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Research Advancements In Autism Spectrum Disorder Using Neuroimaging
Autism Spectrum Disorder (ASD) is a complex neurological condition that manifests as a spectrum of symptoms at varying levels of severity.. Insufficient data and heterogeneous characteristics of ASD are the primary causes of it being a complex, challenging, and intriguing field of research. ASD is declared one of the fastest-growing mental disorders affecting the normal life of subjects at various levels of severity and stages of age. Recent research work observed a significant change in brain structure, functional connectivity, and network using neuroimaging resources. Each autistic brain is as unique as a fingerprint for typically developed subjects. Magnetic Resonance Imaging (MRI) is accepted as an excellent diagnostic technology for numerous disorders with a satisfactory amount of information by medical experts. Cognitive deficits brain MRI modalities contain microscopic information, which is time-consuming and needs experts to interpret. Artificial intelligence (AI) strategies (Machine Learning and Deep Learning) are implemented with various imaging modalities to decrypt the information for diagnosis and to support computer-added solutions for appropriate treatment. The research aims to discover the various evolutionary impacts of artificial intelligence for the diagnosis of Autism syndrome disorder using neuroimaging. To automate the diagnosis using artificial intelligence methodologies, medical imaging has proved to be of immense use. Though neuroimaging and AI produced satisfactory diagnostic solutions for many mental disorders, research is required to explore the autistic brain for more neuroimaging information to be used for further investigation. Some of the Internet of Things (IoT) solutions for detection and training are also invented but not with the use of Neuroimaging. Autism is a neurological condition that affects the brain, and hence more research is advised using imaging and AI techniques to support the community to enjoy a normal life. 2023 American Institute of Physics Inc.. All rights reserved. -
Spatio - Temporal Analysis of Temperature in Indian States
Data, the oil of the century, is available in multiple formats for various applications. It is collected, stored, and distributed across different use cases in various forms. Researchers study, analyse and use data for numerous analyses and predictions. There is an increase in demand and consideration of spatiotemporal data analysis. Analysing and obtaining insights from the spatiotemporal data are carried out by various researchers. Many investigations have started investigating the strategies for spatial-transient examination and applying spatial-transient information investigation procedures to different areas. Analysing spatiotemporal data has been an advanced task; with the help of various Python libraries, Spatio Temporal dataset about the temperature of states of India is analysed to support the harsh climate near the region of tropic of cancer. Across the decade, there has been a cyclic trend in the temperature, which keeps toggling yet increases over time. It remains a question of worry and genuine concern to predict climatic conditions. Spatio-temporal analysis of temperature in Indian states involves analysing the spatial and temporal variations in temperature across different states in India. The study can use various statistical and geographic information systems (GIS) tools. Spatio-temporal analysis of temperature in Indian states can provide valuable insights into the changing climate patterns in different regions of the country, which can be helpful for policymakers, researchers, and other stakeholders to make informed decisions related to climate change mitigation and adaptation. 2023 American Institute of Physics Inc.. All rights reserved. -
A Comprehensive Study On Detection Of Emotions Using Human Body Movements: Machine Learning Approach
Identifying emotions from human beings is the most challenging area in artificial intelligence. There are different modules used to identify emotions like speech, face, EEG, Physiological Signals, and body movement. However, emotional recognition from body movement is the need of time. The review focuses on identifying various emotions with the help of the full-body movement model and the parts-based model. The aim of the survey is to identify the recent work done by the researchers with the help of full-body movements and body parts-based models. Recently, little research has been done on the identification of emotions using body movements, but most of the time it has succeeded to some extent. Identifying various human emotions using body movements is a really very challenging task. This research work discovers that the various popular machine learning algorithms like Support Vector Machines, Neural Networks, and convolutional neural networks are majorly used to identify basic emotions. 2023 American Institute of Physics Inc.. All rights reserved. -
Novel Deep Neural Network Based Stress Detection System
