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Full Reference Image Quality Assessment (FR-IQA) of Pre-processed Structural Magnetic Resonance Images
Deep learning-based Artificial Intelligence algorithms have surpassed human-level performance in many fields including medicine. Specifically in diagnosis using radiology images, deep neural networks empowered AI to excel by educating intricate nonlinear relationships which is a core part of the complicated radiology problems. However, these models require a massive amount of quality data for training. The accuracy of the deep learning model is based on the amount of training data and the quality of the trained data being fed. So, preprocessing the data from different capturing devices is inevitable. This study aimed to highlight some of the image quality metrics that can be used to quantify the efficiency of the chosen preprocessing pipeline. By quantifying the result of each preprocess step, the user can choose an optimal set of preprocesses that can greatly improve the image quality, leading to a high and accurate diagnosis through a deep learning model. Thus, this study detailed how the full reference image quality metrics can be used to validate the performance of sMRI preprocess tasks. 2024 IEEE. -
Label-Based Feature Classification Model for Extracting Information with Dynamic Load Balancing
Efficient extraction of information from various sources is very tedious. Achieving this requires very sophisticated feature classification model and ability of the system to adapt to changing environments of data and its random distributions with an efficient use of computational resources. Label-based feature classification model (LFCM) with dynamic load balancing is proposed to address an efficient model to extract information in data set. This technique is effective in data analysis to discover the new feature set. Label approach incorporates unique label concept and it avoids any data duplication using labels. Each data sample is assigned to only one label to improve the accuracy and effectiveness of the retrieval process. Based on the data relevancy and specific features that can be extracted using proposed algorithm, classification model and semantic representation of data in vector form minimizes the data loss, and dimensionality reduction plays a vital role in building an efficient model. Various graphs and results obtained from the experiments show an improvement of information extraction using this proposed labeled LFCM approach. This approach brings lots of real time challenges that are handled to bring accuracy factor as the main focus in this proposed system. Both classification and extraction uses different model to obtain the intended results. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Secure Bitcoin Transaction and IoT Device usage in Decentralized Application
In the recent years, there has been a boom in the number of connected devices due to developments in the field of Internet of things. This has also increased the requirements of security specification. The proposed method is introducing a secure information transmission system by using Blockchain technology. Blockchain is a relatively new technology which was introduced by stoshi nakamoto, which was also the basis for developing crypto currency [bitcoin]. Crypto currencies are made transparent and secure using their network architecture, which is a combo of a decentralized and distributed network. In this paper is try to exploit the same methodology used in crypto currencies to develope an IOT network, where the devices can talk to their peers in a secure manner. They explored all the different networks and features of developing a Decentralized application that is named as Dapp. 2018 IEEE. -
Emoji Sentiment Analysis of User Reviews on Online Applications Using Supervised Machine Learning
Analyzing the sentiment behind emojis can provide valuable insights into the emotional context and user sentiment associated with textual content. To conduct a comparative analysis of diverse supervised machine learning models that can achieve the highest level of accuracy in Emoji Sentiment Analysis is the purpose of this research. Five machine learning models used in this research are K-Nearest Neighbors (KNN), Artificial Neural Network (ANN), Logistic Regression, Naive Bayes, and Random Forest. The experimental process resulted in ANN and KNN models giving an accuracy of 92%. The ANN model shows its proficiency in effectively managing large datasets. ANN also supports fault tolerance. The KNN model refrains from conducting calculations during the training phase and only constructs a model when a query is executed on the dataset. This characteristic makes KNN particularly well-suited for data mining. Both ANN and K-NN excelled in the experimental study due to these distinctive attributes. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Application of Regression Analysis of Student Failure Rate
