Author name cluster

Deepak Gupta

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20 papers

2 author rows

JBHI Journal 2026 Journal Article

A 6G-Enabled Hierarchical Contrastive Learning Framework for Multi-Scale Medical Time Series Analysis

Le Sun
Jie Lin
Zhiguo Qu
Yimin Yu
Jinliang Liu
Deepak Gupta
Yanchun Zhang

Medical time series analysis, particularly for electrocardiogram (ECG) and electroencephalogram (EEG) signals, is essential in modern diagnostics, supporting early detection of conditions such as arrhythmias and epileptic seizures. However, existing approaches often struggle to capture multi-scale periodic patterns and longrange dependencies while meeting real-time processing demands. The envisioned 6G networks, with their terahertz communication and integrated sensing and communication (ISAC) capabilities, will generate vast volumes of high-fidelity physiological data at the network edge. This paradigm shift intensifies the conflict between the computational complexity of advanced AI models and the limited resources of edge devices, creating a critical bottleneck for deploying sophisticated analytics in real-world healthcare scenarios. To overcome these limitations, this paper introduces a 6G-enabled hierarchical contrastive learning framework, referred to as Hierarchical Contrastive Learning for Multi-Scale Medical time series analysis (HCL-MSM), which integrates three core components: a signal-adaptive encoder based on multi-period decomposition and 2D convolution, a patient-level contrastive module enhanced with decomposable multi-scale mixing, and a 6G-edge deployment module optimized via quantization and pruning. The framework effectively models nested physiological rhythms and cross-time dependencies in medical data, while maintaining low-latency operation under resource-constrained edge environments. We evaluated HCL-MSM on multiple clinical datasets under simulated 6G settings. Our framework achieves significant gains in arrhythmia detection, seizure prediction, and neurological monitoring. We evaluated HCL-MSM on multiple clinical datasets under simulated 6G settings. Our framework achieves significant gains in arrhythmia detection (F1-score: 86. 39 percent), seizure prediction (Recall: 87. 72 percent), and neurological monitoring (Recall: 87. 8 percent), outperforming existing state-of- the-art methods.

JBHI Journal 2026 Journal Article

DTQFL: A Digital Twin-Assisted Quantum Federated Learning Algorithm for Intelligent Diagnosis in 5G Mobile Network

Zhiguo Qu
Yang Li
Bo Liu
Deepak Gupta
Prayag Tiwari

Smart healthcare aims to revolutionize medical services by integrating artificial intelligence (AI). The limitations of classical machine learning include privacy concerns that prevent direct data sharing among medical institutions, untimely updates, and long training times. To address these issues, this study proposes a digital twin-assisted quantum federated learning algorithm (DTQFL). By leveraging the 5G mobile network, digital twins (DT) of patients can be created instantly using data from various Internet of Medical Things (IoMT) devices and simultaneously reduce communication time in federated learning (FL) at the same time. DTQFL generates DT for patients with specific diseases, allowing for synchronous training and updating of the variational quantum neural network (VQNN) without disrupting the VQNN in the real world. This study utilized DTQFL to train its own personalized VQNN for each hospital, considering privacy security and training speed. Simultaneously, the personalized VQNN of each hospital was obtained through further local iterations of the final global parameters. The results indicate that DTQFL can train a good VQNN without collecting local data while achieving accuracy comparable to that of data-centralized algorithms. In addition, after personalized training, the VQNN can achieve higher accuracy than that without personalized training.