Stress is a state of tension on an emotional or bodily level. Frustration, despair, anxiety, and other mental health problems can all be brought on by Stress. Strain is a side effect of Stress. People can openly share their views and opinions on social media networking sites like Twitter and Facebook, which are highly popular. The COVID 19 pandemic has wreaked havoc on millions of peoples lives all across the world. The public has experienced Stress as a result of the various measures employed to stop the spread of COVID 19, including confinement and social isolation. The current research seeks to develop an unique COVID 19 scenario-based deep neural network-based Stress detection system using tweets related to COVID 19. We use deep learning to create three models. RNN with single LSTM layer, two layers of LSTM with RNN followed by bidirectional LSTM layer is built to detect Stress for the considered dataset. A number of recurrent neural networks are built upon the Keras layers. The optimization algorithm called RMSProp and Sigmoid activation function is used. It is observed that RNN with 2 layers of LSTM outperforms the other deep learning architectures constructed. 2023 American Institute of Physics Inc.. All rights reserved. -
Restructuring of Layout Designs and Operational Processes in Production Lines of Manufacturing Companies Globally to Compete Post Pandemic Conditions
Manufacturers globally faced various challenges in terms of sustainability and continuous production in the past COVID conditions and it showed the importance of redesigning the existing processes. All new manufacturing processes should be designed by considering the pandemic situation in the future. The present study is focused on restructuring the layout of an existing production unit in order to cope with any such eventualities. Various models that are suitable for adoption in post-pandemic, have been proposed and their efficiencies are compared in this paper. The authors also investigate how such changes will impact the efficiency of the existing line. Key parameters considered in this paper are the total production hours, line efficiency, balancing delay, and production rate. 2023 ACM. -
Forecasting a Fast-Moving Consumer Goods (FMCG) Company's Customer Repurchase Behavior via Classification Machine Learning Models
With numerous businesses offering clients equivalent products, the FMCG (Fast Moving Consumer Goods) industry is very competitive. Retaining client loyalty and encouraging them to return to make product purchases is a big concern for businesses in this sector. One of the main issues this bleak business needs to overcome is customer retention. Failure to repurchase by customers is a sign that they do not trust the brand, which will increase attrition rates and have an adverse effect on the company's revenue. These issues were addressed by attempting to predict the customer repurchase rate and approaching the target segments in accordance with that prediction, but this was done entirely from the perspective of the consumer and not from the retailer, and it ignores other factors like location, the salespeople they work with, the wholesaler they are affiliated with, and the customer programme they have chosen. The retailer's repurchase pattern must be predicted using a more accurate and effective model that considers all the variables. Retailers play a significant role in the supply chain for FMCG businesses. Different models like KNN, Nae Bayes and Logistic Regression was explored to find the best fit. By keeping them, the business can forge enduring connections that are crucial for preserving stabilityand dependability in the distribution network and having the resources necessary to serve its clients. 2023 ACM. -
Using Time series analysis, analyze the impact of the wholesale price index on the price escalation in the automotive industry
The automobile industry is a crucial sector of the economy, contributing significantly to employment and economic growth. One of the major challenges faced by this industry is the problem of price escalation, which can affect both consumers and manufacturers. In this project, we explore the impact of wholesale price index (WPI) on the price escalation of automobiles using time series analysis. We analyze the historical data of WPI and automobile prices in India from 2010 to 2022. We use statistical techniques like stationarity tests, autocorrelation analysis, and Granger causality tests to understand the relationship between WPI and automobile prices. Furthermore, we employ a SARIMA model in predicting WPI value and Vector Auto regression (VAR) model to analyze the dynamic interactions between WPI and CPI value. Our findings suggest that WPI has a significant impact on the price escalation of automobiles in India. The VAR model shows that there is a positive feedback loop between WPI, CPI and automobile prices, implying that an increase in WPI leads to a corresponding increase in automobile prices and vice versa. This feedback loop can create an inflationary spiral in the automobile industry, which can be detrimental to the economy. Our project highlights the importance of monitoring WPI and its impact on the automobile industry. Policymakers and industry experts can use our findings to develop effective strategies to manage price escalation in the automobile industry and mitigate its negative impact on the economy. 2023 ACM. -
Catalyzing Green Mobility: Consumer Preferences for Green Energy Vehicles
