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Hidden Markov Model: Application towards genomic analysis
Hidden Markov Model (HMM) has become one of the interesting methods for the researchers, especially in bioinformatics where different analysis are carried out. These are widely used in science, engineering and many other areas such as bioinformatics, genomic mapping, computer vision, finance and economics, and in social science. HMMs require much smaller training sets, and that the examination of the inner structure of the model provides often a deeper understanding of the phenomenon. In this survey, we first describe the important algorithms for the HMMs, and provide useful comparisons, aiming at their advantages and shortcomings. We then consider the major g applications, such as annotations, gene alignment and profiling of sequences, DNA structure prediction, and pattern recognition. We also list some analysis on how to use HMM for DNA genomes. Finally, we conclude use and perspectives of HMMs in bioinformatics and provide a critical appraisal for the same. 2016 IEEE. -
Rectangular microstrip antenna for WLAN application
This paper deals with the design of rectangular microstrip patch antenna for Wireless applications. In this paper a modified slotted microstrip antenna design for 2.5GHz operation is proposed. This provides improved performance in terms of lower return loss and higher gain. This is possible by inclusion of slots appropriately on the patch shape. The substrate material used in this design is Duroid5880 with permittivity 2.2 and size 47.43mm 39.65mm 1.6mm. ANSOFT HFSS EM simulator has been used for design and simulation of the microstrip antenna. The various antenna parameters such as frequency, VSWR, gain and directivity are analyzed to characterize the proposed antenna. 2015 IEEE. -
Deep Learning Based Face Recognized Attendance Management System using Convolutional Neural Network
In today's digital age, manual attendance tracking is plagued by inefficiency and the potential for inaccuracies, often leading to proxy attendance. The main aim of this research work is to manage and monitor the student's attendance by using face recognition technology. This proposed model is mainly categorized four major modules. First module is database creation. Second module is face detection. Then third module is face recognition and final module is automatic attendance updating process. Student images are compiled to create a comprehensive database, ensuring inclusivity across the class roster. The system utilizes the face recognition library, which relies on deep learning based algorithms for face detection and recognition during testing. This face recognition part Convolutional Neural Network algorithm is used. The system matches detected faces with the known database and marks attendance, ensuring a streamlined and accurate attendance tracking process. This innovative approach has the potential to revolutionize attendance management in educational settings, offering a contactless and efficient solution while mitigating proxy attendance concerns. The proposed model is to compare the accuracy level of face recognition. 2023 IEEE. -
Identification of Predominant Genes that Causes Autism Using MLP
Autism or autism spectrum disorder (ASD) is a developmental disorder comprising a group of psychiatric conditions originating in childhood that involve serious impairment in different areas. This paper aims to detect the principal genes which cause autism. Those genes are identified using a multi-layer perceptron network with sigmoid as an activation function. The multi-layer perceptron model selected sixteen genes through different feature selection techniques and also identified a combination of genes that caused the disease. From the background study, it is observed that CAPS2 and ANKUB1 are the major disease-causing genes but the accuracy of the model is less. The selected 16 genes along with CAPS2 and ANKUB1 produce more accuracy than the existing model which proved 95% prediction rate. The analysis of the proposed model shows that the combination of the predicted genes along with CAPS2 and ANKUB1 will help to identify autism at an early stage. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Lifestyle Diseases Prevalent in Urban Slums of South India
Lifestyle diseases have always been considered to be a malady of the middle and upper classes of society. Recent findings indicate that these chronic non-communicable diseases are common among the lower socioeconomic classes as well. The objective of this study was to assess the prevalence of lifestyle diseases in three cohorts of urban slums, namely, waste pickers living in non-notified slums, communities living in notified slums, and BBMP Pourakarmikas, and to identify the risk factors among the three cohorts contributing to the common lifestyle diseases including hypertension, diabetes, and cardiovascular diseases. In this study, the data was collected by conducting health camps, followed by analysis of the data using logistic regression, HosmerLemeshow test and ROC Curve Analysis. The prevalence of hypertension was found 13.35%, diabetes-8.53% and cardiovascular disease-3.59%. These were significantly associated with substance abuse, high BMI, and age. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Comparative Study of Collaborative Movie Recommendation System