The education sector has been rapidly growing and is currently facing several challenges. One such challenge is identifying students who are at risk of failing, as this can help educators provide targeted interventions to improve student performance. Machine learning models have been developed to predict the probability of student failure based on various student performance metrics to address this issue. In this paper, we present a regression-based model that predicts the probability of student failure using student performance metrics such as attendance, previous academic performance, and demographic information. The model was trained on a dataset of students and achieved high accuracy in predicting the probability of student failure. While the model performs well in predicting the probability of student failure, there is always room for improvement. Possible enhancements to the model include feature engineering, ensemble learning, hyperparameter tuning, deep learning, and interpretability. These enhancements can improve the models accuracy, stability, and transparency, leading to better predictions and targeted interventions for at-risk students. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Soft Computing Approach for Student Dropouts in Education System
The education system has increased the number of dropouts in the coming years, decreasing the number of educated people. Education system refers to a group of institutions like ministries of education, local education bodies, teacher training institutes, universities, colleges, schools, and more whose primary purpose is to provide education to all the people, especially young people and children in educational settings. The research aims to improve the student dropout rate in the education system by focusing on students performance and feedback. The students dropout rate can be calculated based on complexity, credits, attendance, and different parameters. This study involves the extensive study that inculcates student dropout with their performance and other parameters with soft computing approaches. There are various soft computing approaches used in the education system. The approaches and techniques used are sequential pattern mining, sentimental analysis, text mining, outlier decision, correlation mining, density estimation, etc. The approaches and techniques will be beneficial to calculating and decreasing the rate of dropout of students in the education system. The research will make a unique contribution to improved education by calculating the dropout rate of students. In particular, we argue that the dropout rate is increasing, so soft computing techniques can be the solution to improvise/reduce the dropout rate. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Nano ZnO@PEG catalyzed one-pot green synthesis of pyrano[2,3-d] pyrimidines in ethanol via one-pot multicomponent approach
A facile one-pot multicomponent protocol for the synthesis of bio-active Pyrano[2,3-d]pyrimidine derivatives by a one- step condensation reaction of substituted aldehyde, malononitrile/methyl cyanoacetate, barbituric acid has been demonstrated using nano ZnO@PEG as a catalyst at room temperature. The present approach offers several advantages, such as shorter reaction time, higher yields, and environmental friendliness. Easy isolation of products, absence of column chromatographic purification, use of commercially available low-cost starting materials and reusability of the catalyst make the methodology viable in organic synthesis. 2020 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Second International Symposium ''Functional Nanomaterials in Industrial Applications: Academy - Industry Meet''. -
Synthesis of 1, 8-Naphthyridine-3-Carbonitriles under solvent-free conditions using ceric ammonium nitrate
1,8-naphthyridines are synthesized using a four-component, one-pot approach. This method includes the reaction of aromatic aldehyde, malononitrile, 1,6-dimethylpyridin-2(1H)-one, substituted aniline in a solvent-free condition catalyzed by Ceric Ammonium Nitrate (CAN). Contrary to the reported literature, this distinct method houses several promising factors to the same degree as solvent-free reaction conditions, shorter reaction duration, excellent yields, and a straightforward extraction process. 2023 Elsevier Ltd. All rights reserved. -
An insight into the superior performance of ZnO@PEG nanocatalyst for the synthesis of 1,4-dihydropyrano[2,3-c]pyrazoles under ultrasound
The investigation presents a straightforward synthesis of fifteen 1,4-dihydropyrano[2,3-c]pyrazoles using ZnO@PEG nanocatalyst in ethanol via Multicomponent approach under the influence of ultrasound. The present methodology successively tolerates a variety of functional groups and offers several advantages such as excellent yields without chromatographic purification, milder reaction conditions, shorter reaction times, and the use of an environmentally benign reusable catalyst. Ecstatically, the reaction was successfully scaled to gram level ascertaining the wider applicability of ZnO@PEG nanoparticles in multicomponent reactions. 2019 Elsevier Ltd. All rights reserved. -
Relevance of psychophysiological and emotional features for the analysis of Human behavior-A Survey
With fresh development in the area of artificial intelligence and machine learning, the analysis of human physiological and psychological behavior has increased greater attention around the world. In this paper, we have provided a detailed survey of the approaches used for human behavior detection considering different modalities with physiological behavior, psychological behavior, and emotion detection with the help of sensors EEG, ECG, GSR, and temperature. At long last, it finishes up with the results of this study and represents the thoughts for future exploration in the zone of human behavior understanding. A rundown and comparison among the ongoing investigations done, that uncovers the currently existing issues and the future work has been examined. The Electrochemical Society -
Examining the Benefits of AI in Wearable Sensor-based Healthcare Solutions
