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Design, Training, and Implementation of A New Individualized Education Plan (IEP) Format For Special Educators And Students With Intellectual Disabilities At Selected Special Schools
An individualized Education Plan (IEP) is a multidisciplinary, teamdeveloped plan required for every child receiving special education services. The researcher delved into concerns surrounding Individualized newlineEducation Programs (IEPs) for students with intellectual disabilities. Two significant hurdles were discovered: existing IEPs lacked essential intervention areas, and special education teachers felt inadequately newlineequipped to construct effective plans. newlineThe study tackled these concerns head-on through a multi-pronged approach. Firstly, a meticulous analysis of existing IEPs revealed crucial sections missing from intervention plans, hindering their effectiveness. newlineThis analysis served as the blueprint for crafting a more comprehensive IEP format that addressed the identified gaps and provided a robust framework for intervention. Next, the study focused on empowering special education teachers. Sixty special education teachers certified by the Rehabilitation Council of newlineIndia, participated in training sessions on the new format, undergoing a vital skills and knowledge upgrade in IEP development. This equipped them with the tools and understanding necessary to create more effective plans tailored to individual student needs. The theory then transitioned to practice. Students with intellectual newlinedisabilities were included in interventions based on the improved IEPs, with their progress closely tracked and evaluated. The results were highly promising. Teachers demonstrated a tangible improvement in knowledge, translating into their ability to create more effective IEPs. More importantly, students thrived with the enhanced format. Those involved in interventions using the improved IEPs exhibited significant progress in various domains, highlighting the positive impact of the new approach. The study culminated in key recommendations for further newlineimprovement. Ongoing teacher training sessions were suggested to ensure teachers remain updated on best practices and evolving methodologies. -
Low-frequency pulse-jitter measurement with the uGMRT I: PSR J0437-4715
High-precision pulsar timing observations are limited in their accuracy by the jitter noise that appears in the arrival time of pulses. Therefore, it is important to systematically characterise the amplitude of the jitter noise and its variation with frequency. In this paper, we provide jitter measurements from low-frequency wideband observations of PSR J0437 4715 using data obtained as part of the Indian Pulsar Timing Array experiment. We were able to detect jitter in both the 300-500 MHz and 1 260-1 460 MHz observations of the upgraded Giant Metrewave Radio Telescope (uGMRT). The former is the first jitter measurement for this pulsar below 700 MHz, and the latter is in good agreement with results from previous studies. In addition, at 300-500 MHz, we investigated the frequency dependence of the jitter by calculating the jitter for each sub-banded arrival time of pulses. We found that the jitter amplitude increases with frequency. This trend is opposite as compared to previous studies, indicating that there is a turnover at intermediate frequencies. It will be possible to investigate this in more detail with uGMRT observations at 550-750 MHz and future high-sensitive wideband observations from next generation telescopes, such as the Square Kilometre Array. We also explored the effect of jitter on the high precision dispersion measure (DM) measurements derived from short duration observations. We find that even though the DM precision will be better at lower frequencies due to the smaller amplitude of jitter noise, it will limit the DM precision for high signal-to-noise observations, which are of short durations. This limitation can be overcome by integrating for a long enough duration optimised for a given pulsar. The Author(s), 2024. Published by Cambridge University Press on behalf of Astronomical Society of Australia. -
Powerlessness in the moral self: a social cognitive perspective on drug users
Powerlessness resides in devalued self-images of drug users. This study, drawing on social and moral psychology, examined the moral functioning of drug users compared to non-drug users. Self-reported data concerning moral identity and moral judgment on drug use were assessed and compared between groups. Drug users appeared to have significantly weaker moral identity centrality and pro-drug moral judgment than non-drug users. They also showed dissociation in the relationship between moral identity and moral judgment. As a result, the study proposed a moral identity model of drug use to better approach social cognitive powerlessness in drug users moral self. 2021 Taylor & Francis Group, LLC. -
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High-Movement Human Segmentation in Video Using Adaptive N-Frames Ensemble
