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A Comprehensive Survey on Federated Learning Applications in Computational Mental Healthcare

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Computer Modeling in Engineering & Sciences 2025年第1期142卷 49-90页

作者： Vajratiya Vajrobol Geetika jain Saxena Amit Pundir Sanjeev Singh Akshat Gaurav Savi Bansal Razaz Waheeb Attar Mosiur Rahman Brij B.Gupta Institute of Informatics and Communication University of DelhiDelhi110021India Maharaja Agrasen College University of DelhiDelhi110096India Computer Engineering Ronin InstituteMontclairNJ 07043USA Department of Research and Innovation Insights2TechinfoJaipur302001India University Centre for Research and Development(UCRD) Chandigarh UniversityChandigarh140413India Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia CCRI&Department of Computer Science and Information Engineering Asia UniversityTaichung413Taiwan Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India

Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing *** aims to alleviate the workload on healthcare professionals and aid *** applications have been developed to support the challenges in intelligent healthcare ***,because mental health data is sensitive,privacy concerns have *** learning has gotten some *** research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare *** explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health *** research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security *** survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.

关键词： Depression emotional recognition intelligent healthcare systems mental health federated learning stress detection sleep behaviour

EPASAD:ellipsoid decision boundary based Process-Aware Stealthy Attack Detector

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Cybersecurity 2024年第3期7卷 32-48页

作者： Vikas Maurya rachit Agarwal Saurabh Kumar Sandeep Shukla Department of Computer Science and Engineering Indian Institute of Technology KanpurkanpurIndia Merkle Science BangaloreIndia

Due to the importance of Critical Infrastructure(Cl)in a nation's economy,they have been lucrative targets for cyber *** critical infrastructures are usually Cyber-Physical Systems such as power grids,water,and sewage treatment facilities,oil and gas pipelines,*** recent times,these systems have suffered from cyber attacks numer-ous *** have been developing cyber security solutions for Cls to avoid lasting *** to standard frameworks,cyber security based on identification,protection,detection,response,and recovery are at the core of these *** of an ongoing attack that escapes standard protection such as firewall,anti-virus,and host/network intrusion detection has gained importance as such attacks eventually affect the physical dynamics of the ***,anomaly detection in physical dynamics proves an effective means to implement *** is one example of anomaly detection in the sensor/actuator data,representing such systems physical *** present EPASAD,which improves the detection technique used in PASAD to detect these micro-stealthy attacks,as our experiments show that PASAD's spherical boundary-based detection fails to *** method EPASAD overcomes this by using Ellipsoid boundaries,thereby tightening the boundaries in various dimen-sions,whereas a spherical boundary treats all dimensions *** validate EPASAD using the dataset produced by the TE-process simulator and the C-town *** results show that EPASAD improves PASAD's average recall by 5.8%and 9.5%for the two datasets,respectively.

关键词： Intrusion detection system Critical infrastructure security Industrial control system Machine learning

Identifying malicious accounts in blockchains using domain names and associated temporal properties

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Blockchain(Research and Applications) 2023年第3期4卷 39-51页

作者： Rohit Kumar Sachan rachit Agarwal Sandeep Kumar Shukla C3i Hub Indian Institute of Technology KanpurKanpur 208016India CSE Department Indian Institute of Technology KanpurKanpur 208016India Bennet University Greater Noida 201310India Merkle Science Bangalore 560102India

The rise in the adoption of blockchain technology has led to increased illegal activities by cybercriminals costing billions of *** machine learning algorithms are applied to detect such illegal *** algorithms are often trained on the transaction behavior and,in some cases,trained on the vulnerabilities that exist in the *** our approach,we study the feasibility of using the Domain Name(DN)associated with the account in the blockchain and identify whether an account should be tagged malicious or ***,we leverage the temporal aspects attached to the *** approach achieves 89.53%balanced-accuracy in detecting malicious blockchain *** our results identify 73769 blockchain DNs that show malicious behavior at least once,out of these,34171 blockchain DNs show persistent malicious behavior,resulting in 2479 malicious blockchain DNs over ***,none of these identified malicious DNs were reported in new officially tagged malicious blockchain DNs.

关键词： Blockchain Machine learning Suspect identification Domain name Temporal properties

Neoadjuvant therapy for esophageal cancer

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World Journal of Gastrointestinal Oncology 2014年第10期6卷 403-406页

作者： rachit D Shah Anthony D Cassano James P Neifeld De-partment of Surgery Virginia Commonwealth University School of Medicine Richmond VA 23298-0068 United States

Esophageal cancer is increasing in incidence more than any other visceral malignancy in North America. Adenocarcinoma has become the most common cell type. Surgery remains the primary treatment modality for locoregional disease. Overall survival with surgery alone has been dismal, with metastatic disease the primary mode of treatment failure after an R0 surgical resection. Cure rates with chemotherapy or radiation therapy alone have been disappointing as well. For these reasons, over the last decade multi-modality treatment has gained increasing acceptance as the standard of care. This review examines the present data and role of neoadjuvant treatment using chemotherapy and radiation therapy followed by surgery for the treatment of esophageal cancer.

关键词： Neoadjuvant therapy Esophageal cancer Esophagectomy Chemotherapy

Image smog restoration using oblique gradient profile prior and energy minimization

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Frontiers of Computer Science 2021年第6期15卷 147-153页

作者： Ashok KUMAR Arpit jain College of Computing Science and Information Technogy TeerthankerMahaveer UniversityMoradabad 244001India

Removing the smog from digital images is a challenging pre-processing tool in various imaging ***,many smog removal(i.e.,desmogging)models are proposed so far to remove the effect of smog from *** desmogging models are based upon a physical model,it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy ***,many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric ***,these models utilized computationally extensive minimization of an energy ***,the existing restoration models suffer from various issues such as distortion of texture,edges,and ***,in this paper,a convolutional neural network(CNN)is used to estimate the physical attributes of smoggy *** gradient channel prior(OGCP)is utilized to restore the smoggy ***,a dataset of smoggy and sunny images are ***,we have trained CNN to estimate the smog gradient from smoggy ***,based upon the computed smog gradient,OGCP is utilized to restore the still smoggy *** analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.

