Human Body Odour as a Biometric Indicator for Person Identification
Integrated Edge Thresholded Deep Network for Image Denoising
A Pattern-Based Approach to Detect Irony in Twitter Sentiment Analysis
Multimodal Biometrics System with Face, Ear, and Foot Fusion Techniques
IoT-Based Ultrasonic Sensors and Buzzer Systems in Home Security
An Efficient Foot Ulcer Determination System for Diabetic Patients
Landslide Susceptibility Mapping through Weightages Derived from Statistical Information Value Model
Statistical Wavelet based Adaptive Noise Filtering Technique for MRI Modality
Real Time Sign Language: A Review
Remote Sensing Schemes Mingled with Information and Communication Technologies (ICTS) for Flood Disaster Management
FPGA Implementation of Shearlet Transform Based Invisible Image Watermarking Algorithm
A Comprehensive Study on Different Pattern Recognition Techniques
User Authentication and Identification Using NeuralNetwork
Flexible Generalized Mixture Model Cluster Analysis withElliptically-Contoured Distributions
Efficient Detection of Suspected areas in Mammographic Breast Cancer Images
In biometric identification, various physiological and behavioral traits such as fingerprints, facial features, iris patterns, and voice have been extensively explored. This paper introduces human body odour as a biometric trait for person identification. Body odour, composed of volatile organic compounds unique to each individual, presents a challenge for biometric authentication. The study explores the scientific basis of human body odour, including its chemical composition and uniqueness. The paper presents the enrollment process for capturing and storing body odour data, emphasizing the potential of electronic nose (E-nose) devices for scent detection and pattern recognition. This paper discusses the advantages of body odour biometrics, including its resistance to masking by artificial scents and its potential to reduce password administration costs. The proposed odour biometric system offers a non-intrusive and reliable means of person identification, particularly in scenarios where traditional biometric modalities may be impractical or ineffective.
The outcome of the denoising network suffers from over-smoothing effect, due to this, the texture content of the object will be lost. Lack of accurate texture properties in the image may lead to inefficient object segmentation and classification. This paper proposes an edge-preserving thresholding approach and applies it to the output of the denoised network. The thresholding approach relies on the distance and weight factors, which move the noisy components toward the mean of the subspace. This proposal is meant to treat over and under-smoothed components, where the smoothing decrement or increment is controlled by the threshold calculated with the average mean of the components in the respective subspace. The approach is compared with state-of-the-art methods in terms of image quality, and it is observed that this approach increases the quality proportionately. The result depicts that there is a significant improvement in PSNR of about 0.7~ 1 dB with the proposed integrated mechanism when compared against the conventional CNN-based image denoiser. Moreover, the edge details are better preserved with the proposed integrated mechanism.
Twitter sentiment analysis poses challenges due to the informal language, limited character count, and prevalence of sarcasm, which can alter the polarity of messages. This paper presents a pattern-based approach to detect irony in Twitter sentiment analysis. By analyzing various types of irony and identifying their patterns, this paper proposes a methodology to improve the efficiency of sentiment analysis. Tweets are classified into different categories based on their sarcasm using a machine learning algorithm. The proposed approach involves feature extraction from tweets, including sentiment-related features, punctuation-related features, grammatical and phonetic features, and patternbased features. A hybrid pattern extraction with a classification model is employed to process tweet data and classify it as sarcastic or not. Experimental results demonstrate the effectiveness of the proposed approach in detecting sarcasm in tweets, with precision ranging from 84.6% to 98.1% across different classifier algorithms. This pattern-based approach offers promising results for enhancing sentiment analysis on Twitter and understanding the nuances of communication in social media discourse.
Biometrics, as an identification method, is used for various applications, particularly in security technologies. The integration of multiple biometric sources aims to overcome limitations observed in unimodal systems, enhancing recognition accuracy. Fusion techniques, categorized into sensor level, feature level, matching score level, decision level, and rank level, are explored to optimize the combination of information from different modalities. Various fusion schemes, such as feature-level fusion, decision-level fusion, and hybrid systems, are investigated for their effectiveness in integrating diverse biometric traits. This paper details the fusion schemes at different levels, including sensor, feature extraction, matching score, and decision levels. Experimental results demonstrate the efficacy of the proposed multimodal biometric system. The Equal Error Rate (EER) is analyzed to evaluate system accuracy, with weights assigned to each modality based on their performance. Normalization techniques and fusion rules are applied to combine modalities, resulting in enhanced matching scores. The analysis of results showcases the performance of the system across various fusion combinations. Notably, the combination of ear and foot modalities yields the highest matching score, demonstrating the effectiveness of fusion techniques in multimodal biometrics.
With the increasing demand for security in various aspects of daily life, including homes, the need for reliable and costeffective security systems has increased. This paper presents an Internet of Things (IoT)-based approach to home security utilizing ultrasonic sensors and buzzer systems. The proposed system, implemented using Arduino microcontrollers, offers a wireless solution for detecting intruders within a specified range using ultrasonic sensors. Upon detecting an object, the system activates a piezoelectric buzzer, effectively acting as an alarm. Additionally, the sensor data is processed to generate a graphical representation, providing visual feedback on obstruction status. A servo motor is employed to rotate the ultrasonic sensor, extending the coverage area. The system's setup, working principle, advantages, drawbacks, and future scope are discussed, highlighting its potential for enhancing home security while offering flexibility for future enhancements and integration with advanced features.
