Search results for: curve feature

Authors: Amira M. Attia, Karim H. Youssef, Nabil H. Abbasy

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Energy consumption schedule (ECS) technique shifts usage of loads from on peak hours and redistributes them throughout the day according to residents’ operating time preferences. This technique is used as form of indirect control from utility to improve the load curve and hence its load factor and reduce customer’s total electric bill as well. Similarly, load shifting technique achieves ECS purposes but as direct control form applied from utility. In this paper, ECS is simulated twice as optimal constrained mathematical formula, solved by using CVX program in MATLAB^® R2013b. First, it is utilized for single residential building with ten apartments to determine max allowable energy consumption per hour for each residential apartment. Then, it is used for single apartment with number of shiftable domestic devices, where operating schedule is deduced using previous simulation output results as constraints. The paper ends by giving differences between ECS technique and load shifting technique via literature and simulation. Based on results assessment, it will be shown whether using ECS or load shifting is more beneficial to both customer and utility.

Keywords: energy consumption schedule, load shifting, comparison, demand side mangement

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Authors: Chen Wang, Chun Liang

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Microsatellite instability (MSI) is characterized by high degree of polymorphism in microsatellite (MS) length due to a deficiency in mismatch repair (MMR) system. MSI is associated with several tumor types and its status can be considered as an important indicator for tumor prognostic. Conventional clinical diagnosis of MSI examines PCR products of a panel of MS markers using electrophoresis (MSI-PCR) which is laborious, time consuming, and less reliable. MSIpred, a python 2 package for automatic classification of MSI was released by this study. It computes important somatic mutation features from files in mutation annotation format (MAF) generated from paired tumor-normal exome sequencing data, subsequently using these to predict tumor MSI status with a support vector machine (SVM) classifier trained by MAF files of 1074 tumors belonging to four types. Evaluation of MSIpred on an independent 358-tumor test set achieved overall accuracy of over 98% and area under receiver operating characteristic (ROC) curve of 0.967. These results indicated that MSIpred is a robust pan-cancer MSI classification tool and can serve as a complementary diagnostic to MSI-PCR in MSI diagnosis.

Keywords: microsatellite instability, pan-cancer classification, somatic mutation, support vector machine

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Authors: Pierre Kancir, Jean-Philippe Diguet, Marc Sevaux

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One of the main difficulties in developing multi-robot systems (MRS) is related to the simulation and testing tools available. Indeed, if the differences between simulations and real robots are too significant, the transition from the simulation to the robot won’t be possible without another long development phase and won’t permit to validate the simulation. Moreover, the testing of different algorithmic solutions or modifications of robots requires a strong knowledge of current tools and a significant development time. Therefore, the availability of tools for MRS, mainly with flying drones, is crucial to enable the industrial emergence of these systems. This research aims to present the most commonly used tools for MRS simulations and their main shortcomings and presents complementary tools to improve the productivity of designers in the development of multi-vehicle solutions focused on a fast learning curve and rapid transition from simulations to real usage. The proposed contributions are based on existing open source tools as Gazebo simulator combined with ROS (Robot Operating System) and the open-source multi-platform autopilot ArduPilot to bring them to a broad audience.

Keywords: ROS, ArduPilot, MRS, simulation, drones, Gazebo

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Authors: Yu Li, Dongyao Wang, Xiaobao Sun, Wei Ni

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In long-term evolution (LTE), the carriers around 5GHz are planned to be utilized without licenses to further enlarge system capacity. This feature is termed licensed assisted access (LAA). The channel sensing (clean channel assessment, CCA) is required before any transmission on these unlicensed carriers, in order to make sure the harmonious co-existence of LAA with other radio access technology in the unlicensed band. Obviously, the CCA threshold is very critical, which decides whether the transmission right following CCA is delivered in time and without collisions. An improper CCA threshold may cause buffer overflow of some eNodeBs if the eNodeBs are heavily loaded with the traffic. Thus, to solve these problems, we propose an adaptive threshold adjustment method for CCA in the LAA downlink. Both the load and transmission opportunities are concerned. The trend of the LAA throughput as the threshold varies is obtained, which guides the threshold adjustment. The co-existing between LAA and Wi-Fi is particularly tested. The results from system-level simulation confirm the merits of our design, especially in heavy traffic cases.