The emergence of the AI generation has introduced adjustments to the manner healthcare solutions are advanced and applied. Wearable sensor-based total healthcare answers were revolutionized by leveraging AI in diverse packages. AI may be used to enhance the accuracy and precision of facts analysis, simplify information collection procedures, and pick out affected person-unique styles from the accrued data. Furthermore, AI can provide both actual-time and predictive analytics abilities, which are particularly useful for devising personalized healthcare offerings. Its provision of scalable systems hurries up the traits of various programs and has enabled personalized healthcare answers to be deployed in a shorter length. In spite of a number of the associated challenges, including data privacy troubles, AI-based wearable sensor-based healthcare answers can revolutionize patient tracking and timely detection of capability health conditions, improve preventive fitness care, and decrease healthcare fees. 2024 IEEE. -
Impact of e-commerce on India's exports and investment
E-commerce has become an important mode of trade, both domestically and internationally. E-commerce provides a platform for exchange of goods and services and thus directly alters the cost of trade and profits of firms, while simultaneously, generates a demand for a different set of skilled managers and creates opportunities for increasing investment and thereby affects the volume of domestic and international trade and in-turn affects the overall level of output and employment in an economy. There are empirical evidences on how certain developed countries like UK, USA, earlier, and lately developing countries like China, have leveraged e-commerce to enhance international trade. This paper attempts to contribute to the literature by studying the impact of e-commerce on India's international trade, especially exports, and investment which in-turn impact the level of output/gross domestic product (GDP) and employment in the country. Copyright 2021 Inderscience Enterprises Ltd. -
Water Demand Prediction Using Support Vector Machine Regression
Water is a critical resource for sustainable economic and social development of a country. To maintain health hygiene, energy agricultural products, and the environment management water plays a key role. Water demand prediction is essential to analyze the requirement that indicate emergency state for water management decisions. This paper explores the water usage data for dairy plants to understand the spatial and temporal patterns for future water requirements, to optimize the water demand estimation. It uses concept of Machine learning algorithms to compare and achieve an effective and reliable system for water prediction. 2019 IEEE. -
Synthesis and characterization of graphene filled PC-ABS filament for FDM applications
Present investigation focuses on development of graphene filled PC-ABS filament for Fused Deposition Modeling applications. Compounding and twin screw extrusion was employed to synthesis graphene filled FDM filament of 1.75mm diameter. Percentage of graphene was varied from 0.1 vol% to 0.25 vol% in steps of 0.05. Developed filaments were subjected to SEM studies, dimensional accuracy and density measurements. In order to achieve filament of 1.75mm diameter, filament extrusion temperature was optimized using Taguchi's L25 orthogonal array, microstructure shows homogeneous dispersion of graphene particles in PC-ABS matrix, density decreases with increased content of graphene particles. 2018 Author(s). -
Allometry Authentication in the Field of Finance: Creation of Well Secured System using AI Algo Based Systems
It is true the banking sector is increasingly under pressure to tighten security in an ever-changing digital arena, even as the customer experience needs to be strengthened. Thus, the use of biometric authentication through enhanced AI-driven systems that would enhance the security protocols while at the same time smoothening the users' interactions was a promising way in response. The paper that follows explores the integration of biometric authentication within banking systems in a bid to make clear its effectiveness in relation to reinforcing security and enhancing user experience. Accordingly, bijson etal. argue that biometric security fits perfectly in banks, since with the increasing cyber threats, banks are bound to deploy more advanced security mechanisms. These traditional means, suchjson, use of passwords and PINs, have shown vulnerabilities that are liable to exploitation and should be changed into something much more resilient. The authentication under biometrics also validates a user's identity by basing it on unique physiological or behavioral traits, such as a fingerprint, features of the face, patterns of the iris, and the voice. Biometric systems authenticate users with a very high level of confidence through AI-based algorithms, averting the security risks associated with unauthorized access and identity theft. Further, biometric authentication overcomes the flaws that prevail with the traditional mode of methods and hence, it ensures a very comfortable and user-friendly mode of system security. 2024 IEEE. -
Detection of Malicious Nodes in Flying Ad-hoc Network with Supervised Machine Learning