A wide range of camera apps and online video conferencing services support the feature of changing the background in real-time for aesthetic, privacy, and security reasons. Numerous studies show that the Deep-Learning (DL) is a suitable option for human segmentation, and the ensemble of multiple DL-based segmentation models can improve the segmentation result. However, these approaches are not as effective when directly applied to the image segmentation in a video. This paper proposes an Adaptive N-Frames Ensemble (AFE) approach for high-movement human segmentation in a video using an ensemble of multiple DL models. In contrast to an ensemble, which executes multiple DL models simultaneously for every single video frame, the proposed AFE approach executes only a single DL model upon a current video frame. It combines the segmentation outputs of previous frames for the final segmentation output when the frame difference is less than a particular threshold. Our method employs the idea of the N-Frames Ensemble (NFE) method, which uses the ensemble of the image segmentation of a current video frame and previous video frames. However, NFE is not suitable for the segmentation of fast-moving objects in a video nor a video with low frame rates. The proposed AFE approach addresses the limitations of the NFE method. Our experiment uses three human segmentation models, namely Fully Convolutional Network (FCN), DeepLabv3, and Mediapipe. We evaluated our approach using 1711 videos of the TikTok50f dataset with a single-person view. The TikTok50f dataset is a reconstructed version of the publicly available TikTok dataset by cropping, resizing and dividing it into videos having 50 frames each. This paper compares the proposed AFE with single models and the Two-Models Ensemble, as well as the NFE models. The experiment results show that the proposed AFE is suitable for low-movement as well as high-movement human segmentation in a video. 2022 Tech Science Press. All rights reserved. -
Portrait segmentation using ensemble of heterogeneous deep-learning models
Image segmentation plays a central role in a broad range of applications, such as medical image analysis, autonomous vehicles, video surveillance and augmented reality. Portrait segmenta-tion, which is a subset of semantic image segmentation, is widely used as a preprocessing step in multiple applications such as security systems, entertainment applications, video conferences, etc. A substantial amount of deep learning-based portrait segmentation approaches have been developed, since the performance and accuracy of semantic image segmentation have improved significantly due to the recent introduction of deep learning technology. However, these approaches are limited to a single portrait segmentation model. In this paper, we propose a novel approach using an ensemble method by combining multiple heterogeneous deep-learning based portrait segmentation models to improve the segmentation performance. The Two-Models ensemble and Three-Models ensemble, using a simple soft voting method and weighted soft voting method, were experimented. Intersection over Union (IoU) metric, IoU standard deviation and false prediction rate were used to evaluate the performance. Cost efficiency was calculated to analyze the efficiency of segmentation. The experiment results show that the proposed ensemble approach can perform with higher accuracy and lower errors than single deep-learning-based portrait segmentation models. The results also show that the ensemble of deep-learning models typically increases the use of memory and computing power, although it also shows that the ensemble of deep-learning models can perform more efficiently than a single model with higher accuracy using less memory and less computing power. 2021 by the authors. Licensee MDPI, Basel, Switzerland. -
Selfie Segmentation in Video Using N-Frames Ensemble
Many camera apps and online video conference solutions support instant selfie segmentation or virtual background function for entertainment, aesthetic, privacy, and security reasons. A good number of studies show that Deep-Learning based segmentation model (DSM) is a reasonable choice for selfie segmentation, and the ensemble of multiple DSMs can improve the precision of the segmentation result. However, it is not fit well when we apply these approaches directly to the image segmentation in a video. This paper proposes an N-Frames (NF) ensemble approach for a selfie segmentation in a video using an ensemble of multiple DSMs to achieve a high-performance automatic segmentation. Unlike the N-Models (NM) ensemble which executes multiple DSMs at once for every single video frame, the proposed NF ensemble executes only one DSM upon a current video frame and combines segmentation results of previous frames to produce the final result. For the experiment, we use four state-of-the-art image segmentation models to make an ensemble. We evaluated the proposed approach using 81 videos dataset with a single-person view collected from publicly available websites. To measure the performance of segmentation models, Intersection over Union (IoU), IoU standard deviation, false prediction rate, Memory Efficiency Rate and Computing power Efficiency Rate parameters were considered. The average IoU values of the Two-Models NM ensemble, Two-Frames NF ensemble, Three-Models NM ensemble and Three-Frames NF ensemble were 95.1868%, 95.1253%, 95.3667% and 95.1734% each, whereas the average IoU value of single models was 92.9653%. The result shows that the proposed NF ensemble approach improves the accuracy of selfie segmentation by more than 2% on average. The result of cost efficiency measurement shows that the proposed method consumes less computing power like single models. 2021 IEEE. -