The number of movies available has expanded, making it challenging to select a film that uses current technology to meet users' needs. Following the widespread use of internet services, recommendation systems have become commonplace. The objective for all recommendation systems now is to employ filtering and clustering algorithms to recommend content users are interested in. Suggestions for a media commodity like movies are offered to consumers by locating user profiles of people with comparable likes which makes users' preferences initially determined to allow them to rate movies of their choosing. After a period of use, the recommender system understands the user and offers films that are more likely to receive higher ratings. A comparison study on the existing models helps to understand future scope and improvements for more personalized models for movie recommendation. In comparison to previous models, the MovieLens dataset gives a dependable model that is exact and delivers more customized movie suggestions. In this paper, an approach to do a detailed study and review the user preferences based on item and content of movies has been made to understand the filtering techniques of the collaborative recommendation system to increase accuracy and give highly rated movies as recommendations to the user is carried and based on the results the recommendation system is built with a content-based filtering technique. 2022 IEEE. -
Swarm Intelligence Decentralized Decision Making In Multi-Agent System
This research aims to understand how groups of agents can make decisions collectively without relying on a central authority. The research could focus on developing algorithms and models for distributed problem solving, such as consensus-reaching and voting methods, or for coordinating actions among agents in a decentralized manner. The research could also look into the application of these methods in various fields like distributed robotics, swarm intelligence, and multi-agent systems in smart cities and transportation networks. Swarm intelligence in decentralization is an emerging field that combines the principles of swarm intelligence and decentralized systems to design highly adaptive and scalable systems. These systems consist of a large number of autonomous agents that interact with each other and the environment through local communication and adapt their behaviors based on environmental cues. The decentralized nature of these systems makes them highly resilient and efficient, with potential applications in areas such as robotics, optimization, and block chain technology. However, designing algorithms and communication protocols that enable effective interaction among agents without relying on a centralized controller remains a key challenge. This article proposes a model for swarm intelligence in decentralization, including agents, communication, environment, learning, decision-making, and coordination, and presents a block diagram to visualize the key components of the system. The paper concludes by highlighting the potential benefits of swarm intelligence in decentralization and the need for further research in this area. 2023 IEEE. -
Digital Water Dynamics: Analyzing VA Tech Wabag's Influence on India's Water Technology Landscape
This research delves into the transformative role of VA Tech Wabag in India's water technology landscape, amid the burgeoning challenges of water scarcity, pollution, and infrastructure inadequacies. Leveraging a comprehensive review of literature and fundamental analysis, the study underscores the global shift towards digitalization and sustainability in water management, situating VA Tech Wabag's initiatives at the forefront of this paradigm shift. Through innovative digital water solutions and large-scale infrastructure projects, the company has markedly enhanced water quality and availability across diverse urban and rural settings, underpinning its financial resilience and growth trajectory despite regulatory and fiscal hurdles. The discussion extrapolates the implications of these technological advancements, highlighting the company's commitment to environmental stewardship, community engagement, and the imperative for continuous innovation within a dynamic industry landscape. Conclusively, the paper affirms VA Tech Wabag's pivotal contributions to water security and resilience, advocating for future research on the scalability of digital water technologies and their long-term impacts on resource management. This study, enriched with specific data points and analyses, aims to offer a well-substantiated overview of VA Tech Wabag's influence on shaping a sustainable and efficient water technology ecosystem in India. 2024 IEEE. -
Exploring the Adoption Readiness of the Indian Generation for Social Media Payments: An In-Depth Analysis of WhatsApp Payments
Advancements in technologies always get higher acceptance among people. Regarding payment technologies, integrating payment facility in the Social Media platform are considered a second-generation payment technology. With the introduction of Hike wallets and WhatsApp payment, unprecedented opportunities are available to the users. In India, with the introduction of WhatsApp on November 2020, the users of FinTech got opened a gateway to social media payment. Social Media payments are considered easy and convenient, but is the Indian generation, especially people born in the internet phase (Gen Y and Gen Z), ready to adopt WhatsApp payment. The current study was done to investigate the elements that contribute to the acceptance and use of the WhatsApp payment service in India. To attain this objective, we used an extended UTAUT2 model with the moderating effect of generation. The data was gathered from 265 respondents and analyzed using the PLS-SEM method. The results of the study outlined that Gen Z is strengthening the moderating effect only between the facilitating conditions of the users and the actual usage of WhatsApp payment. The practical implications and directions for the further research are mentioned in the study. ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024. -
Svsl on combination of star with path
Super Vertex Sum Graph is a graph which admits super vertex sum labeling. In this paper, we combine stars and paths under different combinations which results in formation of new graphs and construct algorithm to obtain optimal super vertex sum labeling for the new graphs formed and their super subdivided graphs. 2020 Author(s). -
Comprehensive Review on Video Watermarking Security Threats, Challenges and its Applications