JBHI Journal 2026 Journal Article

Energy-Efficient Online Continual Learning for Time Series Classification in Nanorobot-Based Smart Health

Le Sun
Qingyuan Chen
Min Zheng
Xin Ning
Deepak Gupta
Prayag Tiwari

Nanorobots have been used in smart health to collect time series data such as electrocardiograms and electroencephalograms. Real-time classification of dynamic time series signals in nanorobots is a challenging task. Nanorobots in the nanoscale range require a classification algorithm with low computational complexity. First, the classification algorithm should be able to dynamically analyze time series signals and update itself to process the concept drift (CD). Second, the classification algorithm should have the ability to handle catastrophic forgetting (CF) and classify historical data. Most importantly, the classification algorithm should be energy-efficient to use less computing power and memory to classify signals in real-time on a smart nanorobot. To solve these challenges, we design an algorithm that can Prevent Concept Drift in Online continual Learning for time series classification (PCDOL). The prototype suppression item in PCDOL can reduce the impact caused by CD. It also solves the CF problem through the replay feature. The computation per second and the memory consumed by PCDOL are only 3. 572 M and 1 KB, respectively. The experimental results show that PCDOL is better than several state-of-the-art methods for dealing with CD and CF in energy-efficient nanorobots.

EAAI Journal 2026 Journal Article

Entropy based fuzzy multi-view twin random vector functional link for class imbalanced data

Jyoti Maurya
Deepak Gupta

Multi-view learning (MVL) is employed to improve generalization performance through the integration of information collected from multiple views. Following MVL principles, and in conjunction with the twin random vector Functional Link (TRVFL) network and fuzzy entropy, we introduce here a new method of Entropy based Fuzzy Multi-view Twin Random Vector Functional Link (EFMvTRVFL) for real-world dataset classification. By taking the fuzzy membership value for each sample and allocating it according to the entropy value, samples with higher class certainty are allocated relatively greater fuzzy membership. The proposed model is designed to address uncertainty and class imbalance in real-world datasets by assigning fuzzy membership values based on entropy, thereby giving more importance to samples with higher class certainty. EFMvTRVFL retains the structural benefits of TRVFL but employs multi-view learning to learn complementary information from various feature representations. The novelty of EFMvTRVFL is the integration of fuzzy entropy-based membership assignment and multi-view TRVFL into a single optimization framework, whereas EFTWSVM and MvTWSVM lack random feature mapping and clear entropy regularization. Experimental evaluation on 20 benchmark datasets demonstrates that EFMvTRVFL consistently outperforms existing models in terms of both accuracy and stability. The results confirm the model's effectiveness in handling high-dimensional, imbalanced, and noisy data.

EAAI Journal 2025 Journal Article

A robust fuzzy twin support vector machine with kernel-target alignment for binary classification

Deepak Gupta
Barenya Bikash Hazarika
Umesh Gupta
Witold Pedrycz

Many algorithms similar to the twin version of the support vector machine and their variants have shown better results in the binary classification of nonlinear data points. But in the presence of outlier and noise, these algorithms exhibit low generalization efficiency. To alleviate this challenge, recently proposed, kernel-target alignment based fuzzy least square twin bounded support vector machine (KTA-FLSTBSVM) used fuzzy membership values and is solved using least squares. Inspired by this strategy, for further improvement, we propose a novel approach called decision support kernel-target alignment based fuzzy least square twin bounded support vector machine (DS-KFIFTBSVM). DS-KFIFTBSVM considers the kernelized fuzzy membership values with the regularized twin support vector machine and solves for linear and nonlinear data points using a functional iterative approach. In DS-KFIFTBSVM, the solution is obtained by solving a linearly convergent iterative scheme rather than solving quadratic programming problems. The proposed DS-KFIFTBSVM offers better generalization efficiency, which has been evaluated using both linear and Gaussian kernels, mostly on artificially developed and publicly accessible datasets with diverse dimensionalities. In terms of various performance evaluation metrics, including specificity, precision, false positive rate, rate of misclassification error, F_score, and geometric mean in linear and non-linear cases, DS-KFIFTBSVM outperforms various baseline approaches. It shows the highest accuracy in various datasets, including 98. 1884 % for musk dataset (linear kernel) and 100 % for the glass dataset (Gaussian kernel). Further statistical analysis confirms its classification efficiency.

