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An efficient intelligent transportation system for traffic flow prediction using meta-temporal hyperbolic quantum graph neural networks
Intelligent Transportation Systems (ITS) necessitate scalable, real-time, and adaptive traffic flow prediction models to enhance urban mobility and alleviate congestion. Conventional Graph Neural Network methodologies encounter difficulties in managing extensive road networks, long-range temporal relationships, and computing efficiency for real-time applications. An innovative deep learning framework named Meta Temporal Hyperbolic Quantum Graph Neural Networks that integrates hyperbolic embeddings, meta learning, quantum graph, Neural Ordinary Differential Equation (NODEs) to improve the ITS Performance. Across many cities, meta learning facilitates swift adaptation with minimum retraining whereas hyperbolic graph embeddings efficiently depict hierarchical route configurations The usage of Quantum Graph Neural Networks (QGNNs) enhances graph-based scheming, enabling real-time traffic flow to forecast for extensive networks. Also, NODEs summarize ongoing traffic progress, enhancing precision under dynamic sceneries. Datasets like Los-loop and SZ-taxi datasets are validated by experiments which highlights the impact of the proposed MTH-QGNN model, acquiringamean value RMSE of 4.5 and MAE of 3.5, ensuring minimal prediction error. MTH-QGNN model constantly sustained accuracy above 80% and R2 values exceeding 83%, representing robust predictive trustworthiness. MTH-QGNN effectively captures complex spatiotemporal traffic patterns with a variance score above threshold value. The Author(s) 2025. -
An efficient inclusion complex based fluorescent sensor for mercury (II) and its application in live-cell imaging /
Journal of Fluorescence, Vol.32, pp.1109–1124, ISSN No: 1573-4994.
The formation of an inclusion complex between hydroxypropyl-β-cyclodextrin (H-CD) and 4-acetylphenyl-4-(((6-chlorobenzo[d]thiazol-2-yl)-imino)-methyl)-benzoate (L) was investigated by FT-IR, 1H-NMR, X-ray diffraction (XRD), FT-Raman, scanning electron microscope (SEM) techniques in the solid-state, absorption and emission spectroscopy in the liquid state and the virtual state as molecular docking technique. The binding properties of the inclusion complex (H-CD: L) with cations in deionized water was observed via absorbance and photoluminescence (PL) emission spectroscopy. -
An Efficient Inclusion Complex Based Fluorescent Sensor for Mercury (II) and its Application in Live-Cell Imaging
The formation of an inclusion complex between hydroxypropyl-?-cyclodextrin (H-CD) and 4-acetylphenyl-4-(((6-chlorobenzo[d]thiazol-2-yl)-imino)-methyl)-benzoate (L) was investigated by FT-IR, 1H-NMR, X-ray diffraction (XRD), FT-Raman, scanning electron microscope (SEM) techniques in the solid-state, absorption and emission spectroscopy in the liquid state and the virtual state as molecular docking technique. The binding properties of the inclusion complex (H-CD: L) with cations in deionized water was observed via absorbance and photoluminescence (PL) emission spectroscopy. The fluorescence probe (H-CD: L) inclusion complex (IC) was examined for several heavy metal cations, and identified that the PL emission wavelength of the complex displayed a continuous rise in the fluorescence intensity for Hg2+. A linearity range of 1 108 11 108M and limit of detection value of 2.71 1010M was found to be achieved for the detection of Hg2+. This outcome proves that the inclusion complex H-CD: L would be a promising material for the development a solid-state fluorescence probe for detecting Hg2+. It also shows application in real sample analysis and cell imaging. 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
An efficient image denoising method based on bilateral filter model and neighshrink SURE
In all the instances of image acquisition, transmission and storage, the unwanted noise gets into the information content of the image and thereby introduces an unpleasant visual quality to the observer. So the field of image processing has produced a lot of image denoising algorithms and techniques to improve the visual quality of the image. Since noise cannot be reduced to zero practically, the need for faithful and efficient denoising techniques to produce almost noiseless images demands a systematic research work in the field of denoising methods. The denoising process using a bilateral filter even though produces improvement in the image quality, it does not show consistency when the noise level is high and also the peak signal to noise ratio (PSNR) and Image quality Index (IQI) do not show any improvement. This paper proposes an improved algorithm that incorporates the function of bilateral filter model and wavelet thresholding using Neighshrink SURE method. The results show significant improvement in both PSNR and IQI values with respect to the four standard test images under various noise conditions. BEIESP. -
