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Quantitative Structure-Activity Relationship Modeling for the Prediction of Fish Toxicity Lethal Concentration on Fathead Minnow
As there has been a rise in the usage of in silico approaches, for assessing the risks of harmful chemicals upon animals, more researchers focus on the utilization of Quantitative Structure Activity Relationship models. A number of machine learning algorithms link molecular descriptors that can infer chemical structural properties associated with their corresponding biological activity. Efficient and comprehensive computational methods which can process huge set of heterogeneous chemical datasets are in demand. In this context, this study establishes the usage of various machine learning algorithms in predicting the acute aquatic toxicity of diverse chemicals on Fathead Minnow (Pimephales promelas). Sample drive approach is employed on the train set for binning the data so that they can be located in a domain space having more similar chemicals, instead of using the dataset that covers a wide range of chemicals at the entirety. Here, bin wise best learning model and subset of features that are minimally required for the classification are found for further ease. Several regression methods are employed to find the estimation of toxicity LC50 value by adopting several statistical measures and hence bin wise strategies are determined. Through experimentation, it is evident that the proposed model surpasses the other existing models by providing an R2 of 0.8473 with RMSE 0.3035 which is comparable. Hence, the proposed model is competent for estimating the toxicity in new and unseen chemical. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Quantum Computing: Navigating The Technological Landscape for Future Advancements
Quantum Computing represents a transformative paradigm in information processing, leveraging principles of quantum mechanics to enable computations that transcend the limitations of classical computing. This research paper explores the cutting-edge technologies employed in Quantum Computing, examining the key components that facilitate quantum information processing.The purpose of this study is to provide a comprehensive exploration of the state-of-the-art technologies in Quantum Computing, laying the groundwork for future advancements and applications in this rapidly evolving field.The methodology employed in this study integrates three analytical approaches: sentiment analysis, topic modeling, and thematic analysis. Sentiment analysis is utilized to discern and quantify emotional tones within the content. Topic modeling is applied to identify latent themes and patterns within the data, revealing underlying structures. Thematic analysis, on the other hand, involves a systematic identification and exploration of recurrent themes to provide a nuanced understanding of the subject matter. This tripartite methodology ensures a comprehensive examination of the data, facilitating a robust and multifaceted analysis of quantum computing technologies. 2024 IEEE. -
Quantum Computings Path toSupremacy: Progress in the NISQ Epoch
Quantum computing leverages the principles of quantum mechanics for information processing, with qubits serving as the fundamental units of quantum information. Qubits are quantum states where information processing can be engineered. Qubits possess the unique ability to encode, manipulate and extract information, enabling remarkable parallelism in computation. This enhanced computational speed, called quantum supremacy, promises to transcend established complexity boundaries. Significant strides have been made in demonstrating quantum supremacy through various experiments, most notably Googles 2019 experiment utilizing the Sycamore quantum processor to solve a problem that would stymie classical supercomputers for millennia. Other research groups, such as the Chinese team employing Jiuzhang and Zuchongzhi quantum processors, have achieved similar feats, showcasing the profound computational capabilities of quantum computers. It is essential to underscore that quantum supremacy does not signify quantum computers superiority across all tasks; current quantum computers remain constrained in their applicability, excelling primarily in specific problem domains. Nevertheless, recent advancements in quantum computing are noteworthy and ongoing development promises to expand their problem-solving capacities. This paper offers an introductory overview of quantum computing and an assessment of three prominent quantum supremacy experiments. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Quantum Convolutional Neural Network for Medical Image Classification: A Hybrid Model
This study explores the application of Quantum Convolutional Neural Networks (QCNNs) in the realm of image classification, particularly focusing on datasets with a highly reduced number of features. We investigate the potential quantum computing holds in processing and classifying image data efficiently, even with limited feature availability. This research investigates QCNNs' application within a highly constrained feature environment, using chest X-ray images to distinguish between normal and pneumonia cases. Our findings demonstrate QCNNs' utility in classifying images from the dataset with drastically reduced feature dimensions, highlighting QCNNs' robustness and their promising future in machine learning and computer vision. Additionally, this study sheds light on the scalability of QCNNs and their adaptability across various training-test splits, emphasizing their potential to enhance computational efficiency in machine learning tasks. This suggests a possibility of paradigm shift in how we approach data-intensive challenges in the era of quantum computing. We are looking into quantum paradigms like Quantum Support Vector Machine (QSVM) going forward so that we can explore trade offs effectiveness of different classical and quantum computing techniques. 2024 IEEE. -
Quantum Information Processing for Legal Applications through Bloch Sphere of Law
