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Detection of time delay between UV and X-ray variability in Mrk 1044 using AstroSat observations
Active galactic nuclei are known to exhibit flux variations across the entire electromagnetic spectrum. Among these, correlations between UV/optical and X-ray flux variations serve as a key diagnostics for understanding the physical connection between the accretion disk and the corona. In this work, we present the results of analysis of ultraviolet (UV) and X-ray flux variations in the narrow line Seyfert 1 galaxy Mrk 1044. Simultaneous observations in the far-UV band (FUV: 1300 - 1800 and the X-ray band (0.5 - 7 keV) obtained during 31 August - 8 September 2018 with the Ultraviolet Imaging Telescope and the Soft X-ray Telescope onboard AstroSat were used for this study. Significant flux variability was detected in both FUV and X-ray bands. The fractional root mean square variability amplitude ( F var) was found to be 0.036 0.001 in the FUV band and 0.384 0.004 in the X-ray band. To explore potential time lag between the two bands, cross-correlation analysis was performed using both the interpolated cross-correlation function (ICCF) and just another vehicle for estimating lags in nuclei (JAVELIN) methods. Results from both approaches are consistent within 2 ? uncertainty, indicating that X-ray variations lead the FUV variations, with measured lags of 2.25 0.05 days (ICCF) and 2.35?0.01+0.02 days (JAVELIN). This is the first detection of a time delay between UV and X-ray variations in Mrk 1044. The observed UV lag supports the disk reprocessing scenario, wherein X-ray emission from the corona irradiates the accretion disk, driving the observed UV variability. 2026 Elsevier B.V. -
Detection of strangers based on dogs sound
Nowadays, people having a pet at home are increasing. Usually, dog is the favorite pet animal for most of the people in the world. Dogs are more capable of identifying strangers in the surroundings than humans. The proposed work identifies the strangers based on the barking sound of the dog. In this anticipated work, multiple features are extracted from the dogs barking sound using Fast Fourier Transform and Statistical based methods. The classification is done using Nae Bayes classifier. The dataset contains 770 barking audio files of 8 dogs. Whenever known and unknown person comes home, the sounds of the dogs are recorded. The classification result for identifying the stranger is 79.1094%. BEIESP. -
Detection of picric acid in industrial effluents using multifunctional green fluorescent B/N-carbon quantum dots /
Journal of Environmental Chemical Engineering, Vol.10, Issue 2, ISSN No: 2213-3437.
Carbon quantum dots have recently gained widespread attention due to their excellent physicochemical features. The rapid escalation in the dumping of hazardous chemicals into water, spurred demand for developing efficient and selective sensors for toxic chemicals. Herein, we have developed a novel fluorescence sensor for picric acid which is a major pollutant in industrial effluents. The new strategy exploits the development of a fluorescence sensor based on N-doped carbon quantum dots (N-CQDs) followed by boron functionalization. The N-CQDs were synthesized in a rapid single-step microwave technique by employing L-serine and citric acid. -
Detection of picric acid in industrial effluents using multifunctional green fluorescent B/N-carbon quantum dots
Carbon quantum dots have recently gained widespread attention due to their excellent physicochemical features. The rapid escalation in the dumping of hazardous chemicals into water, spurred demand for developing efficient and selective sensors for toxic chemicals. Herein, we have developed a novel fluorescence sensor for picric acid which is a major pollutant in industrial effluents. The new strategy exploits the development of a fluorescence sensor based on N-doped carbon quantum dots (N-CQDs) followed by boron functionalization. The N-CQDs were synthesized in a rapid single-step microwave technique by employing L-serine and citric acid. Subsequent boron functionalization of N-CQDs was carried out using boric acid for the synthesis of Boron-nitrogen carbon quantum dots (B/N-CQDs). The B/N-CQDs were found to exhibit high quantum yield (24%), good water solubility, outstanding photostability features, and bright green fluorescence under UV light. The morphology of B/N-CQDs is spherical, with scattered particle sizes ranging from 2 to 8 nanometers. Furthermore, B/N-CQDs were found to be an effective fluorescence probe for the selective and sensitive detection of picric acid, with a good linear range of 37 nM-30 M and a detection limit of 1.8 nM. The Photoluminescence (PL) intensity of B/N-CQDs was selectively quenched by picric acid. The quenching mechanism was conclusively established using fluorescence lifetime decay studies. Moreover, the synthesized B/N-CQDs was successfully employed for the analysis of picric acid from industrial effluents and cell imaging with Hela cells to showcase the utility of the developed fluorescent probe. 2022 Elsevier Ltd -
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 Lung Cancer with a Deep Learning Hybrid Classifier