An efficient hybrid digital architecture for space vector PWM method for multilevel VSI
This paper presents an efficient, cost effective design implementation of a hybrid digital architecture for space vector pulse width modulation (SVPWM) method for multilevel inverters (MLIs). The SVPWM method is one of the most popular real time PWM method for three phase voltage source inverter (VSI). The implementation of SVPWM method becomes complex with an increase in the number of levels in a multilevel inverter. The SVPWM method for multilevel inverter is a multitask system. The main constraint when it comes to implementing SVPWM for multilevel inverters is the processing of dwell time computation and the generation of PWM gate signals for all of the switches with an accurate delay. A hybrid hardware structure consisting of a simple low-cost, low-power dsPIC micro controller (dsPIC 30F4011) and a state of the art Field Programmable Gate Array (FPGA) (Cyclone V 5CGXFC5C6F27C7N) is used to implement SVPWM. The proposed hybrid digital architecture utilizes the advantages and resources of the dsPIC and FPGA. The hybrid digital architecture meets the timing constraints of multitasking through synchronization and parallelism. A communication interface between the dsPIC and the FPGA reduces the design complexity. The software overhead for the communication interface remains fixed for any number of levels. The hybrid structure of the digital architecture provides scalability for the SVPWM method with more number of levels in multilevel inverter. The operation of the proposed hybrid digital architecture is experimentally validated with an optimized SVPWM method for a five level VSI. An optimized region identification algorithm and simple dwell time expressions are described for a five level SVPWM. The input DC of the five level VSI is obtained from a differential power processing (DPP) based PV system. Experimental results under different operating conditions are presented. 2020, The Korean Institute of Power Electronics. -
An efficient hybrid approach for numerical study of two-dimensional time-fractional Cattaneo model with Riesz distributed-order space-fractional operator along with stability analysis
In this article, we study and analyze the two-dimensional time-fractional Cattaneo model with Riesz space distributed-order. To obtain approximate solutions of this type of fractional model the combined and effective numerical approach based on the ADI Galerkin method and the Legendre spectral method used the ADI Galerkin numerical method uses the finite difference approach. The ADI Galerkin numerical method is used to approximate the proposed model in terms of the time variable, and the Legendre spectral method is applied to discretize the fractional model with respect to the space variable. Also, the convergence analysis and stability of the proposed method are discussed and reviewed in this manuscript. In the end, some numerical examples are tested for the effectiveness and accuracy of the proposed method. As well as, in the numerical examples section, the presented numerical approach is compared with two numerical methods and the results are reported in a table. 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved. -
An Efficient HOG-Centroid Descriptor for Human Gait Recognition
Automatic recognition of human gait have gained much attention nowadays. Histogram of Oriented Gradient (HOG) is a widely adopted descriptor for object's shape analysis. In this paper, combination of HOG descriptor with silhouette centroid for human gait recognition is proposed. The resultant descriptor, namely HOG-Centroid, achieves better recognition performance on comparison with HOG descriptor individually as well as other existing gait recognition methods. Experiments are carried out with CASIA gait dataset B and cumulative matching scores of 95.3%, 98.1% and 99.2% are obtained for rank 1, rank 5 and rank 10 respectively. 2019 IEEE. -
An Efficient Fuzzy Logic-Integrated Hybrid Deep Learning Framework for Medical Diagnosis
Medical diagnosis involves analyzing symptoms, test results, and patient histories, but uncertainty from vague symptoms and incomplete records complicates the process. Fuzzy logic-based systems address this issue but often depend on manual rule creation, which is time-consuming. This research proposes a hybrid approach integrating fuzzy logic with deep learning techniques (FL-DLT) for intelligent diagnosis. The framework combines adaptive neuro-fuzzy inference system (ANFIS) for handling uncertainty with convolutional neural networks (CNNs) for extracting features from medical images like X-rays and MRIs. ANFIS models relationships between symptoms, results, and diagnoses, while CNNs analyze medical images. Experimental results show high accuracy and reliability, even with noisy or incomplete data. The proposed approach can improve diagnostic accuracy and efficiency, supporting clinicians in decision-making. Key contributions include the development of the FL-DLT framework and its evaluation using a large dataset of patient records and medical images. Additionally, the research offers insights into the application of fuzzy logic and deep learning in medical diagnosis, highlighting their potential to enhance diagnostic outcomes and efficiency in clinical practice. 2009 Tsinghua University Press. -