EAAI Journal 2025 Journal Article

Entropy-based fuzzy 1norm twin random vector functional link networks for binary class imbalance learning

Chittabarni Sarkar
Deepak Gupta
Barenya Bikash Hazarika
Rajat Subhra Goswami

Real-world datasets may contain dissimilar numbers of positive and negative class samples. Class imbalance is a common issue in classification tasks, where one class (the majority) significantly outnumbers another (the minority). This can lead to conventional models failing to predict the minority class, which is more important accurately. Hence, in this work we have proposed a model with high generalization capability on imbalanced datasets to address this problem. The proposed Entropy-based Fuzzy 1-norm Twin Random Vector Functional Link Network (EFTRVFL1norm) solves the binary class imbalance learning problem. In the training phase, this model evaluates entropy values based on the concept of k nearest neighbour of two samples that belong to two distinct classes. To improve model's performance, samples with higher class certainty or lower entropy values are associated with larger fuzzy memberships. Entropy-driven fuzzy membership pays more consideration to the samples with higher class probability by providing relatively large fuzzy members to majority samples compared to minority class samples. Moreover, the model becomes robust and generalized in the presence of a 1-norm which generates sparse output vectors. The performance analysis is undertaken based on two evaluators: Area under the curve and Geometric mean by comparing the results with some related models such as twin support vector machine, kernel ridge regression (KRR), affinity-based fuzzy KRR (AFKRR), random vector functional link layer network (RVFL), intuitionistic fuzzy RVFL classifier (IFRVFLC) and 1-norm twin RVFL (TRVFL1norm). The comparing results demonstrate the model's classification efficiency for computing binary imbalanced classification problems.

EAAI Journal 2025 Journal Article

Multimodal feature fusion for human activity recognition using human centric temporal transformer

Samee Ullah Khan
Maryam Sultana
Sufyan Danish
Deepak Gupta
Norah Saleh Alghamdi
Suchang Woo
Dong-Gyu Lee
Sangtae Ahn

In recent years, human activity recognition (HAR) has focused considerable interest due to its manifold monitoring applications. Mainstream HAR approaches often face challenges with the reliability of results when relying on a single data modality, especially when integrating heterogeneous data sources. A notable limitation of the implemented artificial intelligence (AI) models is their limited capability to handle dynamic scenarios, as they lack the necessary contextual information from multiple sources, which impedes the models’ adaptability and accuracy. This paper proposes a multi-modality framework for HAR that fuses human concern patterns using various spatiotemporal model flavors. In addition, to get the spatial features, a swin transformer with a dual attention concept is applied to process visual sensor data, while one-dimensional convolutional neural network leverages human skeleton information obtained from the detection model with numerous key points. Later, these multi-modality features are fused to improve the robust analysis and comprehension of activities. Next, these resulting features are passed to the human centric temporal transformer (HCTT), that has the capabilities to process multimodal sequence data for temporal learning. Moreover, the attention block of HCTT enables human-related attentive patterns followed by a dual fusion mechanism. The proposed model was evaluated on four open-access large-scale HAR datasets, where comprehensive ablation studies and comparative analyses demonstrated that our developed multimodal approach outperforms recent baseline HAR models. This underscores its potential for advancing AI applications and human activity analysis.

TMLR Journal 2025 Journal Article

Rethinking the Value of Training-Free Structured Pruning of LLMs

Nahush Lele
Arnav Chavan
Aryamaan Thakur
Deepak Gupta

This paper investigates the effectiveness of training-free structured pruning techniques for Large Language Models (LLMs), with a particular focus on depth and width pruning strategies. Through an extensive empirical evaluation across a diverse range of tasks, datasets and modalities, we reveal critical limitations in current pruning methods. While some tasks exhibit minimal performance degradation, others face significant deterioration, even at low pruning rates, contradicting prior findings that often rely on selective benchmarks. Our analysis also finds that depth pruning, despite its simplicity, usually outperforms the more granular width pruning approaches in maintaining downstream task performance. Our findings highlight that existing evaluations of pruned LLMs often overstate their effectiveness due to incomplete or limited evaluation tasks, necessitating a critical reassessment of the true value of pruning and emphasizing the need to explore more robust pruning algorithms.