Due to growing urbanization and the increase of vehicles, most Indian cities endure traffic congestion and significant air pollution. As a result, alternate technology in autos, such as electric vehicles, may become necessary (EV). This study aims to identify consumer preferences toward electric vehicles in the Indian market. This research conducted a survey and analyzed the opinions of people regarding their preferences for electric vehicles, demographics, and some of the demotivation which might be stopping them to switch to electric vehicles altogether. This research will help in determining different factors influencing the perception of consumers toward electric vehicles and what they expect when they think about purchasing a new electric vehicle. It is important to understand that electric vehicles are really getting popular now because of the rising fuel prices and environmental concerns. People are thinking about electric vehicles and replacing them with their regular petrol or diesel vehicles. In this research there might be some challenges or roadblocks in switching to electric vehicles. This research found out that despite a favorable attitude toward electric vehicles, individuals are hesitant to transition to electric vehicles due to different hurdles connected with them. This research found out that mostly the preferences of the consumers are good charging infrastructure, a good range of the electric vehicle, pocket-friendly vehicles are the most common preferences of consumers buying an electric vehicle. 2023 EDP Sciences. All rights reserved. -
A Comparative Study in Predictive Analytic Frameworks in Big Data
Every information processing sector uses predictive analytic framework in terms of distributed datasets through a variety of applications. These analytic frameworks are effectively used for various analyses of data, parameter, and attributes. Leveraging data to make insightful decisions for maximizing the effectiveness requires the determination of the best predictive framework for any organization. Even a retail unit which wants to scale up its production rely on multiple parameters. These parameters must be analyzed for effective quality control in any domain. Since there are diversities in every domain the data will be in varied form, and these are accumulated as Big Data. These analyses are done using machine learning frameworks. The strategy involved would differ from one domain to another such as in the health care sector the framework might predict the magnitude of patients admitted to the urgent care facility over the upcoming days whereas in the production industry the framework would align quality control measures. This article analyses a few domains and their deployed machine learning impacts in a strategic way. 2023 American Institute of Physics Inc.. All rights reserved. -
Continuance Intention of ChatGPT Use by Students
ChatGPT, an AI language model, has gained significant attention for its potential to enhance educational experiences and foster interactive learning environments. The potential of student interaction via ChatGPT has engendered significant debate around educational technology. It is apparent that the current literature has yet to fully explore the role of ChatGPT in management education. Amidst the increasing integration of ChatGPT into educational contexts, the concept of continuance intention takes center stage. This research paper delves into the nuanced landscape of students continuance intention regarding the use of ChatGPT in educational settings. We ground our study in Technology Continuance Theory and Theory of Planned Behavior to examine students continuance intention to use ChatGPT. By investigating the determinants that shape this intention, we aim to provide insights that inform educators and educational technology designers in optimizing the integration of AI-driven tools like ChatGPT. This study contributes to the growing body of research at the intersection of AI and education, offering valuable implications for both theory and practice. 2024, IFIP International Federation for Information Processing. -
Specular Reflection Removal Techniques in Cervix Image: A Comprehensive Review
Cancer detection through medical image segmentation and classification is possible owing to the advancement in image processing techniques. Segmentation and classification tasks carried out to predict and classify diseases need to be dependable and precise. Specular reflections are the high-intensity and low-saturation areas that reflect the light from the probing devices that capture the picture of the organ surface. These areas sometimes mimic the features that are key identifying factors for cancers like acetowhite lesions. This review article examines the various methods proposed for removing specular reflections from medical images, especially those captured by colposcope. The fundamentals of specular reflection removal and its associated challenges are discussed. The paper reviews several state-of-the-art approaches for specular reflection removal. The comprehensive review can be a strong foundation for researchers looking to decide on appropriate techniques to employ in their respective research approaches. 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Predicting the Thyroid Disease Using Machine Learning Techniques