Keywords: LTE, LAA, CCA, threshold adjustment

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Authors: Hanbhin Ryu, Ilkwon Yun

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Automotive industry targets to provide an improvement in safety and convenience through realizing fully autonomous vehicle. For partially realizing fully automated driving, Current vehicles already feature varieties of advanced driver assistance system (ADAS) for safety and infotainment systems for the driver’s convenience. This paper presents Cloud Connected Vehicle (CCV) which connected vehicles with cloud data center via the access network to control the vehicle for achieving next autonomous driving form and describes its features. This paper also describes the shortcoming of the existing In-Vehicle Network (IVN) to be a next generation IVN of CCV and organize the 802.3 Ethernet, the next generation of IVN, related research issue to verify the feasibility of using Ethernet. At last, this paper refers to additional considerations to adopting Ethernet-based IVN for CCV.

Keywords: autonomous vehicle, cloud connected vehicle, ethernet, in-vehicle network

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Authors: Arslan Usman, Abdul Sattar, Hamid Latif, Afshan Ashfaq, Muhammad Rafique, Martin Koch

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Two-dimensional materials have promising applications in optoelectronic and photonics; MoS₂ is the pioneer 2D material in the family of transition metal dichalcogenides. Its optical, optoelectronic, and structural properties are of practical importance along with its exciton dynamics. Exciton, along with exciton complexes, plays a vital role in realizing quantum devices. MoS₂ monolayers were synthesized using chemical vapour deposition (CVD) technique on SiO₂ and hBN substrates. Photoluminescence spectroscopy (PL) was used to identify the monolayer, which also reflects the substrate based peak broadening due to screening effects. In-plane and out of plane characteristic vibrational modes E¹₂g and A₁g, respectively, were detected in a different configuration on the substrate. The B-excitons and trions showed a dominant feature at low temperatures due to electron-phonon coupling effects, whereas their energies are separated by 100 meV.

Keywords: 2D materials, photoluminescence, AFM, excitons

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Authors: Rahma Abed, Sahbi Bahroun, Ezzeddine Zagrouba

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Due to the huge amount of data in videos, extracting the relevant frames became a necessity and an essential step prior to performing face recognition. In this context, we propose a method for extracting keyframes from videos based on face quality and deep learning for a face recognition task. This method has two steps. We start by generating face quality scores for each face image based on the use of three face feature extractors, including Gabor, LBP, and HOG. The second step consists in training a Deep Convolutional Neural Network in a supervised manner in order to select the frames that have the best face quality. The obtained results show the effectiveness of the proposed method compared to the methods of the state of the art.

Keywords: keyframe extraction, face quality assessment, face in video recognition, convolution neural network

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Authors: Revytia Tanera

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This research is a pilot study to examine the rise of selfie trend in dealing with individual self representation and identity building in social media. The symbolic interactionism theory is used as the concept of the desired self image, and Cooley’s looking glass-self concept is used to analyze the mechanical reflection of ourselves; how do people perform their “digital self” in social media. In-depth interviews were conducted in the study with a non-random sample who owns a smartphone with a front camera feature and are active in social media. This research is trying to find out whether the selfie trend brings any influence on identity building on each individual. Through analysis of interview results, it can be concluded that people take selfie photos in order to express themselves and to boost their confidence. This study suggests a follow up and more in depth analysis on identity and self representation from various age groups.

Keywords: self representation, selfie, social media, symbolic interaction, looking glass-self

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Authors: Agâh Tuğrul Korucu, Ismail Fatih Yavuzaslan, Lale Toraman