JBHI Journal 2025 Journal Article

Revolutionizing Wearable Sensor Data Analysis With an Automated Decision-Making Model for Enhanced Human Activity Detection

Nitesh Bharot
Priyanka Verma
Ankit Vidyarthi
Deepak Gupta
John G. Breslin

Human Activity Recognition (HAR) stands as a crucial technology, with applications ranging from healthcare monitoring to sports analytics. However, the traditional approach to HAR is often time-consuming and susceptible to human errors due to the high complexities involved in processing diverse sensor data. Recognizing the imperative for efficiency and accuracy in HAR systems, we propose the development of an Automated Decision-maker (ADM) system. This system serves to automate HAR pipelines, addressing the challenges posed by the huge sensor data. By harnessing the power of automation, ADM significantly streamlines the HAR process, reducing the time required for hyperparameter tuning and minimizing the risk of human errors. The results obtained from our proposed ADM system demonstrate notable improvements in HAR performance, showcasing achieved accuracy of 96. 436% for UCI-HAR & 99. 783% for PAMAP2 datasets. Moreover, ADM can be described as an innovative approach that contributes to the optimization of HAR systems while also establishing a foundation for building robust and reliable systems in complex environments.

EAAI Journal 2024 Journal Article

Advancements in artificial intelligence for biometrics: A deep dive into model-based gait recognition techniques

Anubha Parashar
Apoorva Parashar
Mohammad Shabaz
Deepak Gupta
Aditya Kumar Sahu
Muhammad Attique Khan

Over the past decade, Deep Learning (DL) pipelines have undergone significant evolution and demonstrated effectiveness in addressing complex challenges within artificial intelligence domains. The construction of tailored DL pipelines for specific applications necessitates a solid grasp of deep learning principles and the range of intermediary layers at one’s disposal. Crafting a DL pipeline involves leveraging appropriate datasets for the intended application and iteratively refining the pipeline by navigating through intermediary layers. The process of selecting and validating configurations demands substantial time and meticulous consideration, making it intricate to identify an optimal and resilient DL pipeline that excels across pertinent datasets. This article seeks to support researchers in comprehending diverse gait sensing technologies while establishing a foundational understanding of deep learning concepts to expedite problem-solving. A comprehensive overview of gait biometrics tailored for surveillance applications is presented herein. The fundamental aspects of deep learning pipelines are expounded upon, encompassing their selection criteria and implications for specific problems. Recent pivotal research on deep learning models is surveyed, encompassing their performance across varying application datasets. By elucidating the merits and limitations of these approaches, this work guides the derivation of an optimized pipeline achieved through a fusion of existing alternatives. The ultimate objective is to attain swifter yet precise outcomes for a given problem.

JBHI Journal 2024 Journal Article

DCNet: A Self-Supervised EEG Classification Framework for Improving Cognitive Computing-Enabled Smart Healthcare

Yiyang Zhang
Le Sun
Deepak Gupta
Xin Ning
Prayag Tiwari

Cognitive computing endeavors to construct models that emulate brain functions, which can be explored through electroencephalography (EEG). Developing precise and robust EEG classification models is crucial for advancing cognitive computing. Despite the high accuracy of supervised EEG classification models, they are constrained by labor-intensive annotations and poor generalization. Self-supervised models address these issues but encounter difficulties in matching the accuracy of supervised learning. Three challenges persist: 1) capturing temporal dependencies in EEG; 2) adapting loss functions to describe feature similarities in self-supervised models; and 3) addressing the prevalent issue of data imbalance in EEG. This study introduces the DreamCatcher Network (DCNet), a self-supervised EEG classification framework with a two-stage training strategy. The first stage extracts robust representations through contrastive learning, and the second stage transfers the representation encoder to a supervised EEG classification task. DCNet utilizes time-series contrastive learning to autonomously construct representations that comprehensively capture temporal correlations. A novel loss function, SelfDreamCatcherLoss, is proposed to evaluate the similarities between these representations and enhance the performance of DCNet. Additionally, two data augmentation methods are integrated to alleviate class imbalances. Extensive experiments show the superiority of DCNet over the current state-of-the-art models, achieving high accuracy on both the Sleep-EDF and HAR datasets. It holds substantial promise for revolutionizing sleep disorder detection and expediting the development of advanced healthcare systems driven by cognitive computing.