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. -
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An efficient framework for scientific article recommendation system
Excess data makes it challenging to extract information that is relevant to a domain of study or research. Existing state-of-the-art systems focus majorly on the selection of highly connected, prestigious and cited articles, regardless of the relevance of papers. To improve quality of findings, recommender systems which are a subclass of information filtration systems are used. They filter out relevant information over prestigious data from an existing repository of information. There are various sub-domains under recommender systems. This study focuses on citation recommendation. Citations are an integral part of any scientific paper, academic dissertation or projects. Finding appropriate citations for any work is a scholar's most time-consuming task. Thus, a well-defined citation recommendation system provides fulfillment and completeness for citing the giants works. The thesis aims to study existing frameworks for citation recommendation systems and identify the best dataset to work on graph- based recommender systems. A framework that recommends the most similar and relevant article to the user rather than prestigious authors or papers is here by proposed. The study explores various machine learning and deep learning techniques and methods which can be used effectively in recommending loosely connected yet highly relevant articles. -
An Efficient Face Recognition System using Deep Transfer Learning
Face recognition is an AI-based innovation used to find and recognize human appearances in videos and images. Organizations can apply face recognition to many different kinds of fields which may include biometrics, regulation of law, security and individual wellbeing; so as to take observation of individuals in any scenario. Face recognition has advanced from simple vision methods to progress in ML; and further to progressively refined neural networks (ANN) and related advances. It currently assumes an indispensable part as the initial phase in numerous basic applications, including the task of tracking a face. Face recognition is utilized to focus cameras or count the number of individuals present in a particular region. The innovation likewise has showcasing applications, for instance, showing recommended promotions when a specific user is detected. 2022 IEEE. -
An Efficient Detection of Suspicious Objects from Dynamic Video Surveillance by Fusion-based Multiview Deep Learning Techniques
Real-time detections of suspicious objects are needed to identify for finding criminal activities and are used in immediate alert systems for public safety applications. Video surveillance systems use live, closed-circuit televisions (live CCTVs) for dynamic video capturing of objects. Finding criminal activities over the dynamic video data is an emerging surveillance problem. The deep learning techniques are tedious for detecting suspicious movable objects and criminal activities. YOLO (You Only Look Once) gives more prominent movable video object detection accuracy than conventional deep models, like Convolutional Neural Network (CNN), 3D CNN, and Convolutional LSTM. State-of-the-art YOLO models, YOLOv8n, YOLOv8s, and YOLOv8l, are emphasized for extracting and detecting object motion detection from the dynamic video. YOLO models use single-view deep learning to classify or detect objects. These models limit the accuracy of the detection of complex and dynamic objects of dynamic video data. This paper presents the Fusion-based Multiview deep learning techniques to overcome this issue. The experimental study demonstrates that the proposed methodology efficiently detects suspicious data objects more than the single-view deep models. 2025 River Publishers. -
An Efficient Detection and Prediction of Intrusion in Smart Grids Using Artificial Neural Networks