The objective of the research work is to propose a quantum information processing model (QIP) for legal applications including litigation and investigation phases. The quantum information processing and quantum computing concepts can be visualized within a Bloch Sphere of Law (BSL) as legal Bloch vectors (LBV) as quantum computing entities. This quantum approach is needed since the complexity of legalities and the legal objects involved in the final judgement are to be reversible with a lot of uncertainties. The reasoning and prosecution through various trials and investigations are to be considered as mathematical matrix or unitary operations in this muti dimensional legal space. The mapping of legal information into technical and then vectorial representations are deployed through a glossary of legal terms in this quantum paradigm. As a forerunning study and application in the quantum paradigm, mathematical and computational models have been proposed in the work with a case study of a recent civil case. 2022 IEEE. -
Quantum vs. Classical: A Rigorous Comparative Study on Neural Networks for Advanced Satellite Image Classification
Navigating the intersection of quantum computing and classical machine learning in image classification, this study confronts prevailing challenges. Centered on the "Satellite Image dataset (RSI-CB256),"our investigation probes the early phases of quantum architectures, utilizing simulations to transform numerical data into a quantum format, the investigation highlights the existing limitations in traditional classical methodologies for image classification tasks. In light of the groundbreaking possibilities presented by quantum computing, this study underscores the need for creative solutions to push image classification beyond the usual methods. Additionally, the study extends beyond conventional CNNs, incorporating Quantum Machine Learning through the Qiskit framework. This dualparadigm approach not only underscores the limitations of current classical machine learning methods but also sets the stage for a more profound understanding of the challenges that quantum methodologies aim to address. The research offers valuable insights into the ongoing evolution of quantum architectures and their potential impact on the future landscape of image classification and machine learning. 2024 IEEE. -
Radar Cross Section (RCS) of HIS-based Microstrip Patch Array: Parametric Analysis
Low profile structures such as High Impedance Surfaces (HIS) are capable of modifying the scattering properties of a radiating structure. This paper presents the novel design of patch antenna/array with non-uniform HIS based ground plane. Two FSS elements of different dimensions are designed with different resonant frequencies. The performance of the high impedance surfaces has been carried out by varying the HIS dimensions and height of the substrate. Using the analyses, patch antenna/array with ground plane based on non-uniform configurations of HIS elements are designed. The radiation and scattering characteristics of microstrip patch antenna/array with HIS- based ground plane are compared to those with conventional PEC-based ground plane. A maximum of 8 dB RCS reduction has been achieved for patch array with non-uniform HIS layer. 2018 IEEE. -
Radon transform processed neural network for lung X-ray image based diagnosis
A novel method for image diagnosis with artificial learning is presented-ray images tuberculosis patients is subjected to neural network learning for prediction of diagnosis. The X-ray images of lungs are normally difficult for diagnosis, since its similarity to lung cancer. Under and over diagnosis of lung X-ray images is a difficult medical problem to resolve. In the present work radon transform of the x-ray images is fed to back propagation neural network trained with Levenberg algorithm. The present methodology gives sharp results, distincting the normal and abnormal images. 2014 IEEE. -
Raman spectrum of graphite layers in Indian coal
Two Indian coals of different rank (bituminous and subbituminous coal) have been demineralized by chemical method. Fourier transform Raman spectroscopy studies have been performed to study the changes in functional groups. Well resolved G peak is observed at 1605 cm-1 and 1590 cm-1 both in bituminous coal and subbituminous coal. With HF leaching, this doublet is reduced to a singlet along with reduction of frequency to 1585 cm -1 in subbituminous coal, where as in bituminous coal the absorption become very distinct. Bituminous coal is showing more intense absorption with HF leaching in this region where as subbituminous coal is shown a reduction in intensity. G' band is observed at ? 2700 cm-1 with almost the same intensity as that of G band. This confirms the presence of multilayer formation of graphite layer. The defect band at 1355 cm-1 is due to benzene or condensed benzene rings present in amorphous carbon. This band is weak in the present study. This is mainly due to immature nature of subbituminous coal than the higher rank bituminous coal. Graphite structure is remained behind after chemical leaching liberated oxygenated functional groups and mineral groups. The decrease of ID/IG ratio indicates that graphitization is increased in bituminous coal. 2011 American Institute of Physics. -
RASK: Request authentication using shared keys for secured data aggregation in sensor network
Accomplishing a robust security features to resists lethal attacks is still an open research area in wireless sensor network. The present paper review existing security techniques to find that there is still a trade-off between cryptographic-based security incorporations and communication performance. Moreover, we have identified that majority of the existing system has not emphasized on first line of defense i.e. security the route discovery process that can act as a firewall for all forms of illegitimate nodes existing in the network. The proposed study introduced RASK i.e. Request Authentication using Shared Key, which is a novel concept developed using simple quadratic formulation of generating keys for encrypting the message during data aggregation. The study outcome has been significantly benchmarked with recent studies and existing cryptographic standards to find RASK outperform existing techniques. Springer International Publishing AG 2017. -