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Nowadays, thanks to development of technology, integration of technology into teaching and learning activities is spreading. Increasing technological literacy which is one of the expected competencies for individuals of 21st century is associated with the effective use of technology in education. The most important factor in effective use of technology in education institutions is ICT teachers. The concept of computer assisted instruction (CAI) refers to the utilization of information and communication technology as a tool aided teachers in order to make education more efficient and improve its quality in the process of educational. Teachers can use computers in different places and times according to owned hardware and software facilities and characteristics of the subject and student in CAI. Analyzing teachers’ use of computers in education is significant because teachers are the ones who manage the course and they are the most important element in comprehending the topic by students. To accomplish computer-assisted instruction efficiently is possible through having positive attitude of teachers. Determination the level of knowledge, attitude and behavior of teachers who get the professional knowledge from educational faculties and elimination of deficiencies if any are crucial when teachers are at the faculty. Therefore, the aim of this paper is to identify ICT teachers' attitudes toward computer-assisted instruction in terms of different variables. Research group consists of 200 prospective ICT teachers studying at Necmettin Erbakan University Ahmet Keleşoğlu Faculty of Education CEIT department. As data collection tool of the study; “personal information form” developed by the researchers and used to collect demographic data and "the attitude scale related to computer-assisted instruction" are used. The scale consists of 20 items. 10 of these items show positive feature, while 10 of them show negative feature. The Kaiser-Meyer-Olkin (KMO) coefficient of the scale is found 0.88 and Barlett test significance value is found 0.000. The Cronbach’s alpha reliability coefficient of the scale is found 0.93. In order to analyze the data collected by data collection tools computer-based statistical software package used; statistical techniques such as descriptive statistics, t-test, and analysis of variance are utilized. It is determined that the attitudes of prospective instructors towards computers do not differ according to their educational branches. On the other hand, the attitudes of prospective instructors who own computers towards computer-supported education are determined higher than those of the prospective instructors who do not own computers. It is established that the departments of students who previously received computer lessons do not affect this situation so much. The result is that; the computer experience affects the attitude point regarding the computer-supported education positively.

Keywords: computer based instruction, teacher candidate, attitude, technology based instruction, information and communication technologies

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Authors: Rupan Das Chakraborty, Surendra K. Martha

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Hard carbons (HCs) are extensively used as anode materials for sodium-ion batteries due to their availability, low cost, and ease of synthesis. It possesses the ability to store Na ion between stacked sp2 carbon layers and micropores. In this work, hard carbons are synthesized from different concentrations (0.5M to 5M) of dextrose solutions by hydrothermal synthesis followed by high-temperature calcination at 1100 ⁰C in an inert atmosphere. Dextrose has been chosen as a precursor material as it is a eco-friendly and renewable source. Among all hard carbon derived from different concentrations of dextrose solutions, hard carbon derived from 3M dextrose solution delivers superior electrochemical performance compared to other hard carbons. Hard carbon derived from 3M dextrose solution (Dextrose derived Hard Carbon-3M) provides an initial reversible capacity of 257 mAh g-1 with a capacity retention of 83 % at the end of 100 cycles at 30 mA g-1). The carbons obtained from different dextrose concentration show very similar Cyclic Voltammetry and chargedischarging behavior at a scan rate of 0.05 mV s-1 the Cyclic Voltammetry curve indicate that solvent reduction and the solid electrolyte interface (SEI) formation start at E < 1.2 V (vs Na/Na+). Among all 3M dextrose derived electrode indicate as a promising anode material for Sodium-ion batteries (SIBs).

Keywords: dextrose derived hard carbon, anode, sodium-ion battery, electrochemical performance

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Authors: Nabeel Safdar, Qian Aimin

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Corporate philanthropy is important, as the donations have been considered as a source to improve the image of business entity in modern era of high competition. We used data on annual basis from 2000 to 2014 for 1,248 firms listed at Shanghai and Shenzhen stock exchanges. Results for giving firms reveal that there is curve linear relation of profitability and CP, as profitable firms utilize cash in an efficient way and have fewer amounts of slack resource and tradeoff among stakeholder and agency cost made it more justifiable. We found that more profitability does not mean that the cash flows are available, actually good performing firms or profitable firm also good at cash management. Cash is utilized in an effective way by profitable firms, and have fewer extents of slack resources which generate curvilinear relationship of profitability with Corporate Philanthropy. We found that the trend of Corporate Philanthropy also got affected due to natural disasters. Analysis made by innovation, slack resources and directors salary revealed the positive significant relationship. It is not compulsory that firm should be only profitable for engaging in philanthropy rather they should have abundant slack resources to donate.

Keywords: corporate philanthropy, free cash flows, natural disasters, profitability

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Authors: A. J. Shah, G. R. Vesmawala

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Connections perform a fundamental role in the steel structures as global behaviour. In order to evaluate the real influence of the physical and geometrical parameters that control their behaviour, many experimental tests and analysis have been developed but a definitive answer to the problem in question still stands. Here, various configurations of bolts were tried and the resulting moment rotation (M-θ) curves were plotted. The connection configuration is such that two bolts are located above each of the flanges and beside each of the webs. The model considers the combined effects of prying action, the formation of yield lines, and failures due to punching shear and beam section failure. For many types of connections, the stiffness at the service load level falls somewhere in between the fully restrained and simple limits and designers need to account for its behaviour. The (M-θ) curves are generally assumed to be the best characterization of connection behaviour. The moment rotation curves are generally derived from experiments on cantilever type specimens. The moments are calculated directly from the statics of the specimen, while the rotations are measured over a distance typically equal to the point of loading. Thus, this paper establishes the relationship between M-θ behaviour of different types of connections tested and presents the relative strength of various possible arrangements of bolts.