IJCAI Conference 2024 Conference Paper

Faster and Lighter LLMs: A Survey on Current Challenges and Way Forward

Arnav Chavan
Raghav Magazine
Shubham Kushwaha
Merouane DEBBAH
Deepak Gupta

Despite the impressive performance of LLMs, their widespread adoption faces challenges due to substantial computational and memory requirements during inference. Recent advancements in model compression and system-level optimization methods aim to enhance LLM inference. This survey offers an overview of these methods, emphasizing recent developments. Through experiments on LLaMA(/2)-7B, we evaluate various compression techniques, providing practical insights for efficient LLM deployment in a unified setting. The empirical analysis on LLaMA(/2)-7B highlights the effectiveness of these methods. Drawing from survey insights, we identify current limitations and discuss potential future directions to improve LLM inference efficiency. We release the codebase to reproduce the results presented in this paper at https: //github. com/nyunAI/Faster-LLM-Survey

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JBHI Journal 2024 Journal Article

IoMT-Based Smart Healthcare Detection System Driven by Quantum Blockchain and Quantum Neural Network

Zhiguo Qu
Wenke Shi
Bo Liu
Deepak Gupta
Prayag Tiwari

Electrocardiogram (ECG) is the main criterion for arrhythmia detection. As a means of identification, ECG leakage seems to be a common occurrence due to the development of the Internet of Medical Things. The advent of the quantum era makes it difficult for classical blockchain technology to provide security for ECG data storage. Therefore, from the perspective of safety and practicality, this article proposes a quantum arrhythmia detection system called QADS, which achieves secure storage and sharing of ECG data based on quantum blockchain technology. Furthermore, a quantum neural network is used in QADS to recognize abnormal ECG data, which contributes to further cardiovascular disease diagnosis. Each quantum block stores the hash of the current and previous block to construct a quantum block network. The new quantum blockchain algorithm introduces a controlled quantum walk hash function and a quantum authentication protocol to guarantee legitimacy and security while creating new blocks. In addition, this article constructs a hybrid quantum convolutional neural network called HQCNN to extract the temporal features of ECG to detect abnormal heartbeats. The simulation experimental results show that HQCNN achieves an average training and testing accuracy of 94. 7% and 93. 6%. And the detection stability is much higher than classical CNN with the same structure. HQCNN also has certain robustness under the perturbation of quantum noise. Besides, this article demonstrates through mathematical analysis that the proposed quantum blockchain algorithm has strong security and can effectively resist various quantum attacks, such as external attacks, Entanglement-Measure attack and Interception-Measurement-Repeat attack.

TMLR Journal 2024 Journal Article

Pushing the Limits of Gradient Descent for Efficient Learning on Large Images

Deepak Gupta
Gowreesh Mago
Arnav Chavan
Dilip Prasad
Rajat Mani Thomas

Traditional deep learning models are trained and tested on relatively low-resolution images (< 300 px), and cannot be directly operated on large-scale images due to compute and memory constraints. We propose Patch Gradient Descent (PatchGD), an effective learning strategy that allows us to train the existing CNN and transformer architectures (hereby referred to as deep learning models) on large-scale images in an end-to-end manner. PatchGD is based on the hypothesis that instead of performing gradient-based updates on an entire image at once, it should be possible to achieve a good solution by performing model updates on only small parts of the image at a time, ensuring that the majority of it is covered over the course of iterations. PatchGD thus extensively enjoys better memory and compute efficiency when training models on large-scale images. PatchGD is thoroughly evaluated on PANDA, UltraMNIST, TCGA, and ImageNet datasets with ResNet50, MobileNetV2, ConvNeXtV2, and DeiT models under different memory constraints. Our evaluation clearly shows that PatchGD is much more stable and efficient than the standard gradient-descent method in handling large images, especially when the compute memory is limited. Code is available at https://github.com/nyunAI/PatchGD.

