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Article ; Online: Vocal-friend: internet of social-things framework to aid verbal communication.

Khullar, Vikas / Singh, Harjit Pal

Disability and rehabilitation. Assistive technology

2022 Volume 18, Issue 8, Page(s) 1527–1535

Abstract: Purpose: Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are ... ...

Abstract	Purpose: Deficits in social verbal communication in individuals with Social Communication Disorder (SCD) is of concern and SCD in the human community is prevalent in large population throughout the globe. Deficits in verbal social communication are prevalent in a large population. This paper aimed to propose internet connected multi-system architecture which is capable to support verbal communication in a social environment for individuals with social communication deficits. Material and methods: Implementation methodology was included with corpus collection for specific communication, deep learning based machine training for intelligent communication, and implementation of the trained algorithm on internet connected electronic multiple social communication devices. The implemented system is smart enough to initiate and maintain two types of communication; the first type includes communication between multiple individuals on the remote location and the second type includes communication with the individual present in the physical listening range. Results: The system was investigated in terms of its algorithmic parameters and found 97% to 100% in terms of training and testing accuracy with negligible mean squared error. Vocal-Friend analysed results based on audio-bot simulative conditions provide more than 91% accuracy, interaction rate and fallback rate. On the basis of the satisfaction analysis, above average results were noticed. Conclusion: In terms of technical implementations and satisfaction analysis, results found acceptable with above average score.IMPLICATION FOR REHABILITATIONProposed framework is easy to use by caregivers with even having little knowledge.Support individual with deficit to learn social verbal communication skill to survive in society.Aiding parents, caregivers and professionals to understand the communication needs of individuals with communication deficits.Since technology is also grooming in the domain of rehabilitation, so this system could be used in various future applications such as social robots, social virtual assistants etc.
MeSH term(s)	Humans ; Friends ; Communication ; Parents ; Internet ; Caregivers
Language	English
Publishing date	2022-04-11
Publishing country	England
Document type	Journal Article
ZDB-ID	2221782-4
ISSN	1748-3115 ; 1748-3107
ISSN (online)	1748-3115
ISSN	1748-3107
DOI	10.1080/17483107.2022.2060349
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Article ; Online: IoT-Fog-enabled robotics-based robust classification of hazy and normal season agricultural images for weed detection

Kansal Isha / Khullar Vikas / Verma Jyoti / Popli Renu / Kumar Rajeev

Paladyn: Journal of Behavioral Robotics, Vol 14, Iss 1, Pp pp. 99-

2023 Volume 103

Abstract: The mechanization of farming is currently the most pressing problem facing humanity and a burgeoning academic field. Over the last decade, there has been an explosion of Internet of Things (IoT) application growth in agriculture. Agricultural robotics is ...

Abstract	The mechanization of farming is currently the most pressing problem facing humanity and a burgeoning academic field. Over the last decade, there has been an explosion of Internet of Things (IoT) application growth in agriculture. Agricultural robotics is bringing about a new era of farming because they are growing more intelligent, recognizing causes of variation on the farm, consuming fewer resources, and optimizing their efficiency to more flexible jobs. The purpose of this article is to construct an IoT-Fog computing equipped robotic system for the categorization of weeds and soy plants during both the hazy season and the normal season. The used dataset in this article included four classes: soil, soybean, grass, and weeds. A two-dimensional Convolutional Neural Network (2D-CNN)-based deep learning (DL) approach was implemented for data image classification with dataset of height and width of 150 × 150 and of three channels. The overall proposed system is considered an IoT-connected robotic device that is capable of applying classification through the Internet-connected server. The reliability of the device is also enhanced as it is enabled with edge-based Fog computing. Hence, the proposed robotic system is capable of applying DL classification through IoT as well as Fog computing architecture. The analysis of the proposed system was conducted in steps including training and testing of CNN for classification, validation of normal images, validation of hazy images, application of dehazing technique, and at the end validation of dehazed images. The training and validation parameters ensure 97% accuracy in classifying weeds and crops in a hazy environment. Finally, it concludes that applying the dehazing technique before identifying soy crops in adverse weather will help achieve a higher classification score.
Keywords	agriculture ; deep learning ; convolution neural network ; dehazing ; classification ; weed ; robotics ; Technology ; T
Subject code	571
Language	English
Publishing date	2023-03-01T00:00:00Z
Publisher	De Gruyter
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Cross-Silo, Privacy-Preserving, and Lightweight Federated Multimodal System for the Identification of Major Depressive Disorder Using Audio and Electroencephalogram.