Keywords: bolt, moment, rotation, stiffness, connections

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Authors: Stepan Papacek, Jiri Jablonsky, Radek Kana, Ctirad Matonoha, Stefan Kindermann

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FRAP (Fluorescence Recovery After Photobleaching) is a widely used measurement technique to determine the mobility of fluorescent molecules within living cells. While the experimental setup and protocol for FRAP experiments are usually fixed, data processing part is still under development. In this paper, we formulate and solve the problem of data selection which enhances the processing of FRAP images. We introduce the concept of the irrelevant data set, i.e., the data which are almost not reducing the confidence interval of the estimated parameters and thus could be neglected. Based on sensitivity analysis, we both solve the problem of the optimal data space selection and we find specific conditions for optimizing an important experimental design factor, e.g., the radius of bleach spot. Finally, a theorem announcing less precision of the integrated data approach compared to the full data case is proven; i.e., we claim that the data set represented by the FRAP recovery curve lead to a larger confidence interval compared to the spatio-temporal (full) data.

Keywords: FRAP, inverse problem, parameter identification, sensitivity analysis, optimal experimental design

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Authors: hassan gheisari

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Ferro silicate calcium nano particles are prepared through the sol-gel method using polyvinyl alcohol (PVA) as a chelating agent. The powder, as prepared, is annealed at three different temperatures (900 ºC, 1000 ºC, and 1100 ºC) for 3 h. The XRD patterns of the samples indicate broad peaks, and the full width at half maximum decreased with increasing annealing temperature. FTIR spectra of the samples confirm the presence of metal - oxygen complexes within the structure. The average particle size obtained from PSA curve demonstrates ultrafine particles. SEM micrographs indicate the particles synthesized have spherical morphology. The saturation magnetization (Ms) and remnant magnetization (Mr) of the samples show dependence on particle size and crystallinity of the samples. The highest saturation magnetization is achieved for the sample annealed at 1100 ºC having maximum average particle size. The high saturation magnetization of the samples suggests the present method is suitable for obtaining nano particles magnetic ferro bioceramic, which is desirable for practical applications such as hyperthermia bone cancer therapy.

Keywords: hyperthermia, bone cancer, bio ceramic; magnetic materials; sol– gel, silicate calcium

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Authors: Hassan Gheisari

Abstract:

Ferro silicate calcium nano particles are prepared through the sol-gel method using polyvinyl alcohol (PVA) as a chelating agent. The powder as prepared is annealed at three different temperatures (900 ºC, 1000 ºC and 1100 ºC) for 3 h. The XRD patterns of the samples indicate broad peaks and the full width at half maximum decreased with increasing annealing temperature. FTIR spectra of the samples confirm the presence of metal - oxygen complexes within the structure. The average particle size obtained from PSA curve demonstrates ultrafine particles. SEM micrographs indicate the particles synthesized have spherical morphology. The saturation magnetization (Ms) and remnant magnetization (Mr) of the samples show dependence on particle size and crystallinity of the samples. The highest saturation magnetization is achieved for the sample annealed at 1100 ºC having maximum average particle size. The high saturation magnetization of the samples suggests the present method is suitable for obtaining nano particles magnetic ferro bioceramic which is desirable for practical applications such as hyperthermia bone cancer therapy.

Keywords: hyperthermia, bone cancer, bio ceramic, magnetic materials, sol– gel, silicate calcium

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Authors: S. Shamohammadi, L. Razavi

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This paper based on the principle concepts of SCS-CN model, a new mathematical model for computation of retention potential (S) presented. In the mathematical model, not only precipitation-runoff concepts in SCS-CN model are precisely represented in a mathematical form, but also new concepts, called “maximum retention” and “total retention” is introduced, and concepts of potential retention capacity, maximum retention, and total retention have been separated from each other. In the proposed model, actual retention (F), maximum actual retention (Fmax), total retention (S), maximum retention (Smax), and potential retention (Sp), for the first time clearly defined, so that Sp is not variable, but a function of morphological characteristics of the watershed. Indeed, based on the mathematical relation of the conceptual curve of SCS-CN model, the proposed model provides a new method for the computation of actual retention in watershed and it simply determined runoff based on. In the corresponding relations, in addition to Precipitation (P), Initial retention (Ia), cumulative values of actual retention capacity (F), total retention (S), runoff (Q), antecedent moisture (M), potential retention (Sp), total retention (S), we introduced Fmax and Fmin referring to maximum and minimum actual retention, respectively. As well as, ksh is a coefficient which depends on morphological characteristics of the watershed. Advantages of the modified version versus the original model include a better precision, higher performance, easier calibration and speed computing.