In recent years, fraud identification on Internet of Things (IoT) devices has been essential to obtaining better results in all fields, such as smart cities, smart grids, etc. As a result, there are more IoT devices in the smart grid's power management sectors, and apart from these identifications, intrusion into the smart grid is very difficult. Hence, to overcome this, a proposed intrusion detection system in a smart grid using an artificial neural network (ANN) has been used to detect the intrusion and improve the prediction rate, and it has been very effective on various faults injected into the smart grids in ranges and seasons. As per the simulation result, the proposed method shows better results as compared to a conventional neural network (CNN) with respect to the root mean square error in terms of weekly, monthly, and seasonal terms of 0.25%, 0.15%, and 0.26%, respectively. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
An efficient design and comparison of machine learning model for diagnosis of cardiovascular disease
Cardiovascular disease has a significant global impact. Cardiovascular disease is the primary cause of disability and mortality in most developed countries. Cardiovascular disease is a condition that disturbs the structures and functionality of the heart and can also be called heart disease. Cardiovascular diseases require more precise, accurate, and reliable detection and forecasting because even a small inaccuracy might lead to fatigue or mortality. There are very few death occurrences related to cardio sickness, and the amount is expanding rapidly. Predicting this disease at its early stage can be done by employing Machine Learning (ML) algorithms, which may help reduce the number of deaths. Data pre-processing can be employed here to eliminate randomness in data, replace missing data, fill in default values if appropriate, and categorize features for forecasting and making decisions at various levels. This research investigates various parameters that are related to the cause of heart disease. Several models discussed here will come under the supervised learning type of algorithms like Support Vector Machine (SVM), K-nearest neighbor (KNN), and Nae Bayes (NB) algorithm. The existing dataset of heart disease patients from the Kaggle has been used for the analysis. The dataset includes 300 instances and 13 parameters and 1 label are used for prediction and testing the performance of various algorithms. Predicting the likelihood that a given patient will be affected by the cardiac disease is the goal of this research. The most important purpose of the study is to make better efficiency and precision for the detection of cardiovascular disease in which the target output ultimately matters whether or not a person has heart disease. 2023, Bentham Books imprint. All rights reserved. -
An Efficient Deep Learning-Based Hybrid Architecture for Hate Speech Detection in Social Media
Social media has become an integral part of life as users are spending a significant amount of time networking online. Two primary reasons for its increasing popularity are ease of access and freedom of speech. People can express themselves without worrying about consequences. Due to lack of restriction, however, cases of cyberbullying and hate speeches are increasing on social media. Twitter and Facebook receive over a million posts daily, and manual filtration of this enormous number is a tedious task. This paper proposes a deep learning-based hybrid architecture (CNN + LSTM) to identify hate speeches by using Stanfords GloVe, which is a pre-trained word embedding. The model has been tested under different parameters and compared with several state-of-the-art models. The proposed framework has outperformed existing models and has also achieved the best accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
An Efficient Deep Learning Model Using Harris-Hawk Optimizer for Prognostication of Mental Health Disorders
Mental health disorders are primarily life style driven disorders, which are mostly unidentifiable by clinical or direct observations, but act as a silent killer for the impacted individuals. Using machine learning (ML), the prediction of mental ailments has taken significant interest in medical informatics community especially when clinical indicators are not there. But, majority studies now focus on usual machine learning methods used to predict mental disorders with few organized health data, this may give wrong signals. To overcome the drawbacks of the conventional ML prediction models, this work presents Deep Learning (DL) trained prediction model for automated feature extraction to realistically predict mental health disorders from the online textual posts of individuals indi cating suicidal and depressive contents. The proposed model encompasses three phases named pre-processing, feature extraction and optimal prediction phase. The developed model utilizes a novel Sparse Auto-Encoder based Optimal Bi-LSTM (SAE-O-Bi-LSTM) model, which integrates Bi-LSTM and Adaptive Harris-Hawk Optimizer (AHHO) for extracting the most relevant mental illness indicating features from the textual content in the dataset. The dataset utilized for training consist of 232074 unique posts from the "SuicideWatch" and "Depression" subreddits of the Reddit platform during December 2009 to Jan 2021 downloaded from Kaggle. In-depth comparative analysis of the testing results is conducted using accuracy, precisions, F1 score, specificity, and Recall and ROC curve. The results depict considerable improvement for our developed approach with an accuracy of 98.8% and precision of 98.7% respectively, which supports the efficacy of our proposed model. The Author(s) 2024. -
