different points of machined surface. The influences of cutting conditions on surface roughness were evaluated as statistical and experimental. The analysis of variance (ANOVA) with 95% confidence level was applied for statistical analysis of experimental results. Finally, mathematical models were developed using the artificial neural networks (ANN). ANOVA results show that feed rate is the dominant factor affecting surface roughness, followed by tool nose radius and cutting speed.

Keywords: ANN, hard turning, DIN 1.2210, surface roughness, Taguchi method

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Authors: Vijaya Yuvaram Singh V M, Kameshwar Rao J V

Abstract:

The challenge with development of neural network based methods for medical is the availability of data. Anatomical landmark detection in the medical domain is a process to find points on the x-ray scan report of the patient. Most of the time this task is done manually by trained professionals as it requires precision and domain knowledge. Traditionally object detection based methods are used for landmark detection. Here, we utilize reinforcement learning and query based method to train a single agent capable of detecting multiple landmarks. A deep Q network agent is trained to detect single and multiple landmarks present on hip and shoulder from x-ray scan of a patient. Here a single agent is trained to find multiple landmark making it superior to having individual agents per landmark. For the initial study, five images of different patients are used as the environment and tested the agents performance on two unseen images.

Keywords: reinforcement learning, medical landmark detection, multi target detection, deep neural network

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Authors: Mannan Malik Muhammad Naeem, Jeong Myung Yung

Abstract:

Functional near infrared spectroscopy (fNIRS) has a favorable position in non-invasive brain imaging techniques. The concentration change of oxygenated hemoglobin and de-oxygenated hemoglobin during particular cognitive activity is the basis for this neuro-imaging modality. Two wavelengths of near-infrared light can be used with modified Beer-Lambert law to explain the indirect status of neuronal activity inside brain. The temporal resolution of fNIRS is very good for real-time brain computer-interface applications. The portability, low cost and an acceptable temporal resolution of fNIRS put it on a better position in neuro-imaging modalities. In this study, an optimization model for impulse response function has been used to estimate/predict initial dip using fNIRS data. In addition, the activity strength parameter related to motor based cognitive task has been analyzed. We found an initial dip that remains around 200-300 millisecond and better localize neural activity.

Keywords: fNIRS, brain-computer interface, optimization algorithm, adaptive signal processing

Procedia PDF Downloads 215

Authors: Xiaoqi Jia, Qing Zhu, Hao Zhang, Su Yang

Abstract:

A gender recognition system is able to tell the gender of the given person through a few of frontal facial images. An effective gender recognition approach enables to improve the performance of many other applications, including security monitoring, human-computer interaction, image or video retrieval and so on. In this paper, we present an effective method for gender classification task in frontal facial images based on deep belief networks (DBNs), which can pre-train model and improve accuracy a little bit. Our experiments have shown that the pre-training method with DBNs for gender classification task is feasible and achieves a little improvement of accuracy on FERET and CAS-PEAL-R1 facial datasets.

Keywords: gender recognition, beep belief net-works, semi-supervised learning, greedy-layer wise RBMs

Procedia PDF Downloads 442

Authors: Swapnil Singh, Sanjoy Das

Abstract:

Mobile Ad-Hoc Network (MANET) is infrastructure less networks dynamically formed by autonomous system of mobile nodes that are connected via wireless links. Mobile nodes communicate with each other on the fly. In this network each node also acts as a router. The battery power and the bandwidth are very scarce resources in this network. The network lifetime and connectivity of nodes depends on battery power. Therefore, energy is a valuable constraint which should be efficiently used. In this paper, we survey various energy efficient routing protocol. The energy efficient routing protocols are classified on the basis of approaches they use to minimize the energy consumption. The purpose of this paper is to facilitate the research work and combine the existing solution and to develop a more energy efficient routing mechanism.

Keywords: delaunay triangulation, deployment, energy efficiency, MANET

Procedia PDF Downloads 607

Authors: Katie Kilgour, Brendan Turner, Carly Catella, Michael Daniele, Stefano Menegatti

Abstract:

In vivo, the extracellular matrix (ECM) provides biochemical and mechanical properties that are instructional to resident cells to form complex tissues with characteristics to develop and support vascular networks. In vitro, the development of vascular networks can be guided by biochemical patterning of substrates via spatial distribution and display of peptides and growth factors to prompt cell adhesion, differentiation, and proliferation. We have developed a technique utilizing peptide ligands that specifically bind vascular endothelial growth factor (VEGF), erythropoietin (EPO), or angiopoietin-1 (ANG1) to spatiotemporally distribute growth factors to cells. This allows for the controlled release of each growth factor, ultimately enhancing the formation of a vascular network. Our engineered tissue constructs (ETCs) are fabricated out of gelatin methacryloyl (GelMA), which is an ideal substrate for tailored stiffness and bio-functionality, and covalently patterned with growth factor specific peptides. These peptides mimic growth factor receptors, facilitating the non-covalent binding of the growth factors to the ETC, allowing for facile uptake by the cells. We have demonstrated in the absence of cells the binding affinity of VEGF, EPO, and ANG1 to their respective peptides and the ability for each to be patterned onto a GelMA substrate. The ability to organize growth factors on an ETC provides different functionality to develop organized vascular networks. Our results demonstrated a method to incorporate biochemical cues into ETCs that enable spatial and temporal control of growth factors. Future efforts will investigate the cellular response by evaluating gene expression, quantifying angiogenic activity, and measuring the speed of growth factor consumption.

Keywords: growth factor, hydrogel, peptide, angiogenesis, vascular, patterning

Procedia PDF Downloads 155

Authors: Tahsin Shameem, Chowdhury Sadman Jahan, Mohammad Alam

Abstract:

Interest about Net Zero Energy Buildings have gained traction in recent years following the need to sustain energy consumption with generations on site and to reduce dependence on grid supplied energy from large plants using fossil fuel. With this end in view, building integrated photovoltaics are being studied attempting to utilize all exterior facades of a building to generate power. In this paper, we have looked at the physical parameters defining a dye sensitized solar cell (DSSC) and discussed their impact on energy harvest. Following our discussion and experimental data obtained from literature, we have attempted to optimize these physical parameters accordingly so as to allow maximum light absorption for a given active layer thickness. We then modified a planer DSSC design with our optimized properties to allow adequate light transmission which demonstrated a high fill factor and an External Quantum Efficiency (EQE) of greater than 9% by computer aided design and simulation. In conclusion, a DSSC based solar window with such high output values even after such high light transmission through it definitely flags a promising future for this technology and our work elicits the need for further study and practical experimentation.

Keywords: net zero energy building, integrated photovoltaics, dye sensitized solar cell, fill factor, External Quantum Efficiency

Procedia PDF Downloads 134