Keywords: model, mathematical, retention, watershed, SCS

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Authors: Innocent C. Ezenwa, Takashi Yoshino

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Knowledge of the transport properties of Fe and its alloys at extreme high pressure (P), temperature (T) conditions are essential for understanding the generation and sustainability of the magnetic field of the rocky planets with a metallic core. Since Pt, an unfilled d-band late transition metal with an electronic structure of Xe4f¹⁴5d⁹6s¹, is paramagnetic and remains close-packed structure at ambient conditions and high P-T, it is expected that its transport properties at these conditions would be similar to those of ε-Fe. We investigated the T-dependent electrical resistivity of solid and liquid Pt up to 8 GPa and found it constant along its melting curve both on the liquid and solid sides in agreement with theoretical prediction and experimental results estimated from thermal conductivity measurements. Our results suggest that the T-dependent resistivity of ε-Fe is linear and would not saturate at high P, T conditions. This, in turn, suggests that the thermal conductivity of liquid Fe at Earth’s core conditions may not be as high as previously suggested by models employing saturation resistivity. Hence, thermal convection could have powered the geodynamo before the birth of the inner core. The electrical resistivity and thermal conductivity on the liquid and solid sides of the inner core boundary of the Earth would be significantly different in values.

Keywords: electrical resistivity, thermal conductivity, transport properties, geodynamo and geomagnetic field

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Authors: Hyun-Koo Kim, Yonghun Kim, Yong-Hoon Kim, Ju Hee Lee, Myungho Song

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In this paper, we have described an efficient stereo matching method and pedestrian detection method using stereo types LWIR camera. We compared with three types stereo camera algorithm as block matching, ELAS, and SGM. For pedestrian detection using stereo LWIR camera, we used that SGM stereo matching method, free space detection method using u/v-disparity, and HOG feature based pedestrian detection. According to testing result, SGM method has better performance than block matching and ELAS algorithm. Combination of SGM, free space detection, and pedestrian detection using HOG features and SVM classification can detect pedestrian of 30m distance and has a distance error about 30 cm.

Keywords: advanced driver assistance system, pedestrian detection, stereo matching method, stereo long-wave IR camera

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Authors: Ehsan Mehryaar, Reza Bushehri

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One of the principal parameters defining the clay soil dynamic response is the strain-shear modulus relation. Predicting the strain and, subsequently, shear modulus reduction of the soil is essential for performance analysis of structures exposed to earthquake and dynamic loadings. Many soil properties affect soil’s dynamic behavior. In order to capture those effects, in this study, a database containing 1193 data points consists of maximum shear modulus, strain, moisture content, initial void ratio, plastic limit, liquid limit, initial confining pressure resulting from dynamic laboratory testing of 21 clays is collected for predicting the shear modulus vs. strain curve of soil. A model based on an extreme gradient boosting technique is proposed. A tree-structured parzan estimator hyper-parameter tuning algorithm is utilized simultaneously to find the best hyper-parameters for the model. The performance of the model is compared to the existing empirical equations using the coefficient of correlation and root mean square error.

Keywords: XGBoost, hyper-parameter tuning, soil shear modulus, dynamic response

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Authors: Gałka Aleksandra, Jelińska Justyna, Masiak Albert, Walentukiewicz Krzysztof

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An offline signature is well-known however not the safest way to verify identity. Nowadays, to ensure proper authentication, i.e. in banking systems, multimodal verification is more widely used. In this paper the online signature analysis based on dynamic time warping (DTW) coupled with machine learning approaches has been presented. In our research signatures made with biometric pens were gathered. Signature features as well as their forgeries have been described. For verification of authenticity various methods were used including convolutional neural networks using DTW matrix and multilayer perceptron using sums of DTW matrix paths. System efficiency has been evaluated on signatures and signature forgeries collected on the same day. Results are presented and discussed in this paper.