An Efficient Deep Learning Framework FPR Detecting and Classifying Depression Using Electroencephalogram Signals
Depression is a common and real clinical disease that has a negative impact on how you feel, how you think, and how you behave. It is a significant burdensome problem. Fortunately, it can also be treated. Feelings of self-pity and a lack of interest in activities you once enjoyed are symptoms of depression. It can cause a variety of serious problems that are real, and it can make it harder for you to work both at home and at work. The main causes include family history, illness, medications, and personality, all of which are linked to electroencephalogram (EEG) signals, which are thought of as the most reliable tools for diagnosing depression because they reflect the state of the human cerebrum's functioning. Deep learning (DL), which has been extensively used in this field, is one of the new emerging technologies that is revolutionizing it. In order to classify depression using EEG signals, this paper presents an efficient deep learning model that allows for the following steps: (a) acquisition of data from the psychiatry department at the Government Medical College in Kozhikode, Kerala, India, totaling 4200 files; (b) preprocessing of these raw EEG signals to avoid line noise without committing filtering; (c) feature extraction using Stacked Denoising Autoevolution; and (d) reference of the signal to estimate true and all. According to experimental findings, The proposed model outperforms other cutting-edge models in a number of ways (accuracy: 0.96, sensitivity: 0.97, specificity: 0.97, detection rate: 0.94). 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG. -
An efficient deep learning based stress monitoring model through wearable devices for health care applications
Due to the mental stress of the human, the negative effects are known to be recent decades. Early detections of high level stresses are necessary to stop harmful consequences. Studies have proposed on wearable technologies which detect human stress. This study proposes stress detection systems which use physiological signals of people collected by wearable technologies and attached to human bodies. They can carry it during their daily routine. This works proposed system includes removal of artifacts in bio signals and feature extractions from these cleaned signals. Since, DL (deep learning) based models are proven to be the best for these analyses, this article uses a random differential GWO (Grey wolf optimization) algorithm for feature extraction and a ML (machine learning) algorithm called RF (random forest) has been used for classification of the human body parameters like activities of the heart, conductance in skins and corresponding accelerometer signals. The proposed stress detection system is implemented with the real time data gathered from 21 participants. This approach can detect the stress of a human and prevent it from early stages with necessary lectures to avoid the negative effects. 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. -
An efficient deep learning approach for identifying interstitial lung diseases using HRCT images
Interstitial lung disease (ILD) encompasses over 200 fatal lung disorders affecting the interstitium, leading to significant mortality rates. We propose an AI-driven approach to diagnose and classify ILD from high-resolution computed tomography (HRCT) images. The research utilises a dataset of 3,045 HRCT images and employs a two-tier ensemble method that combines various machine learning (ML) models, convolutional neural networks (CNNs), and transfer learning. Initially, ML models achieve high accuracy, with the J48 model at 93.08% accuracy, mainly highlighting the importance of diagonal-wise standard deviation. Deep learning techniques are then applied, with three CNN models achieving test accuracies of 94.08%, 92.04%, and 93.72%. Transfer learning models also show promise, with InceptionV3 at 92.48% accuracy. Ensembling these models further boosts accuracy, with the ensemble of three CNN models reaching 97.42%. This research has the potential to advance ILD diagnosis, offering a robust computational framework that enhances accuracy and ultimately improves patient outcomes. Copyright 2024 Inderscience Enterprises Ltd.