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An efficient privacy-preserving model based on OMFTSA for query optimization in crowdsourcing
Crowdsourcing is now one of the most important and transformative paradigms, with great success in a variety of application tasks. Crowdsourcing obtains knowledge and information to solve cognitive or intelligence-intensive tasks from an evolving group of participants via the Internet. Unfortunately, providing a hard privacy guarantee and query optimization is incompatible when a higher task acceptance rate needs to be accomplished and this case is common in most existing crowdsourcing solutions. The state of art systems suffered from different complexities such as lack of crowdsourcing optimization techniques, increased cost, latency, security, and scalability issues. In this paper, we have proposed a crowdsourcing model to optimize the cost and latency, issues that occur while query optimization using the Moth Flame and Tunicate Swarm Algorithm (MF-TSA). The TSA algorithm is added to the MF algorithm to enhance its exploitation capability and yield fast convergence. The data privacy concerns of the worker and the requestor are addressed using homomorphic encryption that simultaneously enhances the efficiency of the crowdsourcing framework. The main aim of this work is to optimize the cost and latency for query plan selection along with security. Initially, the homomorphic encryption model is used to encrypt the data. In query design, two kinds of crowd-controlled administrators, that is, Crowd Powered Selection (CSelect) and Crowd Powered Join (CJoin) are connected for assessing query. The proposed framework utilizes MF-TSA to optimize the selection and join queries with low cost and latency. Finally, the experimental results demonstrate better query optimization performance than other existing algorithms such as sequential, parallel, and CrowdOp. 2021 John Wiley & Sons Ltd. -
An Efficient Fuzzy Logic Cluster Formation Protocol for Data Aggregation and Data Reporting in Cluster-Based Mobile Crowdsourcing
Crowdsourcing is a procedure of outsourcing the data to an abundant range of individual workers rather than considering an exclusive entity or a company. It has made various types of chances for some difficult issues by utilizing human knowledge. To acquire a worldwide optimal task assignment scheme, the platform usually needs to collect location information of all workers. During this procedure, there is a major security concern; i.e., the platform may not be trustworthy, and so, it brings about a threat to workers location privacy. Recently, many distinguished research papers are published to address the security and privacy issues in mobile crowdsourcing. According to our knowledge, the security issues that occur in terms of data reporting were not addressed. Secure and efficient data aggregation and data reporting are the critical issue in Mobile Crowdsourcing (MCS). Cluster-based mobile crowdsourcing (CMCS) is the efficient way for data aggregation and data reporting. In this paper, we propose a novel procedure, the efficient fuzzy logic cluster formation protocol (EFLCFP) for cluster formation, and use cluster cranium (CC) for data aggregation and data reporting. We recommend a couple of secure and efficient data transmission (SET) protocols for CMCS, (i) SET-IBE uses additively homomorphic identity-based encryption system and (ii) SET-IBOOS uses the identity-based online/offline digital signature system, respectively. Then, we have widen the features of cluster cranium by increasing the propensity to achieve aggregation and reporting on the data yielded by the requesters without scarifying their privacy. Also, considering query optimization using cost and latency. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Brain image classification using time frequency extraction with histogram intensity similarity
Brain medical image classification is an essential procedure in Computer-Aided Diagnosis (CAD) systems. Conventional methods depend specifically on the local or global features. Several fusion methods have also been developed, most of which are problem-distinct and have shown to be highly favorable in medical images. However, intensity-specific images are not extracted. The recent deep learning methods ensure an efficient means to design an end-to-end model that produces final classification accuracy with brain medical images, compromising normalization. To solve these classification problems, in this paper, Histogram and Time-frequency Differential Deep (HTF-DD) method for medical image classification using Brain Magnetic Resonance Image (MRI) is presented. The construction of the proposed method involves the following steps. First, a deep Convolutional Neural Network (CNN) is trained as a pooled feature mapping in a supervised manner and the result that it obtains are standardized intensified pre-processed features for extraction. Second, a set of time-frequency features are extracted based on time signal and frequency signal of medical images to obtain time-frequency maps. Finally, an efficient model that is based on Differential Deep Learning is designed for obtaining different classes. The proposed model is evaluated using National Biomedical Imaging Archive (NBIA) images and validation of computational time, computational overhead and classification accuracy for varied Brain MRI has been done. 2022 CRL Publishing. All rights reserved. -