Keywords: dynamic time warping, handwritten signature verification, feature-based recognition, online signature

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Authors: Adji Achmad Rinaldo Fernandes, Usriatur Rohma

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Nonparametric path analysis is a statistical method that does not rely on the assumption that the curve is known. The purpose of this study is to determine the best truncated spline nonparametric path function between linear and quadratic polynomial degrees with 1, 2, and 3 knot points and to determine the significance of estimating the best truncated spline nonparametric path function in the model of the effect of perceived benefits and perceived convenience on behavior to convert waste into economic value through the intention variable of changing people's mindset about waste using the t test statistic at the jackknife resampling stage. The data used in this study are primary data obtained from research grants. The results showed that the best model of nonparametric truncated spline path analysis is quadratic polynomial degree with 3 knot points. In addition, the significance of the best truncated spline nonparametric path function estimation using jackknife resampling shows that all exogenous variables have a significant influence on the endogenous variables.

Keywords: nonparametric path analysis, truncated spline, linear, kuadratic, behavior to turn waste into economic value, jackknife resampling

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Authors: Wei-Hsuan Hsu, Yi-Xuan Huang

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Nanoimprint lithography (NIL) possesses the advantages of sub-10-nm feature and low cost. NIL patterns the resist with physical deformation using a mold, which can easily reproduce the required nano-scale pattern. However, the variation of process parameters and environmental conditions seriously affect reproduction quality. How to ensure the quality of imprinted pattern is essential for industry. In this study, the authors used the electrowetting technology to assist mold filling in the NIL process. A special mold structure was designed to cause electrowetting. During the imprinting process, when a voltage was applied between the mold and substrate, the hydrophilicity/hydrophobicity of the surface of the mold can be converted. Both simulation and experiment confirmed that the electrowetting technology can assist mold filling and avoid incomplete filling rate. The proposed method can also reduce the crack formation during the de-molding process. Therefore, electrowetting technology can improve the process quality of NIL.

Keywords: electrowetting, mold filling, nano-imprint, surface modification

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Authors: Basit Shahzad, Abdullatif M. Abdullatif

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Recent advancements in Information and Communication Technologies (ICT) and easy access to Internet have made social media the first choice for information sharing related to any important events or news. On Twitter, trend is a common feature that quantifies the level of popularity of a certain news or event. In this work, we examine the impact of Twitter trends on real world events by hypothesizing that Twitter trends have an influence on print media in Pakistan. For this, Twitter is used as a platform and Twitter trends as a base line. We first collect data from two sources (Twitter trends and print media) in the period May to August 2016. Obtained data from two sources is analyzed and it is observed that social media is significantly influencing the print media and majority of the news printed in newspaper are posted on Twitter earlier.

Keywords: twitter trends, text mining, effectiveness of trends, print media

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Authors: Ali Cheraghian, Farshid Hajati, Soheila Gheisari, Yongsheng Gao

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In this paper, we present a novel 2.5D face recognition method based on Gabor Discrete Cosine Transform (GDCT). In the proposed method, the Gabor filter is applied to extract feature vectors from the texture and the depth information. Then, Discrete Cosine Transform (DCT) is used for dimensionality and redundancy reduction to improve computational efficiency. The system is combined texture and depth information in the decision level, which presents higher performance compared to methods, which use texture and depth information, separately. The proposed algorithm is examined on publically available Bosphorus database including models with pose variation. The experimental results show that the proposed method has a higher performance compared to the benchmark.

Keywords: Gabor filter, discrete cosine transform, 2.5d face recognition, pose

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Authors: Majid Forghani, Michael Khachay

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In genetics, the impact of neighboring amino acids on a target site is referred as the nearest-neighbor effect or simply neighbor effect. In this paper, a new method called wavelet particle decomposition representing the one-dimensional neighbor effect using wavelet packet decomposition is proposed. The main idea lies in known dependence of wavelet packet sub-bands on location and order of neighboring samples. The method decomposes the value of a signal sample into small values called particles that represent a part of the neighbor effect information. The results have shown that the information obtained from the particle decomposition can be used to create better model variables or features. As an example, the approach has been applied to improve the correlation of test and reference sequence distance with titer in the hemagglutination inhibition assay.

Keywords: antigenic variants, neighbor effect, wavelet packet, wavelet particle decomposition

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Authors: Lumindu Dilumka, Ranaweera I. D., Sudusinghe S. P., Sanduni Kanchana A. M. K.