Gupta, Chetna / Khullar, Vikas / Goyal, Nitin / Saini, Kirti / Baniwal, Ritu / Kumar, Sushil / Rastogi, Rashi

Diagnostics (Basel, Switzerland)

2023 Volume 14, Issue 1

Abstract: In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early ... ...

Abstract	In this day and age, depression is still one of the biggest problems in the world. If left untreated, it can lead to suicidal thoughts and attempts. There is a need for proper diagnoses of Major Depressive Disorder (MDD) and evaluation of the early stages to stop the side effects. Early detection is critical to identify a variety of serious conditions. In order to provide safe and effective protection to MDD patients, it is crucial to automate diagnoses and make decision-making tools widely available. Although there are various classification systems for the diagnosis of MDD, no reliable, secure method that meets these requirements has been established to date. In this paper, a federated deep learning-based multimodal system for MDD classification using electroencephalography (EEG) and audio datasets is presented while meeting data privacy requirements. The performance of the federated learning (FL) model was tested on independent and identically distributed (IID) and non-IID data. The study began by extracting features from several pre-trained models and ultimately decided to use bidirectional short-term memory (Bi-LSTM) as the base model, as it had the highest validation accuracy of 91% compared to a convolutional neural network and LSTM with 85% and 89% validation accuracy on audio data, respectively. The Bi-LSTM model also achieved a validation accuracy of 98.9% for EEG data. The FL method was then used to perform experiments on IID and non-IID datasets. The FL-based multimodal model achieved an exceptional training and validation accuracy of 99.9% when trained and evaluated on both IID and non-IIID datasets. These results show that the FL multimodal system performs almost as well as the Bi-LSTM multimodal system and emphasize its suitability for processing IID and non-IIID data. Several clients were found to perform better than conventional pre-trained models in a multimodal framework for federated learning using EEG and audio datasets. The proposed framework stands out from other classification techniques for MDD due to its special features, such as multimodality and data privacy for edge machines with limited resources. Due to these additional features, the framework concept is the most suitable alternative approach for the early classification of MDD patients.
Language	English
Publishing date	2023-12-25
Publishing country	Switzerland
Document type	Journal Article
ZDB-ID	2662336-5
ISSN	2075-4418
ISSN	2075-4418
DOI	10.3390/diagnostics14010043
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Neural Network-based Handheld Diagnosis System for Autism Spectrum Disorder.

Khullar, Vikas / Singh, Harjit Pal / Bala, Manju

Neurology India

2021 Volume 69, Issue 1, Page(s) 66–74

Abstract: Objective: The aim of the present work was to propose and implement deep neural network (DNN)-based handheld diagnosis system for more accurate diagnosis and severity assessment of individuals with autism spectrum disorder (ASD).: Methods: Initially, ...

Abstract	Objective: The aim of the present work was to propose and implement deep neural network (DNN)-based handheld diagnosis system for more accurate diagnosis and severity assessment of individuals with autism spectrum disorder (ASD). Methods: Initially, the learning of the proposed system for ASD diagnosis was performed by implementing DNN algorithms such as a convolutional neural network (CNN) and long short-term memory (LSTM), and multilayer perceptron (MLP) with DSM-V based acquired dataset. The performance of the DNN algorithms was analyzed based on parameters viz. accuracy, loss, mean squared error (MSE), precision, recall, and area under the curve (AUC) during the training and validation process. Later, the optimum DNN algorithm, among the tested algorithms, was implemented on handheld diagnosis system (HDS) and the performance of HDS was analyzed. The stability of proposed DNN-based HDS was validated with the dataset group of 20 ASD and 20 typically developed (TD) individuals. Results: It was observed during comparative analysis that LSTM resulted better in ASD diagnosis as compared to other artificial intelligence (AI) algorithms such as CNN and MLP since LSTM showed stabilized results achieving maximum accuracy in less consumption of epochs with minimum MSE and loss. Further, the LSTM based proposed HDS for ASD achieved optimum results with 100% accuracy in reference to DSM-V, which was validated statistically using a group of ASD and TD individuals. Conclusion: The use of advanced AI algorithms could play an important role in the diagnosis of ASD in today's era. Since the proposed LSTM based HDS for ASD and determination of its severity provided accurate results with maximum accuracy with reference to DSM-V criteria, the proposed HDS could be the best alternative to the manual diagnosis system for diagnosis of ASD.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Autism Spectrum Disorder/diagnosis ; Humans ; Neural Networks, Computer
Language	English
Publishing date	2021-03-01
Publishing country	India
Document type	Journal Article
ZDB-ID	415522-1
ISSN	1998-4022 ; 0028-3886
ISSN (online)	1998-4022
ISSN	0028-3886
DOI	10.4103/0028-3886.310069
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Article ; Online: Spoken buddy for individuals with autism spectrum disorder.