A Study on the Effect of Canny Edge Detection on Downscaled Images
Abstract: Nowadays user devices such as phones, tablets etc. allows processing the images with help of high-end applications and softwares developed. Most of the times, the images are downscaled to make them compatible with these end devices. This leads to the loss of image quality. This loss of information on downscaling an image results in distortion of edges and while zoomed in results into a blurred image. As the edge detection is a basic step for many image processing applications such as object detection, object segmentation, object recognition, etc. It is necessary to know the impact of edge detection on downscaled image. In this paper, we are using Canny Edge detection method to detect the edges. The original images are downscaled using different interpolation methods. Canny Edge detection is applied on original images and downscaled images to compare the distortion in the edges. We used Structural Similarity Index Method for comparison. We are also comparing execution time taken by Canny Edge Detection on different interpolation methods to check for optimal interpolation method. We observed that the distortion in edges and time efficiency differ for different interpolation methods which are detailed below in the result section. As blurring is also a disadvantage of downscaling, we are applying Gaussian Blur on the images to compare the blurring due to Gaussian blur technique and blurring due to downscaling. 2020, Pleiades Publishing, Ltd. -
Accuracy Enhancement of Portrait Segmentation by Ensembling Deep Learning Models
Portrait segmentation is widely used as a preprocessing step in multiple applications. The accuracy of portrait segmentation models indicates its reliability. In recent times, portrait segmentation using deep learning models have achieved significant success in performance and accuracy. However, these portrait segmentation models are limited to a single model. In this paper, we propose ensemble approach using multiple portrait segmentation models to improve the segmentation accuracy. The result of experiment shows that the proposed ensemble approach produces better accuracy than individual models. Accuracy of single models and proposed ensemble approach were compared with Intersection over Union (IoU) metric and false prediction rate to evaluate the accuracy performance. The result shows reduced false negative rate and false discovery rate, this reduction in false prediction has enabled ensemble approach to produce segmented images with optimized error and improved result of segmentation in portrait area of human body than individual portrait segmentation models 2020 IEEE. -
Analyzing the Performance of Canny Edge Detection on Interpolated Images
Surveillance cameras are extensively used nowadays in many commercial and domestic places to monitor theft, intrusion and other illegal activities. Typically, the cameras are placed at a high position to monitor a large area. Therefore, the captured images include background area in addition to the target objects. Under such situation, the image can be zoomed to focus on the target objects using various interpolation techniques. For further processing of the image, many techniques like edge detection, image sampling and image thresholding etc. are available. Considering edge detection to be a basic step for many application such as Object detection, Object recognition etc, in this work, we analyze the performance of the Canny Edge Detection algorithm on images interpolated using Nearest Neighbour, Bilinear and Bicubic interpolation methods. Canny Edge Detection is applied to the input image and the resultant image is saved for later comparison. The same image is upscaled using interpolation and the Canny Edge Detection algorithm is used on this upscaled image. This image is then resized to the original image size. Both the images are compared to check for their similarity using the Structural Similarity Index Method (SSIM). 2019 IEEE. -
Moral Identity, Moral Emotions and Maladaptive Personality Traits Among Adolescents in South Korea by Doo Jong Kim
The rule of survival of the fittest often thwarted the leap towards holistic development. How does morality associate with personality in adolescent development? Drawing on the theories of Augusto Blasi and Gordon Allport, the present study took a morality-personality integrative approach to adolescent development and viewed moral identity centrality as an agentic drive for their holistic growth. It aimed to determine whether moral identity centrality, other-praising moral emotion, and personality dysfunction of maladaptive personality traits are coherent in predicting antisocial behaviour in a sample of 436 Korean adolescents (M = 15.71 years, SD = .70; female 48.4%). The present study set up three hypotheses in the structural relationship of research variables (i.e., moral identity centrality, other-praising moral emotion, personality dysfunction of maladaptive personality traits and antisocial behaviour). Hypothesis 1: Personality dysfunction of multiple maladaptive personality traits predicts antisocial behaviour. Hypothesis 2: Other-praising moral emotion and personality dysfunction mediate moral identity centrality and antisocial behaviour. Hypothesis 3: Sex does not make notable differences in the structural relationship of research variables. The study analyzed the data mainly through structural equation modelling (SEM). As a result, all hypotheses were accepted. First, four multiple maladaptive traits, i.e., negative affectivity, antagonism, disinhibition, and psychoticism, significantly predicted adolescents antisocial behaviour (and#946; = .791, p lt .001) (Hypothesis 1). Second, the modified structural model showed a serial multiple mediation effect of other-praising moral emotion and personality dysfunction between moral identity centrality and antisocial behaviour (Hypothesis 2). Third, multi-group analyses showed apparent coherence among research variables regardless of sex (Hypothesis 3). -
