Sumit Dalal

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Naji A . Majedkan

University of Duhok

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Oluwatosin Omotehinwa

Maysoon A . Mohammed

Rakesh Mohanty

IIT Madras

Journal of Computer Science IJCSIS

mazlina abdul majid

Universiti Malaysia Pahang (UMP)

Ishwari Rajput

Amity University, Noida

Lotfi Boussaid

Jayanti Khatri

Poornima University, Jaipur

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Papers by Sumit Dalal

Improved Dynamic Round Robin Process Scheduling

Process Scheduling is one of the fundamental concepts of operating system. Round robin process sc... more Process Scheduling is one of the fundamental concepts of operating system. Round robin process scheduling algorithm is optimal process scheduling algorithm in time sharing systems. The efficiency of the processor depends on the choice of time slice in time sharing systems. The time quantum taken in round robin algorithm is static due to which throughput of the processor decreases. In this paper, selection of time quantum is discussed and a new process scheduling algorithm for time sharing systems named as improved dynamic round robin algorithm is proposed. Proposed algorithm incorporates advantages of both "round robin" and "shortest job first" process scheduling algorithms which tends to reduce chances of starvation and increases throughput of the system. Round robin process scheduling algorithm has high process switch rates and accordingly average waiting time, average turnaround time and system throughput are adversely affected. This situation can be improved ...

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A Systematic Review of Smart Mental Healthcare

SSRN Electronic Journal, 2019

Over the years, the growth and use of technology in daily life is undeniably fast. Everything is ... more Over the years, the growth and use of technology in daily life is undeniably fast. Everything is going to be smart with the technology use, from home to vehicle, devices to factories etc. Enabling factors for this smartness are ubiquitous network, sensors or actuators, embedded devices or things, long battery life, internet/web connected devices. In medical domain also, due to increasing burden on the existing architecture, costly medical services, and 24*7 and remote area service unavailability, the use of technology is now gaining momentum. To cure a patient with mental disorder is unquestionably tough as this type of healthcare is very patient-centric, a smaller number of psychiatrist/patient ratio and the social stigmas. So, smart mental healthcare is the need of hour. In this paper we propose a semantic web of things driven, cost and time effective solution towards the self-help initiative for diagnosing and treating patients with depressive disorders while also addressing the current state of art of the discipline. We have selected research material from various databases like Google Scholar, IEEE Explore, Science Direct, PubMed, Web of Science etc. The papers are reviewed with respect to the architecture used in various mental healthcare systems, services of IoT in mental healthcare.

An Investigation of Data Requirements for the Detection of Depression from Social Media Posts

Recent Patents on Engineering

Background: Only a fraction of the produced social media data is usable in mental health assessme... more Background: Only a fraction of the produced social media data is usable in mental health assessment. So the problem of sufficient training data for deep learning approaches arises. Data sufficiency can be presented in terms of number of users or number of posts per user. Objective: We examine the data need of machine learning and deep learning models for a practical system and let researcher chose best fitting models depending on the dataset type available with them. We perform distinct experiments to find the effect of these issues on depression classification by various approaches. Methods: We explored various machine learning and deep learning techniques on various data set versions, taken from Twitter and Reddit, with varying numbers of users and posts per user. Diagnosed and control users are taken in different ratios to assess the impact of an imbalanced dataset. Results: The results reveal that SVM achieved 68% accuracy in depression classification for 70 users each from diag...