International Journal of Computer and Applications

コンピュータ・サイエンス : ACM computing surveys

Artificial Intelligence (AI) intent is to facilitate human limits. It is getting a standpoint on human administrations, filled by the growing availability of restorative clinical data and quick progression of insightful strategies. Motivated by the need to highlight the need for employing AI in battling the COVID-19 Crisis, this survey summarizes the current state of AI applications in clinical administrations while battling COVID-19. Furthermore, we highlight the application of Big Data while understanding this virus. We also overview various intelligence techniques and methods that can be applied to various types of medical information-based pandemic. We classify the existing AI techniques in clinical data analysis, including neural systems, classical SVM, and edge significant learning. Also, an emphasis has been made on regions that utilize AI-oriented cloud computing in combating various similar viruses to COVID-19. This survey study is an attempt to benefit medical practitioners and medical researchers in overpowering their faced difficulties while handling COVID-19 big data. The investigated techniques put forth advances in medical data analysis with an exactness of up to 90%. We further end up with a detailed discussion about how AI implementation can be a huge advantage in combating various similar viruses.

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AI Techniques for COVID-19

, This paper proposes an efficient fingerprint recognition algorithm combining phase-based image matching and feature-based matching. The use of Fourier phase information of fingerprint images makes possible to achieve robust recognition for weakly impressed, low-quality fingerprint images. Experimental evaluations using two different types of fingerprint image databases demonstrate efficient recognition performance of the proposed algorithm compared with a typical minutiae-based algorithm and the conventional phase-based algorithm.

A fingerprint recognition algorithm combining phase-based image matching and feature-based matching

Technological advancements in high-throughput sequencing have lead to a tremendous increase in the amount of genomic data produced. With the cost being down to 2,000 USD for a single human genome, sequencing dozens of individuals is a task that is feasible even for smaller project or organizations already today. However, generating the sequence is only one issue; another one is storing, managing, and analyzing it. These tasks become more and more challenging due to the sheer size of the data sets and are increasingly considered to be the most severe bottlenecks in larger genome projects. One possible countermeasure is to compress the data; compression reduces costs in terms of requiring less hard disk storage and in terms of requiring less bandwidth if data is shipped to large compute clusters for parallel analysis. Accordingly, sequence compression has recently attracted much interest in the scientific community. In this paper, we explain the different basic techniques for sequence compression, point to distinctions between different compression tasks (e.g., genome versus read compression), and present a comparison of current approaches and tools. To further stimulate progress in genome compression research, we also identify key challenges for future systems.

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Trends in Genome Compression

DNA matching has become one of the most used biometric identification method during the last several years. DNA stores the information for creating and organizing an organism. It can be thought of as a string over the alphabets {A, C, G, T, N}, which makes four chemical components that make it up. Here, N represents an unknown nucleotide. This unknown nucleotide may be either A, or C, or G, or T. The size of each sequence is varying in the range of millions to billions of nucleotides. Compression of DNA is interesting for both practical reasons (such as reduced storage and transmission cost) and functional reasons (such as inferring structure and function from compression models). We present a new Lossless Compression algorithm; which compresses data first horizontally and then vertically. It is based on substitution and statistical methods. We claim that our algorithm achieves one of the best compression ratios for bench mark DNA sequences in comparison to other DNA sequence compression methods.

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An Efficient Horizontal and Vertical Method for Online DNA Sequence Compression

Biometric systems are alternates to the traditional identification systems. This paper provides an overview of single feature and multiple features based biometric systems, including the performance of physiological characteristics (such as fingerprint, hand geometry, head recognition, iris, retina, face recognition, DNA recognition, palm prints, heartbeat, finger veins, palates etc) and behavioral characteristics (such as body language, facial expression, signature verification, speech recognition, Gait Signature etc.). The fingerprints, iris image, and DNA features based multimodal systems and their performances are analyzed in terms of security, reliability, accuracy, and long-term stability. The strengths and weaknesses of various multiple features based biometric approaches published so far are analyzed. The directions of future research work for robust personal identification is outlined.

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Fingerprints, Iris and DNA Features based Multimodal Systems: A Review

The twins are classified into two categories namely fraternal and identical twins. Fraternal twins differ in face structures and DNA sequences but, identical twins have the same face structure and share same DNA sequence. Therefore, it is difficult to identify identical twins on the basis of their faces and DNA sequences. In this research paper, we have introduced a new approach for identifying identical twins on the basis of minutiae coordinates, orientation angles, and minutiae distances of their thumbprint images. We tested the proposed method on thumbprint images of an identical twin pair generated by using Incept H3 T&A Terminal and fifty pairs of identical twins of FVC04, and FVC06 datasets. We have found that the proposed approach is superior, and robust in comparison to existing techniques in terms of accuracy, efficiency, and storage space.

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Minutiae Distances and Orientation Fields Based Thumbprint Identification of Identical Twins

Biometric systems based on a single physiological or behavioral characteristic may not be able to identify a person correctly. This paper presents an efficient and reliable multimodal biometric identification system which is based on minutiae thumb, Eigen iris and DNA sequence features. In this method compressed form of Short tandem repeat (STR) part of DNA sequence, compressed thumbprint and compressed iris image (Eigen values) of a person are used for further identification of an individual. Therefore, personal identification including identical twins and dead person’s cases will become easier in using this method. Our technique will correctly identify a person (living or dead) on the basis of his thumbprint, iris image and DNA sequence features. We have used thumbprint and iris images for identifying a live person. But, for identifying a dead person we have used STR part of DNA sequence. We tested our method for 100 samples of thumbprints and iris images of CASIA database and we found that our multimodal method is able to correctly identify each and every individual including identical twin on the basis of thumbprints

A framework towards using eigen iris, minutiae thumb and dna sequence features for personal identification

This paper introduces an efficient Eigen values based technique for online iris image compression and identification of a human including the case of identical twins. The iris image is extracted after removing the pupil, eye brow, skin and other noise disturbances from an actual image. The extracted iris image is divided into different blocks of size of 16 × 16. Now, Eigen values are calculated for each block and these Eigen values are stored in the smart card memory for further identification. Therefore, when checking if two iris images are identical or not, all we need is to compare the stored Eigen values with online calculated Eigen values. If two iris images have the same Eigen values, this means that both iris images belong to the same person. In our research, we have concluded that iris images of different persons have different Eigen values, including the case of identical twins. We conducted experiments on CASIA and Multimedia University iris image databases and we found that our Eigen Values Based Iris Image Identification Technique is giving 99.99% accuracy for the same image of identical twins and individuals. The implementation leads us to believe that our method is giving the best matching result in the case of identical twins and individuals. It is an efficient, secure and economically feasible approach for online personal identification.

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Prakash C. Srivastava

Papers

An Efficient Horizontal and Vertical Method for Online DNA Sequence Compression

Fingerprints, Iris and DNA Features based Multimodal Systems: A Review

Minutiae Distances and Orientation Fields Based Thumbprint Identification of Identical Twins

A framework towards using eigen iris, minutiae thumb and dna sequence features for personal identification

An efficient eigen values based technique for online iris image compression and identification