Rating of Online Courses: A Machine Learning Based Prediction Model
Online courses market has provided an economical and easy access to knowledge. When it comes to make a decision related to purchase of online course, little is known about what attributes can be depended upon to guess the quality of an online course. Ratings for online courses act as a reliable signal for assessing the quality of a course. The study discusses the prediction of ratings for online courses using Artificial Neural Network based on Particle Swarm Optimization (ANN-PSO). The experimental results suggests that ANN-PSO model has the capacity to predict the ratings for online courses on the basis of its attributes with accuracy. 2021 IEEE. -
Rating-Based Cyberbullying Detection with Text, Emojis on Social Media
In the dynamic landscape of online interactions, cyberbullying has become pervasive, profoundly impacting user's digital well-being. Public figures, especially celebrities and influencers, face heightened vulnerability to online harassment, exacerbated by the post-pandemic surge in social media usage. To address this challenge, our research adopts a holistic approach to detect cyberbullying in text, considering both textual content and the nuanced expressions conveyed through emojis on social media platforms. We employed a diverse set of machine learning and deep learning models, including Support Vector Classifier, Logistic Regression, Random Forest, XGBoost, LSTM, Bi-LSTM, GRU, and Bi-GRU, to accurately classify non cyberbullying or cyberbullying text. Beyond classification, our study introduces an offensive rating system, assigning severity ratings on a 1-5 scale to identify cyberbullying instances. A critical aspect is the establishment of a threshold value which depends on user security and safety ethics of different social media platforms; texts surpassing this trigger an automatic recommendation to block the user, ensuring a proactive response to minimize harm. This recent contribution not only comprehensively addresses cyberbullying but also empowers society. 2024 IEEE. -
Rational suitability of low cost activated carbon in removing hexavalent chromium ions from wastewater by uninterrupted mode of adsorption
Heavy metals such as chromium, lead, arsenic and others are dense metals whose contamination of water may exterminate life on earth at the niche in industrial activities, foodstuffs or medicines and so on. Activated carbons are very helpful in removing heavy metal ions from aqueous solutions by adsorption, and have been investigated by many researchers so far. The practical relevance of activated carbon made from de oiled soya in the removal of hexavalent chromium ions through continuous adsorption mode is reported in this paper. A breakthrough plot was plotted in finding the effect of initial concentration and adsorbent bed height in the adsorption of hexavalent chromium through activated carbon of de oiled soya. The breakthrough time and saturation time increased as the concentration of the initial solution shot up from 40 mg/L to 60 mg/L. The saturation time was in an incremental mode when the thickness of the adsorbent bed height in a fixed bed was increased from 5cm to 7cm for 40 mg/L initial concentration of hexavalent chromium. The Adams-Bohart's model was found to fit perfectly the fixed bed column in the removal of hexavalent chromium from aqueous solutions. The fabricated adsorbent worked well in detoxifying hexavalent chromium metal ion contaminated wastewater. 2020 Published under licence by IOP Publishing Ltd. -
Reading behind the tweets: A sentiment Clustering Approach
Market sentiment influence crude oil future prices in direct or indirect way. In order to measure the polarity of market sentiment various techniques has been deployed by industry and academia alike. This pilot study successfully introduced two instruments, namely topic modeling and Sentiment clustering, to unearth the prevailing sentiments behind crude oil future pricesThree main conclusions that can be drawn from empirical results are. First, the K-Means clustering algorithm is an effective technique for sentiment clustering compared to Louveian and MDS clustering techniques. Second sentiment polarity-related positive sentiments have shown more variations in comparison to neutral and negative sentiments. Third It is possible to extract the keywords related to essential factors influencing crude oil prices using the LDA technique under topic modeling 2022 IEEE. -
Real time conversion of sign language to speech and prediction of gestures using artificial neural network
Sign language is generally used by the people who are unable to speak, for communication. Most people will not be able to understand the Universal Sign Language (unless they have learnt it) and due to this lack of knowledge about the language, it is very difficult for them to communicate with mute people. A device that helps to bridge a gap between mute persons and other people forms the crux of this paper. This device makes use of an Arduino Uno board, a few flex sensors and an Android application to enable effective communication amongst the users. Using the flex sensors, gestures made by the wearer is detected and then according to various pre-defined conditions for the numerous values generated by the flex sensors, corresponding messages are sent using a Global System for Mobile(GSM) module to the wearer?s android device, which houses the application that has been designed to convert text messages into speech. The GSM module is also used to send the sensor inputs to a cloud server and these values are taken as input parameters into the neural network for a time series based prediction of gestures. The system is designed to be a continually learning device and improve reliability by monitoring every individual?s behaviour at all times. 2018 The Authors. Published by Elsevier B.V. -
Real-Time Application of Document Classification Based on Machine Learning