关键词： convolutional neural networks desmogging smog oblique gradient channel prior

＂NEW＂ resource recovery from wastewater using bioelectrochemical systems： Moving forward with functions

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Frontiers of Environmental Science & Engineering 2018年第4期12卷 3-15页

作者： Akshay jain Zhen He Department of Civil and Environmental Engineering Virginia Polytechnic Institute and State University Blacksburg VA 24061 USA

Bioelectrochemical systems （BES） have been extensively studied ｜br resource recovery from wastewater. By taking advantage of interactions between microorganisms and electrodes, BES can accomplish wastewater treatment while simultaneously recovering various resources including nutrients, energy and water （＂NEW＂）. Despite much progress in laboratory sit, dies, BES have not been advanced to practical applications. This paper aims to provide some subjective opinions and a concise discussion of several key challenges in BES-based resource recovery and help identil- the potential application niches that may guide further technological development. In addition to further increasing recovery efficiency, it is also important to have more lbcus on the applications of the recovered resources such as how to use the harvested electricity and gaseous energy and how to separate the recovered nutrients in an energy-efficient way. A changc in mindset fur energy performance of BES is necessary to understand overall energy production and consumption. Scaling up BES can go through laboratory scale, transitional scale, and then pilot scale. Using functions as driving forces lbr BES research and development will better guide the investment of efforts.

关键词： Bioelectrochemical systemsResource recoveryWastewater treatmentEnergy Nutrients

Real-Time Analysis of COVID-19 Pandemic on Most Populated Countries Worldwide

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Computer Modeling in Engineering & Sciences 2020年第12期125卷 943-965页

作者： Meenu Gupta Rachna jain Akash Gupta Kunal jain Department of Computer Science and Engineering Chandigarh UniversityPunjab140301India Department of Computer Science and Engineering Bharati Vidyapeeth’s College of EngineeringNew Delhi110063India

The spread of the Severe Acute Respiratory Syndrome Coronavirus 2(SARS-CoV-2)has already taken on pandemic extents,influencing even more than 200 nations in a couple of ***,regulation measures in China have decreased new cases by over 98%,this decrease is not the situation everywhere,and most of the countries still have been affected by *** objective of this research work is to make a comparative analysis of the top 5 most populated countries namely United States,India,China,Pakistan and Indonesia,from 1st January 2020 to 31st July *** research work also targets to predict an increase in the number of deaths and total infected cases in these five *** our research,the performance of the proposed framework is determined by using three Machine Learning(ML)regression algorithms namely Linear Regression(LR),Support Vector Regression(SVR),andRandom Forest(RF)*** proposed model is also validated upon the infected and death cases of further *** performance of these three algorithms is compared using the RootMean Square Error(RMSE)*** Forest algorithm shows best performance as compared to other proposed algorithms,with the lowest RMSE value in the prediction of total infected and total deaths cases for all the top five most populated countries.

关键词： COVID-19 SARS COV-2 country-wise analysis most populated countries pandemic mortality rate

Tracking Control of Uncertain Nonlinear Systems With Unknown Constant Input Delay

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IEEE/CAA Journal of Automatica Sinica 2020年第2期7卷 420-425页

作者： Ashish Kumar jain Shubhendu Bhasin Department of Electrical Engineering Indian Institute of Technology DelhiNew Delhi-110016India

A robust delay compensator has been developed for a class of uncertain nonlinear systems with an unknown constant input *** control law consists of feedback terms based on the integral of past control values and a novel filtered tracking error,capable of compensating for input *** Lyapunov-Krasovskii functionals are used to prove global uniformly ultimately bounded(GUUB)tracking,provided certain sufficient gain conditions,dependent on the bound of the delay,are *** results illustrate the performance and robustness of the controller for different values of input delay.

关键词： Global uniformly ultimately bounded stability Lyapunov-Krasovskii functionals nonlinear systems tracking control unknown input delay

Performance evaluation of deep neural networks for forecasting time-series with multiple structural breaks and high volatility

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CAAI Transactions on Intelligence Technology 2021年第3期6卷 265-280页

作者： Rohit Kaushik Shikhar jain Siddhant jain Tirtharaj Dash Department of Computer Science and Information Systems Birla Institute of Technology and Science PilaniK.K.Birla Goa CampusZuarinagarGoaIndia

The problem of automatic and accurate forecasting of time-series data has always been an interesting challenge for the machine learning and forecasting community.A majority of the real-world time-series problems have non-stationary characteristics that make the understanding of trend and seasonality *** applicability of the popular deep neural networks(DNNs)as function approximators for non-stationary TSF is *** following DNN models are evaluated:Multi-layer Perceptron(MLP),Convolutional Neural Network(CNN),and RNN with Long Short-Term Memory(LSTM-RNN)and RNN with Gated-Recurrent Unit(GRU-RNN).These DNN methods have been evaluated over 10 popular Indian financial stocks ***,the performance evaluation of these DNNs has been carried out in multiple independent runs for two settings of forecasting:(1)single-step forecasting,and(2)multi-step *** DNN methods show convincing performance for single-step forecasting(one-day ahead forecast).For the multi-step forecasting(multiple days ahead forecast),the methods for different forecast periods are *** performance of these methods demonstrates that long forecast periods have an adverse effect on performance.

关键词： networks neural forecasting