Khullar, Vikas / Singh, Harjit Pal / Agarwal, Ambuj Kumar

Asian journal of psychiatry

2021 Volume 62, Page(s) 102712

MeSH term(s)	Autism Spectrum Disorder ; Humans
Language	English
Publishing date	2021-06-03
Publishing country	Netherlands
Document type	Letter
ZDB-ID	2456678-0
ISSN	1876-2026 ; 1876-2018
ISSN (online)	1876-2026
ISSN	1876-2018
DOI	10.1016/j.ajp.2021.102712
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Article ; Online: From slides to insights

Verma Jyoti / Sandhu Archana / Popli Renu / Kumar Rajeev / Khullar Vikas / Kansal Isha / Sharma Ashutosh / Garg Kanwal / Kashyap Neeru / Aurangzeb Khursheed

Open Life Sciences, Vol 18, Iss 1, Pp 908-

Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology

2023 Volume 14

Keywords	prognostic survival prediction ; colorectal cancer ; deep learning ; histopathological analysis ; retrospective multicenter study ; image patches ; Biology (General) ; QH301-705.5
Language	English
Publishing date	2023-12-01T00:00:00Z
Publisher	De Gruyter
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: From slides to insights: Harnessing deep learning for prognostic survival prediction in human colorectal cancer histology.

Verma, Jyoti / Sandhu, Archana / Popli, Renu / Kumar, Rajeev / Khullar, Vikas / Kansal, Isha / Sharma, Ashutosh / Garg, Kanwal / Kashyap, Neeru / Aurangzeb, Khursheed

Open life sciences

2023 Volume 18, Issue 1, Page(s) 20220777

Abstract: Prognostic survival prediction in colorectal cancer (CRC) plays a crucial role in guiding treatment decisions and improving patient outcomes. In this research, we explore the application of deep learning techniques to predict survival outcomes based on ... ...

Abstract	Prognostic survival prediction in colorectal cancer (CRC) plays a crucial role in guiding treatment decisions and improving patient outcomes. In this research, we explore the application of deep learning techniques to predict survival outcomes based on histopathological images of human colorectal cancer. We present a retrospective multicenter study utilizing a dataset of 100,000 nonoverlapping image patches from hematoxylin & eosin-stained histological images of CRC and normal tissue. The dataset includes diverse tissue classes such as adipose, background, debris, lymphocytes, mucus, smooth muscle, normal colon mucosa, cancer-associated stroma, and colorectal adenocarcinoma epithelium. To perform survival prediction, we employ various deep learning architectures, including convolutional neural network, DenseNet201, InceptionResNetV2, VGG16, VGG19, and Xception. These architectures are trained on the dataset using a multicenter retrospective analysis approach. Extensive preprocessing steps are undertaken, including image normalization using Macenko's method and data augmentation techniques, to optimize model performance. The experimental findings reveal promising results, demonstrating the effectiveness of deep learning models in prognostic survival prediction. Our models achieve high accuracy, precision, recall, and validation metrics, showcasing their ability to capture relevant histological patterns associated with prognosis. Visualization techniques are employed to interpret the models' decision-making process, highlighting important features and regions contributing to survival predictions. The implications of this research are manifold. The accurate prediction of survival outcomes in CRC can aid in personalized medicine and clinical decision-making, facilitating tailored treatment plans for individual patients. The identification of important histological features and biomarkers provides valuable insights into disease mechanisms and may lead to the discovery of novel prognostic indicators. The transparency and explainability of the models enhance trust and acceptance, fostering their integration into clinical practice. Research demonstrates the potential of deep learning models for prognostic survival prediction in human colorectal cancer histology. The findings contribute to the understanding of disease progression and offer practical applications in personalized medicine. By harnessing the power of deep learning and histopathological analysis, we pave the way for improved patient care, clinical decision support, and advancements in prognostic prediction in CRC.
Language	English
Publishing date	2023-12-13
Publishing country	Poland
Document type	Journal Article
ZDB-ID	2817958-4
ISSN	2391-5412 ; 2391-5412
ISSN (online)	2391-5412
ISSN	2391-5412
DOI	10.1515/biol-2022-0777
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Article ; Online: IoT based assistive companion for hypersensitive individuals (ACHI) with autism spectrum disorder.