This research has been performed, keeping a real-time application of document (multi-page, varying length, scanned image-based) classification in mind. History of property title is captured in various documents, recorded against the said property in all the countries across the world. Information of the property, starting from ownership to the conveyance, mortgage, refinance etc. are buried under these documents. This is by far a human driven process to manage these digitized documents. Categorization of the documents is the primary step to automate the management of these documents and intelligent retrieval of information without or minimal human intervention. In this research, we have examined a popular, supervised machine learning technique called, SVM (support vector machine) with a heterogeneous data set of six categories of documents related to property. The model obtained an accuracy of 88.06% in classifying over 988 test documents. 2020, Springer Nature Switzerland AG. -
Real-Time Cyber-Physical Risk Management Leveraging Advanced Security Technologies
Conducting an in-depth study on algorithms addressing the interaction problem in the fields of machine learning and IoT security involves a meticulous evaluation of performance measures to ensure global reliability. The study examines key metrics such as accuracy, precision, recall, and F1 scores across ten scenarios. The highly competitive algorithms showcase accuracy rates ranging from 95.5 to 98.2%, demonstrating their ability to perform accurately in various situations. Precision and recall measurements yield similar information about the model's capabilities. The achieved balance between accuracy and recovery, as determined by the F1 tests ranging from 95.2 to 98.0%, emphasizes the practical importance of data transfer in the proposed method. Numerical evaluation, in addition to an analysis of overall performance metrics, provides a comprehensive understanding of the algorithm's performance and identifies potential areas for improvement. This research leads to advancements in the theoretical vision of machine learning for IoT protection. It offers real-world insights into the practical use of robust models in dynamically changing situations. As the Internet of Things environment continues to evolve, the study's results serve as crucial guides, laying the foundation for developing strong and effective security systems in the realm of interaction between virtual and material reality. The Author(s) 2024. -
Real-Time Fire Detection Through the Analysis of Surveillance Videos
The Forest Fire Detection System is an intelligent system that can detect forest fires and alert authorities in real-time. It uses a YOLOv5 deep learning algorithm to process live video feeds captured by a web camera which is trained with the sizable dataset of inputs to locate the fire accurately, making it an ideal choice for real-time fire detection in the forest. Upon detecting a fire, the system sends an email alert to a designated email address, containing a picture of the fire and location information. The email alert system is built using the standard SMTP protocol, which ensures that the message is delivered to the recipient in a timely and reliable manner. The system is also equipped with a speaker that triggers an alarm upon detecting a fire. The alarm is designed to alert people in the vicinity of the fire so that they can take the necessary action. It is activated using the Pygame library, a collection of Python modules specifically crafted for game development across multiple platforms. Overall, the Forest Fire Detection System is a fast, efficient, and accurate system that can help prevent the spread of forest fires. It is an intelligent system that can detect fires quickly and send alerts to authorities, giving them the information they need to take the necessary action to control the fire. The system is built using a web camera, a computer, and a speaker, making it easy to install and maintain. 2024 IEEE. -
Real-time Litter Recognition Using Improved YOLOv4 Tiny Algorithm
Littered roads have become a familiar sight in India. The main reason is the increasing population and inefficient waste disposal system. Since garbage collectors cannot pick litter in all the places, there is a need for an efficient way to detect it. Hence, a machine learning-based object detection model is used. In this, we have applied an improved YOLOv4-Tiny algorithm to detect the garbage, classify it and make the detection process easier on custom datasets. We have improved the algorithm in terms of the object prediction time, this is done by replacing a max pooling layer with one of two layers present in a fully connected layer. When an input is given, the algorithm detects the litter in the image with a bounding box around it along with the label and confidence score. The proposed model reduces the prediction time by 0.517 milliseconds less than the original algorithm employed which concludes that the object is predicted faster. 2022 IEEE. -
Real-Time State of Charge Prediction Model for Electric Two-Wheeler
To maximise the efficiency and performance of electric vehicles, traction battery State of Charge (SoC) must be accurately predicted. In this work, a prediction model for traction battery State of Charge estimation is developed in real time. The traction battery powers an electric two-wheeler through a predetermined drive cycle. To produce accurate state-of-charge forecasts, the predictive model considers several input characteristics, such as temperature, voltage, and current. This research is crucial for fostering effective energy management and improving the safety and dependability of electric two-wheelers. Open-circuit voltage (OCV) and coulomb counting are two commonly utilised techniques used to evaluate the state of charge prediction model. These techniques act as standards for assessing the developed Neural Network model prediction, the model's dependability and accuracy. The model's usefulness and its potential to outperform the current State of Charge estimating techniques are demonstrated by comparing the state-of-charge predictions from the model with these standard methods. 2024 IEEE.