This article presents a deep learning framework combining a convolutional neural network (CNN) and a support vector machine (SVM) for lung cancer diagnosis. The model uses data divided into six groups: 250 images in the training set and 150 images in the test set. The work includes preliminary data and development using the Keras image data generator, VGG-16 architecture, high-level rules, and SVM classifier training with labels and vectors. The model achieves 90% accuracy with 85% selection impact and 75% cross-validation flexibility using VGG-16 and SVM hybrid classifier. This study finally revealed the classification of the model by multi-class ROC curve analysis and confusion matrix. 2024 IEEE. -
Detection of high-frequency pulsation in WR135: Investigation of stellar wind dynamics
We report the detection of high-frequency pulsations in WR 135 from short-cadence (10 minute) optical photometric and spectroscopic time series surveys. The harmonics up to the sixth order are detected from the integrated photometric flux variations, while the comparatively weaker eighth harmonic is detected from the strengths of the emission lines. We investigate the driving source of the stratified winds of WR 135 using the radiative transfer modeling code, CMFGEN, and find the physical conditions that can explain the propagation of such pulsations. From our study, we find that the optically thick subsonic layers of the atmosphere are close to the Eddington limit and are launched by the Fe opacity. The outer optically thin supersonic winds (Tross = 0.1 0.01) are launched by the He II and C IV opacities. The stratified winds above the sonic point undergo velocity perturbation that can lead to clumps. In the optically thin supersonic winds, dense clumps of smaller size (fVFF = 0.27 0.3, where fVFF is the volume filling factor) pulsate with higher-order harmonics. The larger clumps (fVFF = 0.2) oscillate with lower-order harmonics of the pulsation and affect the overall wind variability. 2024. The Author(s). -
Detection of Fungi Diseases in Tomato Leaf Using ResNet-18 Approach
Agriculture is the largest and most vital sector of our economy, providing employment to over 70% of the Indian population. In an agricultural field identification and classification of disease in a tomato plant plays a vital role. If proper care is not taken in advance with this, it causes the serious effect on the tomato plants. The plants which are affected by the disease reflect on the quality and quantity of the production as well as the economy of the country. Tomato is one of the main vegetables in India which plays an important role in providing food for public needs and its a very vulnerable plant to insects and diseases caused by different pathogens like bacteria, virus, and fungi. This paper proposes a convolutional neural network (CNN) for automatic identification of plant disease caused by fungi. The proposed model attains the results of F1 score is 91% to differentiate tomato leaves in both healthy and unhealthy conditions. Measures like a high Area Under Curve (AUC) score on the Receiver Operating Characteristic (ROC) curve, accuracy, sensitivity, and specificity show the models remarkable performance. The practical uses of this discovery for early illness detection in agriculture make it significant. With its exceptional specificity and precision, ResNet-18 serves as a potential tool for agriculture. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Detection of Fraudulent Alteration of Bank Cheques Using Image Processing Techniques
In todays world illegal alteration and illegal modifications of authenticated financial documents is increasing rapidly as a fastest growing crimes around the world. The result of this kind of crimes may result in a huge financial loss. In this paper image processing and document image analysis techniques are used to examine such cases in order to identify the fraudulent bank cheques. However, it is very difficult to detect an alteration made on documents once the printing ink of alike color is employed. In this paper, alterations and modifications caused with handwritten ball point pen strokes are considered and proposed a technique for recognition of such types of corrections by employing standard techniques under Digital image processing and pattern recognition. The results are quite promising during the experiments conducted. 2021, Springer Nature Singapore Pte Ltd. -
Detection of Forest Fire Using Modified LSTM Based Feature Extraction with Waterwheel Plant Optimisation Algorithm Based VAE-GAN Model
A crucial natural resource that directly affects the ecology is forests. Forest fires have become a noteworthy problem recently as a result of both natural and man-made climatic changes. A smart city application that uses a forest fire discovery technology based on artificial intelligence is provided in order to prevent significant catastrophes. A major danger to the environment, animals, and human lives is posed by forest fires. The early detection and suppression of these fires is crucial. This work offers a thorough method for detecting forest fires using advanced deep learning (DL) algorithms. Preprocessing the forest fire dataset is the initial step in order to improve its relevance and quality. Then, to enable the model to capture the dynamic character of forest fire data, long short-term memory (LSTM) networks are used to extract useful feature from the dataset. In this work, weight optimisation in LSTM is performed using a Modified Firefly Algorithm (MFFA), which enhances the model's performance and convergence. The Variational Autoencoder Generative Adversarial Networks (VAEGAN) model is used to classify the retrieved features. Furthermore, every DL model's success depends heavily on hyperparameter optimisation. The hyperparameters of an VAEGAN model are tuned in this research using the Waterwheel Plant Optimisation Algorithm (WWPA), an optimisation technique inspired by nature. WPPA uses the idea of plant growth to properly tune the VAEGAN's parameters, assuring the network's peak fire detection performance. The outstanding accuracy (ACC) of 97.8%, precision (PR) of 97.7%, recall (RC) of 96.26%, F1-score (F1) of 97.3%, and specificity (SPEC) of 97.5% of the suggested model beats all other existing models, which is probably owing to its improved architecture and training techniques. Copyright: 2024 The authors. This piece is published by IIETA and is approved under the CC BY 4.0 license. -