An Ad-hoc network (FANET) is a new upcoming technology which has been used in several sectors. Ad-hoc networks are mostly wireless local area networks (LANs). The devices communicate with each other directly instead of relying on a base station or access points as in wireless LANs for data transfer. In an Ad-hoc network the communication between one node to another in a FANET is not secured and there isn't any authorized protocol for secured communication. Therefore, we suggest an algorithm to detect the malicious node in a network. This algorithm uses Linear regression to calculate the reputation or trust value of a node in the network. Then the above found trust value is used to classify the node as normal node or malicious node based on the Logistic Regression Classification. Thus, allowing a secure communication of data and avoiding attacks. 2022 IEEE. -
Detection of colorectal cancer using dilated convolutional network via Raman spectra
Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Early detection plays a crucial role in improving patient outcomes and reducing mortality rates. In recent years, Raman spectroscopy has emerged as a promising tool for non-invasive cancer detection. This research introduces a new method for identifying colorectal cancer (CRC). It combines Raman spectroscopy, a technique that analyzes the molecular fingerprint of tissues, with a powerful deep learning algorithm called a dilated convolutional network (DCN). By combining these two tools, the researchers aim to improve the accuracy and reliability of diagnosing CRC. Intraoperative diagnostics and pathology need to distinguish tumors from normal tissues. This proposal explores Raman spectroscopy as a new surgical tool for identifying colorectal cancer during surgery. Raman spectroscopy offers a way to directly analyze the makeup of tissue, potentially revealing the presence of cancer. However, surrounding tissue can create background interference, making it difficult to detect the key signal. The authors suggest that high-quality data from Raman spectroscopy combined with advanced deep learning algorithms could be a solution to overcome this challenge. We collect a large Raman spectroscopy dataset from 26 colorectal cancer patients with Raman shifts from 385 to 1545 cm-1. Second, dilated convolutional networks classify colorectal cancer tumour tissues. Following the deep learning model's output, we proceed by visualizing and analyzing the identified fingerprint peaks. Our deep learning algorithm exceeds previous colorectal cancer detection methods with 99.1% accuracy. Colorectal cancer detection using Raman spectra is unique. Our ensemble DCN could classify colorectal tumour and normal tissue Raman spectra. 2024 Author(s). -
Performance Analysis of Logical Structures Using Ternary Quantum Dot Cellular Automata (TQCA)-Based Nanotechnology
Ternary Quantum-Dot Cellular Automata (TQCA) is a developing nanotechnology that guarantees lower power utilization and littler size, with quicker speed contrasted with innovative transistor. In this article, we are going to propose a novel architecture of level-sensitive scan design (LSSD) in TQCA. These circuits are helpful for the structure of numerous legitimate and useful circuits. Recreation consequences of proposed TQCA circuits are developed by utilizing such QCA designer tool. In realization to particular specification, we need to find the parameter values by using Schrodinger equation. Here, we have optimized the different parameter in the equation of Schrodinger. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Secure Communication Gateway with Parity Generator Implementation in QCA Platform
Quantum-Dot Cellular Automata (QCA) has arisen as a potential option in contrast to CMOS in the late time of nanotechnology. Some appealing highlights of QCA incorporate incredibly low force utilization and dissemination, high gadget pressing thickness, high velocity (arranged by THz). QCA based plans of normal advanced modules were concentrated broadly in the ongoing past. Equality generator and equality checker circuits assume a significant part in blunder discovery and subsequently, go about as fundamental segments in correspondence circuits. In any case, not very many endeavors were made for an efficient plan of QCA based equality generator as well as equality checker circuits up until now. In addition, these current plans need functional feasibility as they bargain a ton with normally acknowledged plan measurements like territory, postponement, intricacy, and manufacture cost. This article depicts new plans of equality generator and equality checker circuits in QCA which beat every one of the current plans as far as previously mentioned measurements. The proposed plans can likewise be effortlessly reached out to deal with an enormous number of contributions with a straight expansion in territory and inactivity. 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Employing Deep Learning in Intraday Stock Trading
Accurate stock price prediction is a significant benefit to the Stock investors. The future Stock value of any company is determined by Stock market prediction. A successful prediction of the stock's future price could result in a significant profit; Hence investors prefer a precise Stock price prediction. Although there are many different approaches to helps in forecasting stock prices, this paper will briefly look into the deep learning models and compare LSTM model and its variants. The key intention of this study is to propose a model that is best suitable and can be implemented to forecasting trend of stock prices. This paper focuses on binary classification problem, predicting the next-minute price movement of SPDR SP 500 index The testing experiments performed on the SPDR SP 500 index reveals that the variants of LSTM models, Slim LSTM1, slim LSTM2, and Slim LSTM3 with less parameters, provide better performance when compared to the Standard LSTM Model. 2020 IEEE.