Data is a crucial resource for every business, and it must be protected both during storage and transmission. One efficient way of securing data and transferring it is through digital watermarking, where data is hidden inside a medium like text, audio, or video. Video watermarking is visible or invisible embedded data on a video in a logo, text, or video copyright disclaimer. In this proposed paper, the goal is to analyze the characteristics of video watermarking algorithms and the different metrics used for them. It deals with the extent to which the different requirements can be fulfilled, taking into consideration the conflicts between them and the practical challenges of video watermarking in terms of attacks like geometric attacks and non-geometric attacks. It also focuses on the process of watermarking a video. Recent advances in data security indicate that employing a video watermarking technology to transmit private data will be an effective method of transmitting sensitive data. The Electrochemical Society -
Design and optimization of the process parameters for fusion deposition modelling by experimental and finite element approach
Fused Deposition Modelling (FDM) is a rapidly evolving technology since the last couple of years. This method is also used for rapid prototyping, which uses layer on top of layer deposition of the material using hot extruders to build a given 3D model. 3D printing technology basically a tool-less process designed specifically to avoid assembly requirements with intricate geometry and complex features created at no extra cost and at the same time it is an energy-efficient technology that can provide environmental efficiencies in terms of both the manufacturing process and material utilization. This research primarily focuses on analyzing the critical process parameters and its influence on the properties of the components made out of FDM process. The FDM specimens are fabricated by using four factors (parameters) at three levels, and the factors are layer thickness, travel speed of the extruder, infill ratio, and infill density. The experiments are designed based on Taguchi L-9 orthogonal array. Total three responses are considered and they are tensile strength compressive strength and flexural strength. Taguchi analysis has done to optimize the factors and its levels. Finite element analysis has also done and compared with the experimental results. 2022 Author(s). -
Green Synthesized Fluorescent Nano-Carbon derived from Indigofera Tinctora (L.) leaf extract for sensing of Pb2+ ions
Plant-based synthesis of nanomaterials is a more reliable method since it is easy, quick, and environmentally friendly, and it does not require any specific conditions, unlike other methods. For the first time, we report the sensing of metal ions using a fluorescent nano-carbon material via a plant-based synthesis from the medicinal plant, Indigofera Tinctora (L.) (IBLH). This nanomaterial from the leaf extract of IBLH was synthesized by hydrothermal assisted green synthesis method. The as-synthesized sample was characterized by various spectroscopic techniques for confirming the formation of nano-carbon material. Optical studies revealed that IBLH was influential in determining toxic heavy metal ions (Pb2+). Detection of Pb2+ was observed from a range of 1 Molar to as low as 1Nano-Molar using IBLH as the probe. Stern-Volmer plot exhibits the progressive detection of the metal ion, proving that the IBLH nano-carbon material is capable of progressive sensing of various heavy metal ions. The Electrochemical Society -
Predicting Stock Market Trends: Machine Learning Approaches of a Possible Uptrend or Downtrend
This paper delves into a statistical analysis of the stock market, emphasizing the significance of accuracy in stock predictions. Large data sets can be handled by machine learning algorithms, which can also forecast outcomes based on past data and spot intricate patterns in financial data. They assist control risks, automate decision-making procedures, and adjust to changing circumstances. Multi-source data can be combined by ML models to provide a comprehensive picture of market circumstances. They can manage intricate, nonlinear interactions, provide impartial analysis, and lessen human bias. Models are able to adjust to shifting market conditions through ongoing learning and retraining. They must, however, exercise caution when deploying models in real-world situations and ensure that they are validated. Although machine learning has advantages for stock market analysis, it must be carefully evaluated for dangers and validated before being used in practical situations. The traditional machine learning model, Logistic Regression has been used in order to predict stock prices. It focuses on binary classification based on the trend of the stock. Through the model training and evaluation and additional analysis done on the results, this research contributes towards obtaining predictions and studying reasons of a possible uptrend or downtrend to further assist companies. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
A Review of Various Line Segmentation Techniques Used in Handwritten Character Recognition
Segmentation is a very critical stage in the character recognition process as the performance of any character recognition system depends heavily on the accuracy of segmentation. Although segmentation is a well-researched area, segmentation of handwritten text is still difficult owing to several factors like skewed and overlapping lines, the presence of touching, broken and degraded characters, and variations in writing styles. Therefore, researchers in this area are working continuously to develop new techniques for the efficient segmentation and recognition of characters. In the character recognition process, segmentation can be implemented at the line, word, and character level. Text line segmentation is the first step in the text/character recognition process. The line segmentation methods used in the character recognition of handwritten documents are presented in this paper. The various levels of segmentation which include line, word, and character segmentation are discussed with a focus on line segmentation. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Convolutional Autoencoder Based Feature Extraction and KNN Classifier for Handwritten MODI Script Character Recognition