Detection of fake opinions on online products using decision tree and information gain
Online reviews are one of the major factors for the customers to purchase any product or to get service from many sources of information that can be used to determine the public opinion on the products. Fake reviews will be published intentionally to drive the web traffic towards the particular products. These fake reviewers mislead the customers to distract the purchasers mind. Reviewers behaviors are extracted based the semantical analysis of his review content for the purpose of identifying the review as fake or not. In this work the reviews are extracted from the web for a particular product, along with the reviews of several other information related to the reviewers also been extracted to identify the fake reviewers using decision tree classifier and Information Gain.Significance of the features on the decision is validated using information gain. Experiments are conducted on exhaustive set of reviews extracted from the web and demonstrated the efficacy of the proposed approach. 2019 IEEE -
Detection of faces from video files with different file formats
Face detection is the primary approach of all fundamental problems of human computer interaction system (HCIS). This paper evaluates the performance of detection system on single face from stored videos that are stored in different file formats. Stored videos contain raw homemade datasets as well as ready-made datasets. This proposed work concludes detection percentage of face detection system in different video formats. The implementation is done in two phases. The raw homemade dataset is tested on.3gp,.avi,.mov,.mp4 and a ready-made dataset is tested on.wmv,.m4v,.asf,.mpg file formats. The coding part for face detection has been done in MATLAB R2013a. The detection of faces from video file was 72.79 % for homemade dataset and 82.78% for ready-made dataset. 2016 IEEE. -
Detection of explosive picric acid via ESIPT-inhibited fluorescent chemosensor: theoretical insights, vapour phase detection and flexible indicator design
A fluorescent probe, (E)-2-((benzo[d]thiazol-2-ylimino)methyl)-5-(diethylamino)phenol (BMP), was designed and synthesized using 4-(diethylamino)-2-hydroxybenzaldehyde and benzothiazole-2-amine, and subsequently characterized for its selective turn-off response toward picric acid (PA). Upon the gradual addition of PA, significant changes in the absorption and fluorescence spectra were observed, marked by strong fluorescence quenching even in the presence of competing nitroaromatic compounds. BMP exhibited two absorption signals at 350 nm and 433 nm with a prominent emission band at 488 nm, attributed to excited-state intramolecular proton transfer (ESIPT), accompanied by a large Stokes shift of 138 nm. The interaction between PA and the hydroxyl group of BMP effectively suppressed the ESIPT process, leading to the observed spectral variations. The binding interactions were further confirmed through NMR spectroscopy and density functional theory (DFT) calculations. The ligand BMP has been utilized as a selective chemosensor for PA with a 2-fold reduction in fluorescence intensity and 19-fold increment in absorption intensity, showing a binding affinity of 2 104 M?1 and strong quenching efficiency toward picric acid, with a SternVolmer constant (Ksv) of 14.059 M?1 with a limit of detection (LOD) of 4.87 ?M. For practical implementation, BMP was successfully employed in a dipstick-based detection format for vapor-phase sensing. Moreover, BMP-embedded polymer films demonstrated excellent potential as solid-state fluorescent sensors, exhibiting visible fluorescence quenching upon exposure to PA. Their rapid, time-dependent emission response under UV light allows for convenient, on-site detection using devices such as smartphones, making them highly promising for real-world applications in explosives detection and environmental monitoring. This journal is The Royal Society of Chemistry, 2025 -
Detection of earnings manipulation in financial reports: a data-driven approach
Earnings manipulation attracts attention from both industry and academia as detection of earnings manipulation helps them to invest wisely rather than based on falsified financial statements. This paper discusses a process through which earnings manipulators can be identified. This can help shareholders to detect companies who made modifications to their financial statements. Earlier researchers developed many mathematical models to identify earnings manipulators. However, their works require an in-depth understanding of financial ratios and accounting principles. The advancement of data-driven algorithms has now brought comparable machine learning detection techniques into the picture, wherein data scientists can use historical data to detect earnings manipulators. This paper discusses the use of an artificial neural network (ANN) for detecting manipulations in the dataset. Copyright 2025 Inderscience Enterprises Ltd. -