An endocrine gland that is allocated in the front of the neck is called the thyroid, which produces thyroid hormones as its main job. Thyroid hormone may be produced insufficiently or excessively as a result of its potential malfunction. There are various thyroid types including Hyperthyroidism, Hypothyroidism, Thyroid Cancer Thyroiditis, swelling of the thyroid. A goiter is an enlarged thyroid gland. When your thyroid gland produces more thyroid hormones than your body requires, you have hyperthyroidism. When the thyroid gland in our body doesnt provide enough thyroid hormones, then our body has hypothyroidism; when you have euthyroid sick, your thyroid function tests during critical illness taken in an inpatient or intensive care setting show alterations. Hypothyroid, hyperthyroid, and euthyroid conditions are expected from these thyroid conditions. The Three similarly used machine learning algorithms are: Support Vector Machine (SVM), Logistic Regression, and Random Forest methods, were evaluated from among the various machine learning techniques to forecast and evaluate their performance in terms of accuracy. Random forest can perform both regression and classification tasks. Logistic Regression is used to calculate or predict the probability of a binary (yes/no) event occurring. SVM classifiers offers great accuracy and work well with high dimensional space. A thyroid data set from Kaggle is used for this. This study has demonstrated the use of SVM, logistic regression, and random forest as classification tools, as well as the understanding of how to forecast thyroid disease. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An Early Detection of Autism Spectrum Disorder Using PDNN and ABIDE I&II Dataset
The current study's objective was to use deep learning methods to separate valetudinarians amidst autism spectrum disorders (ASDs) from controls employing just the patients brain activation patterns from a dataset of large brain images. We examined brain imaging data from ASD patients from the global, multi-site ABIDE dataset (Autism Brain Imaging Data Exchange). Social impairments and repetitive behaviors are hallmarks of the brain condition known as autism spectrum disorder (ASD). ASD affects one in every 68 kids in the USA, as of the most recent data from the Disease Control Centers. To understand the neurological patterns that arose from the categorization, we looked into functional connectivity patterns that can be used to diagnose ASD participants precisely. The outcomes raised the state of the art by correctly identifying 72.10% of ASD patients in the sample vs. control patients. The classification patterns revealed an anti-correlation between the function of the brain's anterior and posterior regions; this anti-correlation supports the empirical data currently showing achingly ASD impedes communication between the livid brain's anterior and posterior areas. We found and pinpointed brain regions damn frolic, distinguishing ASD among typically developing reign according to our deep learning model. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Full Swing Logic Based Full Adder for Low Power Applications
During the design of Application-Specific Integrated Circuits, a whole adder logic circuit plays a significant role. The full adder is a fundamental part of the majority of VLSI and DSP applications. Power consumption in full adders is one of the key factors; hence it is necessary to build full adders with low power consumption. Full adders are developed in this work employing full swing AND, OR, and XOR gates and compared with pass transistor logic (PTL) based AND, OR, and XOR gates, and complementary metal oxide semiconductor logic (CMOS) based AND gate, OR gate, and XOR gate. The Mentor Graphics Tool is used to construct and simulate every planned circuit. After receiving simulation data, we compared the power consumption, delay and PDP of several complete adder-based logic designs. In the proposed full swing XOR, the power dissipation and delay is decreased by 10.5% and 9.8% respectively and hence the full swing full adder PDP is decreased by 0.6%. As compared to alternative full adder designs based on logic, full swing by using gates like AND gate, by using the OR gate, and with the help XOR gate, full adder design consumes less power and hence suitable for low power applications. 2024, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. -
Stress Level Detection in Continuous Speech Using CNNs and a Hybrid Attention Layer
This paper mainly targets stress detection by analyzing the audio signals obtained from human beings. Deep learning is used to model the levels of stress pertaining to this whole paper followed by an analysis of the Mel spectrogram of the audio signals is done. A hybrid attention model helps us achieve the required result. The dataset that has been used for this article is the DAIC-WOZ dataset containing continuous speech files of conversations between a patient and a virtual assistant who is controlled by a human counselor from another room. The best results obtained were a 78.7% accuracy on the classification of the stress levels. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Diseased Leaf Identification Using Bag-of-Features and Sigmoidal Spider Monkey Optimization
