A Novel Software Tool to Generate Customer Needs for Effective Design of Online Shopping Websites

Concurrency and Computation

Patil

2022

Self Cite

Depression is the primary cause of illness and injury in the country, with over 280 million people suffering from it as per the 2021 survey. Depression is one of the most common psychiatric illnesses in the world, affecting millions of people. Early detection of major depression symptoms and treatment with timely intervention can help to prevent the emergence of major depression. This has generated the need for some novel techniques to be utilized for depression detection to help doctors in diagnosing and treating depression effectively. Depression can be investigated through online posts, audio files, facial expressions, as well as through video files. In this regard, the research presents a detailed survey of the existing machine learning methods in depression detection, along with the different datasets available. The research explores automated depression detection strategies and the various ways to detect depression from text, audio, and video, and even examines the various systems and procedures for detecting depression using various criteria. More than 140 related research articles were considered for this review, with 80 of them being processed for comparative analysis based on various key performance indicators. The classifications of various methods used to detect stress, anxiety, and depression are highlighted followed by open research challenges and issues while analyzing depression techniques.

Section: Depression Detection From Video Featuresmentioning

confidence: 99%

Section: Classification Of Depression Detection Techniquesmentioning

confidence: 99%

Review of automated depression detection: Social posts, audio and video, open challenges and future direction

Yadav

Concurrency and Computation

Patil

2022

Self Cite

“…Quality Function Deployment (QFD) is one such decision making tool that can serve the purpose well. The use of QFD as a decision support tool has been demonstrated by 17,18 …”

Section: Introductionmentioning

confidence: 99%

“…The use of QFD as a decision support tool has been demonstrated by. 17,18 This research explores related research works that have presented a method for identifying depression in the public media as well as several pre-trained models on text, which are discussed in Section 2. The dataset used for the experiment is discussed in Section 3.…”

Section: Introductionmentioning

confidence: 99%

A novel automated depression detection technique using text transcript

Yadav

Int J Imaging Syst Tech

2022

Self Cite

“…Quality function deployment is one tool that has been utilized widely in various applications areas. Some of the works depicting it as strong decision-making tool are reported by Sharma et al [2][3][4] In addition, CPS systems are found utilized in application areas such as robots, medical devices, and intelligent buildings. Such CPS systems are utilized with hybridized ESVM-enhanced-support vector machine with enhanced Ada-Boost algorithm for classification for the detection of intrusion and fault classifications relying on TE-dataset.…”

Section: Introductionmentioning

confidence: 99%

An efficient cyber‐physical system using hybridized enhanced support‐vector machine with Ada‐Boost classification algorithm

Concurrency and Computation

Shandilya

2022

The necessity of cyber-security has obtained immense importance in day-to-day concerns of network communication. Therefore, several available research works predominantly focus on network security to protect the resources, services, and networks from any unauthorized access. A CPS (cyber-physical system) model using a dual mutation-based genetic algorithm, with feature classification through Ada-Boost and SVM classifier is proposed in this paper. Dual-mutation based genetic-algorithm overcomes the issues of conventional techniques including convergence issues and local fine-tuning of features. In this paper, necessary modifications were made to the existing Genetic Algorithm (GA) method to reduce the random nature of the traditional GA method. Particularly, the goal of this work is to develop the modified reproduction operators with appropriate fitness functions to guide simulations to gain optimal solutions. In floating-point representation, every chromosome vector has been coded as a floating-point number vector having the same length as the solution vector. Each element was selected initially, to stand within the desired domain, and operators were designed carefully in satisfying the constraints. As a result, there are various enhancements employed in the dual-mutation algorithm that handles local fine-tuned features.The relevant features of dataset samples are extracted and rescaled using feature selection and resampling phase aided by the Markov-resampling process. Followed by this, a hybrid approach of ESVM (enhanced support-vector machine) algorithm with Ada-Boost classifier is implemented for the fault classification process. The performance assessment was explicated in terms of accuracy-factor, F1-score, and execution time. Comparative analysis expounded the efficacy of the proposed model than other conventional methods attaining higher accuracy (97%), F1-score (99%) rates, and less execution time (15.33 s).