Character recognition is the process of identifying and classifying the images of printed or handwritten text and the conversion of that into machine-coded text. Deep learning techniques are efficiently used in the character recognition process. A Convolutional Autoencoder based technique for the character recognition of handwritten MODI script is proposed in this paper. MODI script was used for writing Marathi until the twentieth century. Though at present, Devnagari is taken over as the official script of Marathi, the historical importance of MODI script cannot be overlooked. MODI character recognition will not be an easy feat because of the various complexities of the script. Character recognition-related research of MODI script is in its initial stages. The proposed method is aimed to explore the use of a deep learning-based method for feature extraction and thereby building an efficient character recognition system for isolated handwritten MODI script. At the classification stage, the features extracted from the autoencoder are categorized using KNN classifier. Performance comparison of two different classifiers, such as KNN and SVM, is also carried out in this work. 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Data Augmentation for Handwritten Character Recognition of MODI Script Using Deep Learning Method
Deep learning-based methods such as convolutional neural networks are extensively used for various pattern recognition tasks. To successfully carry out these tasks, a large amount of training data is required. The scarcity of a large number of handwritten images is a major problem in handwritten character recognition; this problem can be tackled using data augmentation techniques. In this paper, we have proposed a convolutional neural network-based character recognition method for MODI script in which the data set is subjected to augmentation. The MODI script was an official script used to write Marathi, until 1950, the script is no more used as an official script. The preparation of a large number of handwritten characters is a tedious and time-consuming task. Data augmentation is very useful in such situations. Our study uses different types of augmentation techniques, such as on-the-fly (real-time) augmentation and off-line method (data set expansion method or traditional method). A performance comparison between these methods is also performed. 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Handwritten Character Recognition of MODI Script using Convolutional Neural Network Based Feature Extraction Method and Support Vector Machine Classifier
Deep learning based algorithms are used in various pattern recognition tasks, including character recognition. Convolutional Neural Network (CNN) is effectively implemented for character recognition and is one of the best performing deep learning models. CNN can be used for character recognition directly or it can be used for extracting features in the character recognition process. Implementation of a feature extraction method using CNN autoencoder for MODI script character recognition is discussed in the paper. The extracted features are then subjected to Support Vector Machine (SVM) for the purpose of classification. The On-the-fly data augmentation method is used to add variability and generalization of the data set. MODI Script is an ancient Indian script and was used for writing Marathi until 1950. Various libraries and temples in India and abroad have a large collection of MODI documents. Character recognition related research of MODI script is still in infancy and research and development is necessary to extract the information from MODI manuscripts stored in various libraries. The performance of the proposed method, which uses CNN autoencoder as a feature extractor and an SVM based classifier gives very high accuracy and is better compared to the most accurate MODI character recognition method reported so far. 2020 IEEE. -
Problematic Gaming Among Adolescents within a Non-Clinical Population: A Scoping Review
Gaming is a pastime activity that has been enjoyed by millions of individuals worldwide for the past few years. The adolescent is in a developmental period that involves significant bio- psychosocial changes, including rapid changes in physical and mental states that make them more vulnerable to addiction. Online Gaming could have a higher risk of developing problematic gaming. Many studies have documented video gaming addiction and not problematic video gaming. Problematic gaming is a condition different from video game addiction. Further research remains needed to synthesise the factors behind problematic video game usage. The purpose of the scoping review is to synthesise the findings related to problematic video by identifying using a search through the following database: JSTOR, ProQuest, APA Psycnet, Ebsco. The research will help detect the early symptoms of addiction and understand the mechanism behind the addictive nature. Through the study, we can provide psychological care for adolescents by educating them and preventing and being aware of problematic gaming usage and experiences. The Electrochemical Society -
Analysis of Multinomial Classification for Legal Document Categorization
A major area of research today is the application of Machine Learning Techniques for Document or Text Classification. Document Classification is an important aspect of Electronic Discovery in the Legal domain. The need for the process to be automated has been realized over the past few years. Multinomial Classification is a well-known Supervised Machine Learning Technique that helps us classify if there are more than two classes used for the purpose of Classification. Evaluation metrics such as Precision, Recall, and F1 Score have been used to measure the efficiency of Classification. Logistic Regression and Gradient Boosting Algorithms have outperformed other Multiclass Classification techniques. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.