Detection of DoS Attacks Using Machine Learning Based Intrusion Detection System
Conventional intrusion detection systems are not always sufficient due to the increasing sophistication and frequency of Denial-of-Service (DoS) attacks. This work presents a novel solution to this problem by leveraging machine learning techniques to increase the precision and efficacy of real-time intrusion detection. The system keeps a careful eye on network traffic patterns, looking for any irregularities that would point to a denial-of-service attack. An Intrusion Detection System (IDS) that utilizes machine learning technologies - specifically, neural networks and support vector machines - allows for real-time adaptation to new attack patterns. A combination of rigorous simulations and real-world testing provides empirical support for the IDS's quick detection and mitigation of DoS threats. This initiative makes a major contribution to the development of cybersecurity defenses. 2024 IEEE. -
Detection of Disease in Mango Trees Using Color Features of Leaves
The goal has been to detect disease in mango trees. This paper compares different approaches to extract color features and check the accuracy and applicability for mango trees. The paper proposes variations which helped in increasing the accuracy of features extracted for mango trees: firstly, a customized method of splitting leaf into layers while doing K-means clustering, and secondly, segmenting the region of interest to blocks to help in applying statistical functions more accurately over a region. 2020, Springer Nature Singapore Pte Ltd. -
Detection of cyber crime based on facial pattern enhancement using machine learning and image processing techniques
Cybercrime has several antecedents, including the rapid expansion of the internet and the wide variety of users around the world. It is now possible to use this data for a variety of purposes, whether for profit, non-profit, or purely for the benefit of the individual. As a result, tracing and detecting online acts of terrorism requires the development of a sound technique. Detection and prevention of cybercrime has been the subject of numerous studies and investigations throughout the years. An effective criminal detection system based on face recognition has been developed to prevent this from happening. Principle component analysis (PCA) and linear discriminant analysis (LDA) algorithms can be used to identify criminals based on facial recognition data. Quality, illumination, and vision are all factors that affect the efficiency of the system. The goal of this chapter is to improve accuracy in the facial recognition process for criminal identification over currently used conventional methods. Using proposed hybrid model, we can get the accuracy of 99.9.5%. 2022, IGI Global. All rights reserved. -
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). -
Detection of carbapenem resistance genes and cephalosporin, and quinolone resistance genes along with oqxAB gene in Escherichia coli in hospital wastewater: A matter of concern
Aims: This study was performed to detect the presence of Escherichia coli resistant to cephalosporins, carbapenems and quinolones in hospital wastewater. Methods and Results: Wastewaters from a rural (H1) and an urban (H2) hospital were tested for E.coli resistant to cephalosporins, carbapenem and quinolones. Genes coding for chromosomal and plasmid-mediated resistance and phylogenetic grouping was detected by multiplex polymerase chain reaction (PCR) and for genetic relatedness by rep-PCR. Of 190 (H1=94; H2=96) E.coli examined, 44% were resistant to both cephalosporins and quinolones and 3% to imipenem. ESBLs were detected phenotypically in 96% of the isolates, the gene blaCTX-M coding for 87% and blaTEM for 63%. Quinolone resistance was due to mutations in gyrA and parC genes in 97% and plasmid-coded aac-(6?)-Ib-cr in 89% of isolates. Only in one carbapenem-resistant E.coli, NDM-1 was detected. Nearly 67% of the isolates belonged to phylogenetic group B2. There was no genetic relatedness among the isolates. Conclusions: Hospital wastewater contains genetically diverse multidrug-resistant E.coli. Significance and Impact of the Study: This study stresses the need for efficient water treatment plants in healthcare settings as a public health measure to minimize spread of multidrug-resistant bacteria into the environment. 2014 The Society for Applied Microbiology. -
Detection of breast cancer in mammography images using intelligent models
Amongst the several cancer types, incidence of breast cancer is the highest in women. Breast cancer can be diagnosed and treated effectively through various screening methods and computer-aided detection systems (CADs). However, conventional computer-aided diagnosis (CAD) programs for detecting potential cancers on mammograms are lacking diagnostic accuracy and require upgradation. The advances in machine learning, particularly with the use of deep (multi-layered) convolutional neural networks, have allowed artificial intelligence to create a transformation in CAD that has improved models' prediction quality. The outline of this chapter includes a structured method for predicting presenting breast cancer stages, identification, segmentation and classification of lesions, and breast density assessment using the current technological models which includes artificial intelligence, deep learning, and machine learning. 2024, IGI Global. All rights reserved.