Khullar, Vikas / Singh, Harjit Pal / Bala, Manju

Asian journal of psychiatry

2019 Volume 46, Page(s) 92–102

Abstract: Objective: Today, most of the individuals with Autism Spectrum Disorders (ASD) have atypical sensory behaviors. The main aim of this study is to propose an assistive intervention for supporting the overloaded sensory responses in hypersensitive ... ...

Abstract	Objective: Today, most of the individuals with Autism Spectrum Disorders (ASD) have atypical sensory behaviors. The main aim of this study is to propose an assistive intervention for supporting the overloaded sensory responses in hypersensitive individuals with ASD. Methods: The vision, auditory, smell, and physical balance related multi-sensors based hardware prototype, namely Assistive Companion for Hypersensitive Individuals (ACHI) has been designed for individuals with ASD. The proposed ACHI prototype is an assistive-technology based companion for hypersensitive individuals with ASD which is able to 'fetch/detect the sensory information using electronic sensors', 'making the decision using fuzzy logic on the basis of fetched sensory information' and then, 'transmit the generated information over the internet through the Internet of Things (IoT)', and also able for 'generating alerts to caregivers'. The proposed design is also capable of providing audio & video feedback to calm down individuals with ASD. Results: After testing, it is observed that 93% percent of the caregivers rated the proposed ACHI intervention on the scale of above average. The remarkable reduction in hyperactive states related triggering incidents in ASD has been found with the use of ACHI. Conclusion: The present work and the proposed prototype can identify and control the sensory overload triggers in ASD and it can guide the caregiver or clinicians to optimize the responsible surrounding causes of explosive behavior in ASD and would help the individuals with ASD to become calm.
MeSH term(s)	Adolescent ; Adult ; Autism Spectrum Disorder/complications ; Autism Spectrum Disorder/physiopathology ; Autism Spectrum Disorder/rehabilitation ; Caregivers ; Child ; Female ; Humans ; Internet ; Male ; Monitoring, Ambulatory ; Neurophysiological Monitoring ; Self-Help Devices ; Sensation Disorders/etiology ; Sensation Disorders/physiopathology ; Sensation Disorders/rehabilitation ; Telemedicine ; Young Adult
Language	English
Publishing date	2019-09-27
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	2456678-0
ISSN	1876-2026 ; 1876-2018
ISSN (online)	1876-2026
ISSN	1876-2018
DOI	10.1016/j.ajp.2019.09.030
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Article: Estimating the Impact of Covid-19 Outbreak on High-Risk Age Group Population in India

Singh, Harjit Pal / Khullar, Vikas / Sharma, Monica

Augmented Human Research

Abstract: The new pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), originated at Wuhan, Hubei province, China in December 2019, threatening the world and becomes the public health crisis throughout the globe Due to changing data ...

Abstract	The new pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), originated at Wuhan, Hubei province, China in December 2019, threatening the world and becomes the public health crisis throughout the globe Due to changing data and behavior of the current epidemic, appropriate pharmacological techniques to cure are getting delayed day by day The estimated trends of the global and Indian region for COVID-19 epidemic were predicted for the next 21 days till 05/05/ 2020 on the data recorded till 14/04/2020 in the present work The main focus of the work was to estimate the trends of COVID-19 outbreak on population, especially the high-risk age group of elderly people (with age 50 years and greater) in the Republic of India It was observed that this identified age-group could be more prone to SARS-CoV-2 virus infection and chances of death in this age group could be more The high-risk Indian states/regions were also identified throughout the nation and trends for infection, death, and cured cases were predicted for the next 21 days The outcome of the present work was presented in terms of suggestions that the proper social and medical care for the identified high-risk age group of elderly people of the Indian population should be required to prevent the COVID-19 community transmission The work also supported the extension in countrywide proper lockdown, mass testing, and also the strict rules to follow social distancing
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #620878
Database	COVID19

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Article ; Online: Estimating the Impact of Covid-19 Outbreak on High-Risk Age Group Population in India

Singh, Harjit Pal / Khullar, Vikas / Sharma, Monica

Augmented Human Research

2020 Volume 5, Issue 1

Keywords	covid19
Language	English
Publisher	Springer Science and Business Media LLC
Publishing country	us
Document type	Article ; Online
ZDB-ID	2843091-8
ISSN	2365-4325 ; 2365-4317
ISSN (online)	2365-4325
ISSN	2365-4317
DOI	10.1007/s41133-020-00037-9
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