ICRA Conference 2024 Conference Paper

VERSE: Virtual-Gradient Aware Streaming Lifelong Learning with Anytime Inference

Soumya Banerjee
Vinay Kumar Verma
Avideep Mukherjee
Deepak Gupta
Vinay P. Namboodiri
Piyush Rai

Lifelong learning or continual learning is the problem of training an AI agent continuously while also preventing it from forgetting its previously acquired knowledge. Streaming lifelong learning is a challenging setting of lifelong learning with the goal of continuous learning in a dynamic non-stationary environment without forgetting. We introduce a novel approach to lifelong learning, which is streaming (observes each training example only once), requires a single pass over the data, can learn in a class-incremental manner, and can be evaluated on-the-fly (anytime inference). To accomplish these, we propose a novel virtual gradients based approach for continual representation learning which adapts to each new example while also generalizing well on past data to prevent catastrophic forgetting. Our approach also leverages an exponential-moving-average-based semantic memory to further enhance performance. Experiments on diverse datasets with temporally correlated observations demonstrate our method’s efficacy and superior performance over existing methods.

EAAI Journal 2023 Journal Article

Affinity based fuzzy kernel ridge regression classifier for binary class imbalance learning

Barenya Bikash Hazarika
Deepak Gupta

The class imbalance learning (CIL) problem indicates when one class have very low proportions of samples (minority class) compared to the other class (majority class). Even though kernel ridge regression (KRR) shows high generalization ability at a considerably quicker learning speed than conventional machine learning algorithms, it fails to achieve an excellent result for CIL problems. To address this inherent limitation of KRR, a novel affinity-based fuzzy KRR (AFKRR) is proposed for dealing with the binary CIL problem. In AFKRR, an affinity-based fuzzy membership value is linked to each training sample. The affinity of the majority class datapoint is measured using the support vector data description (SVDD) trained by the majority class datapoints. The classification ability of the proposed AFKRR is calculated using the area under the receiver optimal characteristics curve (AUC), F-measure and Geometric mean. AFKRR’s performance is compared with the support vector machine (SVM), affinity and class probability-based fuzzy SVM (ACFSVM), entropy-based fuzzy SVM (EFSVM), improved density weighted least squares SVM (IDLSSVM-CIL), KRR and intuitionistic fuzzy KRR (IFKRR) models on a few real-world as well as a few artificial imbalanced datasets. Experimental outcomes reveal the usability and efficacy of the proposed AFKRR model.

TMLR Journal 2023 Journal Article

Aux-Drop: Handling Haphazard Inputs in Online Learning Using Auxiliary Dropouts

Rohit Agarwal
Deepak Gupta
Alexander Horsch
Dilip K. Prasad

Many real-world applications based on online learning produce streaming data that is haphazard in nature, i.e., contains missing features, features becoming obsolete in time, the appearance of new features at later points in time and a lack of clarity on the total number of input features. These challenges make it hard to build a learnable system for such applications, and almost no work exists in deep learning that addresses this issue. In this paper, we present Aux-Drop, an auxiliary dropout regularization strategy for online learning that handles the haphazard input features in an effective manner. Aux-Drop adapts the conventional dropout regularization scheme for the haphazard input feature space ensuring that the final output is minimally impacted by the chaotic appearance of such features. It helps to prevent the co-adaptation of especially the auxiliary and base features, as well as reduces the strong dependence of the output on any of the auxiliary inputs of the model. This helps in better learning for scenarios where certain features disappear in time or when new features are to be modeled. The efficacy of Aux-Drop has been demonstrated through extensive numerical experiments on SOTA benchmarking datasets that include Italy Power Demand, HIGGS, SUSY and multiple UCI datasets. The code is available at https://github.com/Rohit102497/Aux-Drop.