Grading of Red Chilli, Cardamom and Coriander Using Image Processing
Indian cuisine is known for its wide range of spices. Spices are known as the heart and soul of Indian food. Traditionally, categories are identified based on certain chemical technology or with the help of senses gifted to mankind. In this paper, an image processing technique used to extract multiple features is presented to determine the various categories of spices consumed. This proposed work uses different varieties of common Indian spices such as Capsicum annuum (dry red chilli), Elettaria cardamomum (cardamom) and Coriandrum Sativum (coriander). While creating the image dataset, different categories of all spices were taken from southern region of India. Features are extracted from the manually created image dataset, which forms the base for classification. The result obtained using Multilayer Perceptron (MLP), Naive Bayes and Random Forest classifier is found to be optimal. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Ultraviolet Flux and Spectral Variability Study of Blazars Observed with UVIT/AstroSat
Blazars, the peculiar class of active galactic nuclei, are known to show flux variations across the accessible electromagnetic spectrum. Though they have been studied extensively for their flux variability characteristics across wavelengths, information on their ultraviolet (UV) flux variations on timescales of hours is very limited. Here, we present the first UV flux variability study on intraday timescales of a sample of ten blazars comprising two flat-spectrum radio quasars (FSRQs) and eight BL Lacertae objects (BL Lacs). These objects, spanning a redshift (z) range of 0.034 ? z ? 1.003, were observed in the far-UV (1300?1800 and near-UV (2000?3000 wavebands using the ultraviolet imaging telescope on board AstroSat. UV flux variations on timescales of hours were detected in nine sources out of the observed ten blazars. The spectral variability analysis showed a bluer-when-brighter trend with no difference in the UV spectral variability behavior between the studied sample of FSRQs and BL Lacs. The observed UV flux and spectral variability in our sample of both FSRQs and BL Lacs revealed that the observed UV emission in them is dominated by jet synchrotron process. 2024. The Author(s). Published by the American Astronomical Society. -
Efficient feature fusion model withmodified bidirectional LSTM for automatic Parkinson's disease classification
The majority of people affected by Parkinsons disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinsons disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a persons walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin,the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinsons disease.Local features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional LSTM (MBi-LSTM) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinsons disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. Bharati Vidyapeeth's Institute of Computer Applications and Management 2024. -
Ultraviolet variability in radio-loud active galactic nuclei observed by UVIT onboard AstroSat
Radio-loud active galactic nuclei (AGN) are among the most luminous objects in the Universe, emitting radiation from low-energy radio waves to high energy ? -rays. They are well known to exhibit flux variations at nearly all accessible wavelengths. However, their variability properties in the ultraviolet (UV) band remain relatively less explored compared to other wavebands. Here, we present the results of a systematic investigation of the UV flux and spectral variability characteristics of 24 radio-loud AGN spanning the redshift range 0.018 ? z ? 2.218. The sample comprises 17 BL Lac objects, 6 flat spectrum radio quasars (FSRQs) and one radio-loud narrow line Seyfert 1 galaxy. We used observations obtained with the Ultra-Violet Imaging Telescope (UVIT) onboard AstroSat during its first ten years of operation, covering both the far-UV (FUV; 1300 - 1800 and near-UV (NUV; 2000 - 3000 bands. Of the 24 sources analysed, 18 showed significant UV variability on hour timescales. We found a bluer when brighter (BWB) spectral trend in two sources: the FSRQ CTA 102 and the BL Lac PKS 0447 - 439. The observed UV variability in our sample of radio-loud AGN, together with the BWB trend detected in these two sources, supports a scenario in which the hour timescale UV variations are driven by intrinsic processes within their relativistic jets. 2026 Elsevier B.V. -
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. -
Innovative implementation, ethical challenges, and future prospects of artificial intelligence in pharmaceuticals
Incorporation of pharmaceutical industry and artificial intelligence (AI) is revolu-tionizing patient care, drug development, and discovery. Drug interactions have been predicted using machine learning techniques, optimal molecular structures have been designed, and clinical trial lengths have been reduced. Ethical problems including violations of data privacy, biassed algorithms, and unequal access to AI-informed treatments have been highlighted in the face of these technologies Reacting to these problems, regulatory models have been debated and passed. It is well known that artificial intelligence can drive therapeutic developments and customize medicine. One expects continuous innovations in artificial intelligence technologies to change the pharmaceutical industry. Ethical standards must be reinforced and openness kept if artificial intelligence is to be fully embraced. Innovation must guide newly developing projects. 2026 by IGI Global Scientific Publishing. All rights reserved. -
