Numerous behavioral or physiological biometric traits, including iris, signature, hand geometry, speech, palm print, face, etc. have been used to discriminate individuals in a number of security applications over the last 30 years. Among these, hand-based biometric systems have come to the attention of researchers worldwide who utilize them for low- to medium-security applications such as financial transactions, access control, law enforcement, border control, computer security, time and attendance systems, dormitory meal plan access, etc. Several approaches for biometric recognition have been summarized in the literature. The survey in this article focuses on the interface between various hand modalities, summary of inner- and dorsal-knuckle print recognition, and fusion techniques. First, an overview of various feature extraction and classification approaches for knuckle print, a new entrant in the hand biometrics family with a higher user acceptance and invariance to emotions, is presented. Next, knuckle print fusion schemes with possible integration scenarios, and traditional capturing devices have been discussed. The economic relevance of various biometric traits, including knuckle print for commercial and forensic applications is debated. Finally, conclusions related to the scope of knuckle print as a biometric trait are drawn and some recommendations for the development of hand-based multimodal biometrics have been presented.

Knuckle Print Biometrics and Fusion Schemes -- Overview, Challenges, and Solutions

With development in the area of electronics and artificial intelligence (AI), medical devices (MD) have been sophisticated as well. MD management strategies today are very different than decades ago, so it is reasonable to consider how we can prepare for where we are going in the future. This paper presents the result of application of machine learning (ML) techniques in management of infant incubators in healthcare institutions. A total of 140 samples was used for development of Expert system based on ML classifiers. These samples were collected during 2015–2017 period, as part of yearly inspections of incubators in healthcare institutions by ISO 17020 accredited laboratory. Dataset division 80–20 was used for classifiers development and validation. Performance of the following machine learning algorithms was investigated: Naive Bayes (NB), Decision Tree (DT), Random Forest (RF), k-Nearest Neighbour (kNN), and Support Vector Machine (SVM). Resulting classifiers were compared by performance and classifier based on Decision Tree algorithm yielded highest accuracy (98.5%) among other tested systems. Obtained results suggest that by introducing ML algorithms in MD management strategies benefit healthcare institution firstly in terms of increase of safety and quality of patient diagnosis and treatments, but also in cost optimization and resource management.

Prediction of medical device performance using machine learning techniques: infant incubator case study

Evidence-based clinical engineering: Machine learning algorithms for prediction of defibrillator performance

In today’s world, machine learning has gained much popularity, and its algorithms are employed in every field such as pattern recognition, object detection, text interpretation and different research areas. Machine learning, a part of AI (artificial intelligence), is used in the designing of algorithms based on the recent trends of data. This paper aims at introducing the algorithms of machine learning, its principles and highlighting the advantages and disadvantages in this field. It also focuses on the advancements that have been carried out so that the current researchers can be benefitted out of it. Based on artificial intelligence, many techniques have been developed such as perceptron-based techniques and logic-based techniques and also in statistics, instance-based techniques and Bayesian networks. So, overall this paper produces the work done by the authors in the area of machine learning and its applications and to draw attention towards the scholars who are working in this field.

Machine Learning: A Review of the Algorithms and Its Applications

State-of-the-art review of geotechnical-driven artificial intelligence techniques in underground soil-structure interaction

Machine learning is one of the most exciting recent technologies in Artificial Intelligence. Learning algorithms in many applications that’s we make use of daily. Every time a web search engine like Google or Bing is used to search the internet, one of the reasons that works so well is because a learning algorithm, one implemented by Google or Microsoft, has learned how to rank web pages. Every time Facebook is used and it recognizes friends' photos, that's also machine learning. Spam filters in email saves the user from having to wade through tons of spam email, that's also a learning algorithm. In this paper, a brief review and future prospect of the vast applications of machine learning has been made.

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Applications of Artificial Intelligence in Machine Learning: Review and Prospect

Identification is an essential part of our lives. Identification of authentic candidate is essential in E-commerce, in keeping track of criminals, in airport and railway surveillance and many more aspects of the modern world. In this paper an approach has been proposed combining the knuckle and fingerprint biometrics for the purpose of identification of a person using SIFT (Scale Invariant Feature Transform).

An Approach for Identification Using Knuckle and Fingerprint Biometrics Employing Wavelet Based Image Fusion and SIFT Feature Detection

Biometric surveillance is any technology which measures and analyzes human physical and/or behavioural characteristics for authentication, identification, or screening purposes. Identification is an essential part of our lives. Identification of authentic candidate is essential in E-commerce, in keeping track of criminals, in airport and railway surveillance and many more aspects of the modern world. However identification of a person can be challenging especially when the person is not cooperating. This leads to classify the identification techniques broadly in to two categories: Passive and Active. In this current study an approach has been proposed combining the ear and face biometrics for the purpose of identification of a person using SIFT (Scale Invariant Feature Transform). The proposed multi biometric system achieves a recognition accuracy of 99.9958%.

An approach for identification using ear and face biometrics employing score based image fusion and SIFT feature detection in surveillance system

In this paper a new steganographic technique using an advanced cryptographic technique has been proposed. In the encryption part of this paper the concept of ASCII value of the English alphabets have been used. Besides this the binary conversion of those ASCII values and the concept of hamming distance measurement are also used here. Multiple Auxiliary keys are generated using one password and after the encryption, the steganographic part is done with the help of grey-scale concept.

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Akash Pal

Papers

Applications of Artificial Intelligence in Machine Learning: Review and Prospect

An Approach for Identification Using Knuckle and Fingerprint Biometrics Employing Wavelet Based Image Fusion and SIFT Feature Detection

An approach for identification using ear and face biometrics employing score based image fusion and SIFT feature detection in surveillance system

Optimized Block Steganography based Crypt Encryption for Secured Data Transfer