JBHI Journal 2023 Journal Article

IoMT: A COVID-19 Healthcare System Driven by Federated Learning and Blockchain

Omaji Samuel
Akogwu Blessing Omojo
Abdulkarim Musa Onuja
Yunisa Sunday
Prayag Tiwari
Deepak Gupta
Ghulam Hafeez
Adamu Sani Yahaya

Internet of medical things (IoMT) has made it possible to collect applications and medical devices to improve healthcare information technology. Since the advent of the pandemic of coronavirus (COVID-19) in 2019, public health information has become more sensitive than ever. Moreover, different news items incorporated have resulted in differing public perceptions of COVID-19, especially on the social media platform and infrastructure. In addition, the unprecedented virality and changing nature of COVID-19 makes call centres to be likely overstressed, which is due to a lack of authentic and unregulated public media information. Furthermore, the lack of data privacy has restricted the sharing of COVID-19 information among health institutions. To resolve the above-mentioned limitations, this paper is proposing a privacy infrastructure based on federated learning and blockchain. The proposed infrastructure has the potentials to enhance the trust and authenticity of public media to disseminate COVID-19 information. Also, the proposed infrastructure can effectively provide a shared model while preserving the privacy of data owners. Furthermore, information security and privacy analyses show that the proposed infrastructure is robust against information security-related attacks.

EAAI Journal 2021 Journal Article

An insight into crash avoidance and overtaking advice systems for Autonomous Vehicles: A review, challenges and solutions

P. Shunmuga Perumal
M. Sujasree
Suresh Chavhan
Deepak Gupta
Venkat Mukthineni
Soorya Ram Shimgekar
Ashish Khanna
Giancarlo Fortino

Emergence of communication technologies made the automotive industries across the globe to embrace Advanced Driver Assistance Systems (ADAS) by considerable investments to ensure accident-free travel, reduction of pollution, fuel conservation. ADAS achieves its goals by integrating complex subsystems such as obstacle avoidance, overtaking advice, lane changing assistance, planning shortest routes, parking assistance, automatic gear shifting, etc. , using the emerging technologies. This article emphasizes the road safety aspect of the ADAS by exploring Crash Avoidance and Overtaking Advice (CAOA) subsystems. Existing studies have a noticeable lack of connectivity between various aspects of CAOA subsystems. This review deeply explores and connects CAOA subsystems like road geometries, road debris, obstacle avoidance algorithms powered by Artificial Intelligence (AI), overtaking advice systems, perception challenges of human drivers in various light and weather conditions, driver inattention and misjudgments, vehicle blind-spots, vehicle parameter analysis, performance of vision sensors, in-vehicle computers, driver–vehicle interactions, Vehicle to Infrastructure (V2I) technologies. This article emphasizes the three primary performance metrics of the ADAS, namely accuracy, response time and robustness. Finally, this article discusses a typical functional architecture and gaps identified in existing studies. This article is structured to assist like-minded researchers, who work on CAOA systems for road safety.

AIIM Journal 2020 Journal Article

Artificial plant optimization algorithm to detect heart rate & presence of heart disease using machine learning

Prerna Sharma
Krishna Choudhary
Kshitij Gupta
Rahul Chawla
Deepak Gupta
Arun Sharma

In today’s world, cardiovascular diseases are prevalent becoming the leading cause of death; more than half of the cardiovascular diseases are due to Coronary Heart Disease (CHD) which generates the demand of predicting them timely so that people can take precautions or treatment before it becomes fatal. For serving this purpose a Modified Artificial Plant Optimization (MAPO) algorithm has been proposed which can be used as an optimal feature selector along with other machine learning algorithms to predict the heart rate using the fingertip video dataset which further predicts the presence or absence of Coronary Heart Disease in an individual at the moment. Initially, the video dataset has been pre-processed, noise is filtered and then MAPO is applied to predict the heart rate with a Pearson correlation and Standard Error Estimate of 0. 9541 and 2. 418 respectively. The predicted heart rate is used as a feature in other two datasets and MAPO is again applied to optimize the features of both datasets. Different machine learning algorithms are then applied to the optimized dataset to predict values for presence of current heart disease. The result shows that MAPO reduces the dimensionality to the most significant information with comparable accuracies for different machine learning models with maximum dimensionality reduction of 81. 25%. MAPO has been compared with other optimizers and outperforms them with better accuracy.