Agricultural products decide the economy of a country like India. The agricultural business has the involvement of a large population. The quality and quantity of agricultural products highly depend on environmental conditions and facilities provided to farmers. Timely and efficient detection of diseases in plants and crops is one of the most critical issues that affect crop production. Therefore, it is highly desirable to develop some cheap and easy-to-handle automated plant disease detection systems for the timely treatment of plants. Leaves are considered a primary source of information about the health of plants. In the case of plants, the disease may be easily visualized and identified by observing its effect on leaves. Therefore, this paper introduces a bag-of-features in sigmoidal spider monkey optimization to identify a diseased leaf, separating the diseased leaf from a healthy leaf. The investigational outcomes show the superiority of the anticipated technique in contrast to other meta-heuristic-based systems. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Extremal Trees oftheReformulated andtheEntire Zagreb Indices
The first reformulated Zagreb index of trees can take any even positive integer greater than 8, whereas the second reformulated Zagreb index of trees can take all positive integers greater than 47 with a few exceptional values less than 8 and 47, respectively. The entire Zagreb index is defined in terms of edge degrees and incorporates the idea of intermolecular forces between atoms along with atoms and bonds. This intricate significance of studying the entire Zagreb index led to the generalization of the first entire Zagreb index of trees. The inverse problem on the first entire Zagreb of trees gives the existence of a tree for any even positive integer greater than 46. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Comparison of Machine Learning-Based Intrusion Detection Systems Using UNSW-NB15 Dataset
Various machine learning classifiers have been employed recently to enhance network intrusion detection. In the literature, researchers have put forth a wide range of intrusion detection solutions. The accuracy of the machine learning classifiers intrusion detection is limited by the fact that they were trained on dated samples. Therefore, the most recent dataset must be used to train the machine learning classifiers. In this study, UNSW-NB15, machine learning classifiers are trained using the most recent dataset. A taxonomy of classifiers based on eager and lazy learners is used to train the chosen classifiers, such as K-Means (KNN), Polynomial Features, Random Forest (RF), and Naive Bayes (NB), Linear Regression. In order to decrease the redundant and unnecessary features in the UNSW-NB15 dataset, chi-Square, a filter-based feature selection technique, is used in this study. When comparing these machine learning classifiers, performance is measured in terms of accuracy, mean squared error (MSE), precision, recall, and F1-score with or without feature selection technique. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024. -
Barriers to Smart Home Technologies in India
Smart home technologies (SHT) are critical for effectively managing homes in a digital society. However, SHTs face challenges related to their limited use in developing country contexts. This study investigates the factors that act as barriers to SHT adoption among individuals in Bengaluru, India. The roles of perceived risk, performance and after-sale service, and demographics in using smart home technologies (SHT). This study used the data from the primary survey of 133 respondents. The collected data were analyzed using regression analysis. The results supported five of the proposed hypotheses, namely, perceived performance risk, perceived financial risk, perceived psychological risk, and technological uncertainty, which influence the Behavioral intention to adopt SHT. However, service intangibility is influenced by performance risk. Income and age influence the psychological risk and adoption of SHT. The study identifies the barriers to SHT adoption. The supportive environment for SHT needs to be strengthened to reduce the associated risks. IFIP International Federation for Information Processing 2024. -
Analysis of Reinforced Concrete Structure Subjected to Blast Loads Without and with Carbon Fibres
In the past few decades, the terrorist attack on buildings has significantly increased. Blast loads due to explosions cause severe damage to the buildings structural and non-structural elements which may also lead to progressive collapse of the building. Hence, there is a need for the structures to be analysed and designed for blast loads in addition to the conventional loads. An investigation is undertaken to minimize the damage of a G+3 storied building and by improving the mechanical properties such as compressive strength, nonlinear behaviour of M40 grade concrete by adding carbon fibres in different dosages. A finite element model of G+3 storied building has been created using Ansys/LS Dyna to analyse the structure subjected to a blast load with charge weights of 50 kg, 100 kg, 150 kg at 3000 mm standoff distance. The lateral deflections and strains of the structure are determined for different charge weights to study the behaviour of the structure when subjected to blast loads. The addition of carbon fibres has improved the behaviour of structure by reducing the strains and deflections and optimum dosage of fibres is also determined in this paper. 2023, Springer Science and Business Media Deutschland GmbH. All rights reserved.