Electrical and Mechanical Properties of Vapour Grown Gallium Monotelluride Crystals
International Journal of Minerals, Metallurgy and Materials, Vol-20 (10), pp. 967-971. ISSN-1674-4799 -
Vapour Growth and Characterization of Beta Indium Sesquitelluride Crystals
Journal of Crystal Growth, Vol-394, pp. 1-6. ISSN-0022-0248 -
Electrical and mechanical properties of vapour grown gallium monotelluride crystals
The physical vapour deposition (PVD) of gallium monotelluride (GaTe) in different crystalline habits was established in the growth ampoule, strongly depending on the temperature gradient. Proper control on the temperatures of source and growth zones in an indigenously fabricated dual zone furnace could yield the crystals in the form of whiskers and spherulites. Optical and electron microscopic images were examined to predict the growth mechanism of morphologies. The structural parameters of the grown spherulites were determined by X-ray powder diffraction (XRD). The stoichiometric composition of these crystals was confirmed using energy dispersive analysis by X-rays (EDAX). The type and nature of electrical conductivity were identified by the conventional hot probe and two probe methods, respectively. The mechanical parameters, such as Vickers microhardness, work hardening index, and yield strength, were deduced from microindentation measurements. The results show that the vapour grown p-GaTe crystals exhibit novel physical properties, which make them suitable for device applications. 2013 University of Science and Technology Beijing and Springer-Verlag Berlin Heidelberg. -
Spherulitic crystallization of ?-In2Te3 by physical vapour deposition
Different morphologies of indium telluride (In2Te3) including novel spherulites were crystallized using the physical vapour deposition (PVD) method, by varying the difference in the growth and source zone temperature (?T) of a dual zone horizontal furnace assembled indigenously. Whiskers and kinked needles of In2Te3were grown at ?T = 250 K and 300 K respectively, maintaining the growth zone at 500 C. At high supersaturation (? T = 400 K), spherulitic crystals were obtained. The stoichiometric composition of these crystals has been confirmed using energy dispersive analysis by x-rays (EDAX). The structure of ?-In2Te3 spherulitic crystals is identified as zinc blende with lattice parameter a = 6.159 from x-ray diffraction (XRD) studies. The scanning electron microscope (SEM) images revealed the radial structure of the grown spherulites. The growth mechanism for the spherulitic crystallization of ?-In2Te3 crystals has been discussed based on the theoretical models. Copyright 2011 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim. -
Growth and characterization of chalcogenide crystals by vapour method
A horizontal linear gradient two zone furnace was designed and employed to grow single crystals of indium telluride by Physical Vapour Deposition (PVD) method. It was calibrated for various trials including, series and parallel combinations of coils, and set temperatures. Systematic growth runs for chalcogenide crystals were performed by varying the source and growth temperatures. Crystals of different sizes and morphologies were obtained. The morphology and chemical analysis of the grown crystals were investigated by Scanning Electron Microscope (SEM) and Energy Dispersive Analysis using X-rays (EDAX). The hardness of the crystals was estimated using a Vickers microhardness tester. 2011 American Institute of Physics. -
Vapour growth and characterization of beta indium sesquitelluride crystals
Physical Vapour Deposition (PVD) provides stoichiometric crystals of different morphology, depending upon the materials, geometry of ampoules, temperature profiles, growth parameters and kinetics of crystallization. The crystal forms such as needles, platelets and spherulites of beta indium sesquitelluride (?-In2Te3) were produced by controlling the temperature of source and growth zones. The X-Ray Diffraction (XRD) and chemical analysis of the spherulitic crystals confirmed zinc blende structure with beta phase. Their resistivity (135.16 ? cm) at room temperature (300 K) was determined by van der Pauw method. The temperature dependence of DC conductivity was investigated using the conventional two-probe technique. The variation of dielectric constant (?1) and dielectric loss (tan ?) with temperature has been studied for different frequencies (1 kHz-1 MHz). The AC conductivity, ?ac(?) was found to vary with angular frequency as ?s, where s is the frequency exponent. The values of s lie very close to unity and show a slight decrease with increase in temperature, which indicate a Correlated Barrier Hopping (CBH) between centres forming Intimate Valence Alternation Pairs (IVAP). The activation energy for conduction ranges from 0.187 eV to 0.095 eV. The microhardness of ?-In2Te3 spherulites is found to be 353.5 kg/mm2, which is higher than that of other semiconducting chalcogenides. The results thus obtained on crystals grown from vapour phase open up ample possibilities for radiation detector applications. 2014 Elsevier B.V. -
Multimodal Face and Ear Recognition Using Feature Level and Score Level Fusion Approach