Customers satisfaction towards online banking services of public sector banks
At present the banking industry around the world has been undergoing a rapid transformation. The deepening of information technology has facilitated better tracking and fulfillment of commitments, multiple delivery channels for online customers and faster resolution of issues. Customer satisfaction is important criteria for banks sustenance, now banks are offers online banking services according to the customer needs and requirements. This study analysed customers satisfactions towards online banking services of public sector banks in Tiruchirappalli district. It is understand from the present study that bank websites and technology platforms has to offer various knowledge features on financial services. To retain the existing customers, banks has to conduct regular surveys on the customer satisfaction. The results of the study shows that variables like prompt response, security and Website design and ease of use are top three factors affected customer satisfaction. IJSTR 2020. -
The role of business development management consultants in construction industry in India
The Indian construction industry is in boom in terms of the growth and development taking place in the field of infrastructure and the service sector and subsequently many public owned design, architecture, construction and engineering companies were set up in India. The current study emphasises on the role of business development management consultants in the overall performance of construction companies and the existing review of literature focuses on the various factors which affects the overall business performance of the construction companies which includes different variables considering all the existing studies this study identified the gap in terms of intervention of BDMC in the construction field. The methodology adopted for the study is quantitative exploratory research and the results of the study was very positive that the promoters believe that the business development management consultants contribute a lot in the overall performance of the construction company with their expertise in the field and professional way of dealing with the other stake holders of the company helps them to perform very effectively and efficiently. This study helped in getting the practical exposure to the working of construction companies and understanding the role of business development management consultants. The Author(s) 2019. -
Effectiveness of information and communication technologies on the learning outcomes of management graduates in public and private sector institutes
In most of the countries, information and communication technology (ICT) has a made a strong impact on the development of educational sector and it is considered to be the strongest pedagogy in delivering the communication effectively. This is the era of Information and Communication and to increase the efficiency and effectiveness of Education sector the role of ICT is indispensable. The performance of the students can be made better with the help of information and communication technology (ICT). ICT will help the students to gather the information very effectively and help in in developing their knowledge skills as well as to improve their learning skills. To understand the effectiveness and also the impact of ICT in Education sector especially in management education, for the current study the data is been collected from 158 respondents who are MBA graduates from 15 B-schools from Bangalore city, the study is being conducted with the help of convenient sampling from various B-schools from Bangalore city. The study has shown that the learning and teaching through ICT improves the knowledge and learning skills of students. This indicates that existence of ICT is improving the educational efficiency among the management graduates in Bangalore city. 2019, Institute of Advanced Scientific Research, Inc. All rights reserved. -
Optimization of Biodiesel Production from Waste Cooking Oil by Box Behnken Design Using Response Surface Methodology
Interest in Biodiesel production has grown over the years due to concerns related to the environment, and the solutions include deriving energy from waste as the replacement for diesel, a petroleum-derived fuel. Biodiesel has been accepted as a "green fuel" as it is a renewable, non-toxic, safe and biodegradable energy material. The utilisation of waste cooking oil (WCO) by converting it into biodiesel is one of the promising alternatives to diesel. An attempt to optimise the biodiesel production from WCO (a waste material) has been made via this study. The process adopted was Trans-esterification of pretreated WCO, and the optimization of biodiesel production was carried out by Box-Behnken method using a response surface methodology. The variations between the analytical and experimental results were within acceptable limits. The response surface methodology resulted in an optimum yield of 96.88% (analytical), which was validated through an experiment within an acceptable error of 0.58%. 2021,International Journal Of Renewable Energy Research.All rights reserved. -
