Facial expression recognition using geometric features and modified hidden Markov model

This paper gives precise and comprehensive detail along with a proposed system for malware detection using ML and Deep Learning techniques by integrating both behavior-based detection methods and signature-based methods. The primary purpose of this paper is (A) Outline difficulty identified with malware detection. (B) Represent detail and categorized ML technique for malware detection. (C) Investigating the structure of basic strategies in malware discovery. (D) Inspecting the essential deep learning approach for malware detection using a grouping of malware inside the data mining. The point of interest and downside of various malware detection approaches were analyzed based on evaluation strategy and their capability. The proposed model uses random forest for making an end-to-end pipeline for malware detection. During comparative study with five other state of the art models, the proposed model obtained accuracy of 99.7% on the dataset. The experimental results show the proposed model outperformed other five state of the art techniques. This research paper encourages the researcher to think about the best approach for malware detection.

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Malware Analysis Through Random Forest Approach

Kumar

Abhishek

Shandilya

et al. 2020

JWE

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Mobile Notification System for Blood Pressure and Heartbeat Anomaly Detection

Pandey

Hicks

Goyal

et al. 2020

JWE

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In today’s fast-paced world where patients may need to be remotely monitored while they are away or out of hospitals, there is a need for mobile applications that can gather the biometric and biomedical signals from any number of devices and sensors, collecting biometric or biomedical data from a patient. This work presents the improvement of a circulatory strain and heartbeat anomaly detection and notice apparatus as an Android application that permits quick discovery of any variations from the norm in a patient's fundamental dependent on the Pan-Tompkins algorithm and reports it to the pertinent emergency clinic or clinical staff. The blood ECG information can be gotten from the health tracker sensors by means of a Bluetooth association and the patient can enter their Blood pressure esteems. In this case, the information gathered from a set of reproduced data, which is identified with a triggering notice from Firebase Cloud function. This notification is further acknowledged by the enrolled specialist (or any clinical faculty or health sector laborer having a similar application). The system's security part is represented by the fine-grained consent procedure which directs that solitary significant authorizations are required for the best possible working of the application which ought to be given to the application. An encryption method using the Blowfish algorithm is included as a feature of the developed mobile Android application to provide secure data transfer of the patient’s vital signals.

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Effect of Machine Learning Techniques for Efficient Classification of EMG Patterns in Gait Disorders

Kumar

Gautam

Semwal

2022

IJEER

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Gait disorder is very common in neurodegenerative diseases and differentiating among the same kinematic design is a very challenging task. The muscle activity is responsible for the creation of kinematic patterns. Hence, one optimal way to monitor this issue is to analyse the muscle pattern to identify the gait disorders. In this paper, we will investigate the possibility of identifying GAIT disorders using EMG patterns with the help of various machine learning algorithms. Twenty-five normal persons (13 male and 12 females, age around 28 years of age) and 21 persons having GAIT disorders (11 male and 10 females, age around 67 years of age). Four different machine learning algorithms have been used to identify EMG patterns to recognize healthy and unhealthy persons. The results obtained so far have been used to distinguish between GAIT disorders and healthy patients. Our proposed system can also prove that Recurrent Neural Network has achieved the best accuracy with 91.3 % in the case of two classes and 86.95 % in the case of three classes compared to other machine learning algorithms.

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Facial expression recognition using geometric features and modified hidden Markov model

Cited by 3 publications

References 33 publications

Malware Analysis Through Random Forest Approach

Malware Analysis Through Random Forest Approach

Mobile Notification System for Blood Pressure and Heartbeat Anomaly Detection

Effect of Machine Learning Techniques for Efficient Classification of EMG Patterns in Gait Disorders

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