Recent years have seen a significant increase in attention in multimodal biometric systems for personal identification especially in unconstrained environments. This paper presents a multimodal recognition system by combining feature level fusion of ear and profile face images. Multimodal biometric systems by combining face and ear can be used in an extensive range of applications because we can capture both the biometrics in a non-intrusive manner. Local texture feature descriptor, BSIF is used to extract discriminative features from biometric templates. Feature level and score level fusion is experimented to improve the performance of the system. Experimental results on different public datasets like GTAV, FEI, etc., show that the proposed method gives better performance in recognition results than individual modality. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Ear Recognition Using Rank Level Fusion of Classifiers Outputs
An individuals authentication plays a vital role in our daily life. In the last decade, biometric-based authentication has become more prevalent than traditional approaches like passwords and pins. Ear recognition has gained attention in the biometric community in recent years. Researchers defined several features for the identification of a person from ear image. The features play a vital role in the success of classification models. This paper considers an ensemble of features for designing a new classification model. The features are assessed in isolation as well as through feature-level fusion. Subsequently, a rank-level fusion for classification is introduced. The experiments are conducted on both constrained and unconstrained ear datasets. The results are promising and open up new possibilities in machine learning-based ear recognition 2023, International journal of online and biomedical engineering.All Rights Reserved. -
Ear Recognition Using Pretrained Convolutional Neural Networks
Ear biometrics, which involves the identification of a person from an ear image, is challenging under unconstrained image capturing scenarios. Studies in Ear biometrics reported that the Convolutional Neural Network is a better alternative to classical machine learning with handcrafted features. Two major concerns in CNN are the requirement of enormous computing resources and large datasets for training. The pretrained network concept helps to use CNN with smaller datasets and is less demanding on hardware. In this paper, three pre-trained CNN models, AlexNet, VGG16, and ResNet50 are used for ear recognition. The fully connected classification layers of the nets are trained with AWE, an unconstrained ear dataset. Alternatively, the CNN layers output (the CNN features) are extracted, and an SVM classification model is built. To improve the classification accuracy, the training dataset size is increased through data augmentation. Data augmentation improved the classification accuracy drastically. The results show that ResNet50, with the fully connected classification layer, results in higher accuracy. 2021, Springer Nature Switzerland AG. -
A Novel Approach to Automatic Ear Detection Using Banana Wavelets and Circular Hough Transform
Ear is an attractive biometric trait that maintain their structure with increasing age. Because of the complex geometry of ear, its detection is very difficult. This paper proposes a modified algorithm for automatic detection of 2D ear images using Banana wavelets and Hough transform. Banana wavelets derived from bank of stretched and curved Gabor wavelets are used to identify curvilinear ear structure. Addition of a preprocessing stage, prior to application of banana wavelets is found to improve the detection results further. The proposed algorithm is brought in to comparison with three existing algorithms and evaluated on standard databases. In addition to manual detection accuracy, this paper also calculates the efficiency of the proposed method using automatic classification techniques. The features like LBP and Gabor extracted from segmented ear image is used by different classifiers to determine whether the segmented portion of the image is class Ear or Non ear. 2019 IEEE. -
Ethical and Privacy Considerations in GAN-Based Security
Generative Adversarial Networks (GANs) have caused a great transformation in a number of fields, including anomaly detection, healthcare, data encryption, deep learning (DL), machine learning (ML), and entertainment. They are also employed in various security applications. This chapter explores mainly the privacy and ethical concerns associated with the use of GANs. Although GANs have enormous possibilities for security applications such as biometrics, intrusion detection, and data augmentation, implementing them requires careful consideration regarding privacy and ethical issues. GANs ability to produce remarkably lifelike deepfakes poses a risk since it might promote social engineering attacks. Moreover, these discrepancies may be increased by biased security programs that arise from biases in training data. Even with all of the potential benefits, ethical and privacy concerns must be prioritized for the safe and reliable application of GANs in security. This chapter also lays the foundation of GAN-based applications in Cybersecurity. 2026 selection and editorial matter, E. Chandra Blessie, Pethuru Raj, and B. Sundaravadivazhagan; individual chapters, the contributors.