Maximised bioethanol extraction from bamboo biomass through alkali pretreatment and enzymatic saccharification by application of ANN-NSGA-II-based optimisation method
The demand for alternative fuels is growing due to the depletion of fossil fuel resources. Non-edible resources are explored as alternatives, and a bamboo is an up-and-coming option for producing ethanol. The extraction process for bioethanol from bamboo involves alkali pretreatment, enzymatic saccharification, and ethanol production. The bamboo biomass is treated with alkali at high temperatures and pressure. This treatment helps break the lignin bonds that hinder the reaction between cellulose and enzymes. As a result, the pretreated biomass contains 40% less lignin than its raw form. Next, the air-dried pretreated biomass undergoes saccharification using Supercut Acid Cellulose. The saccharification process is optimised to achieve the shortest possible time, determined through prediction models based on artificial neural networks and optimisation techniques like Non-dominated Sorting Genetic Algorithm-II. The optimised process involves specific biomass and enzyme loading, producing reducing sugars estimated using the DNS method. Following enzymatic Saccharification, the hydrolysate is fermented using Saccharomyces cerevisiae, a type of yeast. This fermentation process yields ethanol with a 1614.26mg/kg concentration. 2023, The Author(s), under exclusive licence to Springer Nature B.V. -
Verification and validation of Parallel Support Vector Machine algorithm based on MapReduce Program model on Hadoop cluster
From the recent years the large volume of data is growing bigger and bigger. It is difficult to measure the total volume of structured and unstructured data that require machine-based systems and technologies in order to be fully analyzed. Efficient implementation techniques are the key to meeting the scalability and performance requirements entailed in such scientific data analysis. So for the same in this paper the Sequential Support Vector Machine in WEKA and various MapReduce Programs including Parallel Support Vector Machine on Hadoop cluster is analyzed and thus, in this way Algorithms are Verified and Validated on Hadoop Cluster using the Concept of MapReduce. In this paper, the performance of above applications has been shown with respect to execution time/training time and number of nodes. Experimental Results shows that as the number of nodes increases the execution time decreases. This experiment is basically a research study of above MapReduce applications. 2013 IEEE. -
Value co-creation through search efforts and customer involvement impacting purchase intention of smart phones
A marketing strategy which successfully involves its customer helps in stimulating purchase intentions. Understanding the behavioral aspects of customers become pertinent in formulating such strategies. The aim of this paper is to explore the underlying factors of customer involvement in value co-creation and discover how it affects the purchase intention of the customers towards smartphones. The study also tries to understand the contribution of search efforts towards customer involvement and how it affects purchase intention. The data for the study has been collected through a validated questionnaire from 233 respondents. Extensive literatures are reviewed to identify research gap and identify the variables for the study. The study can help marketers to identify the factors of customer involvement so that they can understand the customer purchase behaviour better and hence forecast on customer purchase intention to improve their sales of smartphones. BEIESP. -
Evaluating forces associated with sentient drivers over the purchase intention of organic food products
The study proposes to find out the factors which influence awareness among the consumers towards purchasing organic food product. The study is based on primary data by using tools Chi-square test, Cronbach alpha, KMO, and Bartlett's test, ANOVA, regression, correlation, and cross-tabulation. The study found that awareness driver's nutritional information, price, certification, brand name, and logos have an essential influence on the purchase intention of the product of organic food. However, labeling and food standards do not show a noteworthy rapport between labeling and organic food products' purchase plans. The core commitment and flow to explore are to analyze purchasers with respect to organic guarantee systems (accreditation, guidelines, logo, imprints, and confirmation) so we can distinguish the genuine organic products. The independent factors of awareness like organic buying preference and buying frequency, have a significant influence on the purchase intention of organic food. The research provided evidence of consumer awareness and purchase intention of organic food that would help the organic food industry to promote their products according to the attribute of customers. 2020 Asian Economic and Social Society. All rights reserved.