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This study was motivated by the challenges experienced by parents and guardians in ensuring the safety of children in cyberspace. In the last two or three years, online education has become very popular all over the world due to the Covid 19 pandemic. Therefore, parents, guardians and teachers must ensure the safety of children in cyberspace. Children are more likely to go astray and there are plenty of online programs are waiting to get them on the wrong track and also, children who are engaging in the online education can be distracted at any moment. Therefore, parents should keep a close check on their children's online activity. Apart from that, due to the unawareness of children, they tempt to share their sensitive information, causing a chance of being a victim of phishing attacks from outsiders. These problems can be overcome through the proposed web-based system. We use feature extraction, web tracking and analysis mechanisms, image processing and name entity recognition to implement this web-based system.

Keywords: online education, cyber bullying, social media, face recognition, web tracker, privacy data

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Authors: Moustafa Ibrahim Moustafa

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The purpose of this study was to determine the immediate and long term effects of a multimodal program, with the addition of cervical sagittal curve restoration and forward head correction, on severity of dizziness, disability, frequency of dizziness, and severity of cervical pain. 72 patients with cervicogenic dizziness, definite hypolordotic cervical spine, and forward head posture were randomized to experimental or a control group. Both groups received the multimodal program, additionally, the study group received the Denneroll cervical traction. All outcome measures were measured at three intervals. The general linear model indicated a significant group × time effects in favor of experimental group on measures of anterior head translation (F=329.4 P < .0005), cervical lordosis (F=293.7 P < .0005), severity of dizziness (F=262.1 P < .0005), disability (F=248.9 P < .0005), frequency of dizziness (F=53.9 P < .0005), and severity of cervical pain (F=350.1 P < .0005). The addition of Dennroll cervical traction to a multimodal program can positively affect dizziness management outcomes.

Keywords: randomized controlled trial, traction, dizziness, cervical

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Authors: M. A. I Perera, C. R. Wijesinghe, A. R. Weerasinghe

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his study was conducted to review and identify the unsupervised techniques that can be employed to analyze gene expression data in order to identify better subtypes of tumors. Identifying subtypes of cancer help in improving the efficacy and reducing the toxicity of the treatments by identifying clues to find target therapeutics. Process of gene expression data analysis described under three steps as preprocessing, clustering, and cluster validation. Feature selection is important since the genomic data are high dimensional with a large number of features compared to samples. Hierarchical clustering and K Means are often used in the analysis of gene expression data. There are several cluster validation techniques used in validating the clusters. Heatmaps are an effective external validation method that allows comparing the identified classes with clinical variables and visual analysis of the classes.

Keywords: cancer subtypes, gene expression data analysis, clustering, cluster validation

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Authors: Latha Subbiah, Dhanalakshmi Samiappan

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In this paper, we propose denoising Common Carotid Artery (CCA) B mode ultrasound images by a decomposition approach to curvelet thresholding and automatic segmentation of the intima media thickness and adventitia boundary. By decomposition, the local geometry of the image, its direction of gradients are well preserved. The components are combined into a single vector valued function, thus removes noise patches. Double threshold is applied to inherently remove speckle noise in the image. The denoised image is segmented by active contour without specifying seed points. Combined with level set theory, they provide sub regions with continuous boundaries. The deformable contours match to the shapes and motion of objects in the images. A curve or a surface under constraints is developed from the image with the goal that it is pulled into the necessary features of the image. Region based and boundary based information are integrated to achieve the contour. The method treats the multiplicative speckle noise in objective and subjective quality measurements and thus leads to better-segmented results. The proposed denoising method gives better performance metrics compared with other state of art denoising algorithms.

Keywords: curvelet, decomposition, levelset, ultrasound

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Authors: Stylianos Kampakis

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This paper presents a new algorithm for neural networks called “Input Dropout with Aggressive Re-weighting” (IDAR) aimed specifically at datasets with many useless features. IDAR combines two techniques (dropout of input neurons and aggressive re weighting) in order to eliminate the influence of noisy features. The technique can be seen as a generalization of dropout. The algorithm is tested on two different benchmark data sets: a noisy version of the iris dataset and the MADELON data set. Its performance is compared against three other popular techniques for dealing with useless features: L2 regularization, LASSO and random forests. The results demonstrate that IDAR can be an effective technique for handling data sets with many useless features.

Keywords: neural networks, feature selection, regularization, aggressive reweighting

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