14 research outputs found

    Audio-Visual Automatic Speech Recognition Towards Education for Disabilities

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    Education is a fundamental right that enriches everyone’s life. However, physically challenged people often debar from the general and advanced education system. Audio-Visual Automatic Speech Recognition (AV-ASR) based system is useful to improve the education of physically challenged people by providing hands-free computing. They can communicate to the learning system through AV-ASR. However, it is challenging to trace the lip correctly for visual modality. Thus, this paper addresses the appearance-based visual feature along with the co-occurrence statistical measure for visual speech recognition. Local Binary Pattern-Three Orthogonal Planes (LBP-TOP) and Grey-Level Co-occurrence Matrix (GLCM) is proposed for visual speech information. The experimental results show that the proposed system achieves 76.60 % accuracy for visual speech and 96.00 % accuracy for audio speech recognition

    Advances of DNA computing in cryptography

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    Introduction to the special section on advances of machine learning in cybersecurity (VSI-mlsec)

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    With the rapid advancement of emerging technologies, such as Internet of Things (IoT), cloud computing, and many more, a huge amount of data is generated and processed in daily life. As these technologies are based on the internet, security issues are continuously increasing due to the presence of numerous hackers who try to hack users’ personal and confidential data by using security attacks. Sometimes, they replace authentic data with their fake data. The situation becomes more critical when a large number of users access and store their personal data outside of their own domain at the same time. Attackers mainly target the financial, healthcare, and defense sectors. Therefore, there must be strong security techniques to protect confidential or personal data against hackers and malicious users

    Analysis of the Pertinence of Indian Women's Institutions in Collaborative Research

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    Nowadays, the relevance of women-only educational institutes is questionable due to various reasons that include social uplifting, ample opportunities, and a free mindset among the population. Though women colleges and universities bring many female students to the purview of the traditional education system every year, other activities like research works and collaborative projects are still a concern for the educationists. In the current manuscript, the role of leading women-only universities and institutions is analyzed throughout India, to scrutinize its impact and opportunities in the research domain. All types of educational institutions and collaborative research work among them are represented as a social network. Different centrality metrics, such as closeness, betweenness, and eigenvector, are utilized to examine the influence of the individual institutes in the collaborative research network. An overlapping community detection method is proposed to perceive the posture of gender-biased universities in the mixed institutional model. The supremacy of the proposed approach is presented over the real-life benchmark datasets. The analytical results exhibit notable improvement over the state-of-the-art approaches and unfold a contemporary framework for strategic analysis in the higher education sector

    Improving security of medical big data by using Blockchain technology

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    Big data refers to a very large and diverse set of data that grow at exponential rates. In the modern healthcare system, medical big data face many security issues due to the presence of hackers and malicious users. Nowadays, medical big data are tremendously benefitted by Blockchain technology due to its several features, namely decentralization, confidentiality, security, privacy, etc. Nevertheless, the conventional cloud and client-server-based information storage models in healthcare are suffering from single-point failure, centralized control of data resources and privacy leakage. This paper explores the usages of Blockchain technology to manage the healthcare system by providing a solution to these problems. Here, a distributed scheme is proposed for data management, which is used to implement Blockchain technology in the healthcare sector. The proposed scheme ensures security by specifying rules with a smart contract. Results and discussions show that the proposed scheme is more efficient than the existing schemes

    Enhancing randomness of the ciphertext generated by DNA-based cryptosystem and finite state machine

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    Nowadays, the research in deoxyribonucleic acid (DNA) cryptography seeks to implement data transmission techniques to ensure secure data transmission across the world. As data transmission techniques are not secured due to the presence of hackers and attackers, a DNA-based cryptosystem can be suitable to secure data transmission, where confidential information (plaintext) is encoded in an unreadable form (ciphertext) prior to its transmission. This paper proposes a novel cryptosystem based on DNA cryptography and finite state machines. Here, finite state machines perform substitution operations on the DNA sequence and make the system more secure. Moreover, a DNA character conversion table is proposed in this paper to increase the randomness of the ciphertext. The efficiency of the proposed scheme is tested in terms of the randomness of the ciphertext. The randomness of the ciphertext determines the security of a cryptosystem, and here, randomness tests mentioned in the National Institute of Standards and Technology (NIST) test suite assess the randomness of the ciphertext. The experimental results show that the proposed scheme yields an average P-value of 0.95, which outperforms the existing systems. The proposed scheme guarantees a highly secured cryptosystem as an average avalanche effect of 75.65% is achieved. As a result, the proposed scheme is more secure than the existing DNA-based cryptosystems

    QEST: Quantized and Efficient Scene Text Detector Using Deep Learning

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    Scene text detection is complicated and one of the most challenging tasks due to different environmental restrictions, such as illuminations, lighting conditions, tiny and curved texts, and many more. Most of the works on scene text detection have overlooked the primary goal of increasing model accuracy and efficiency, resulting in heavy-weight models that require more processing resources. A novel lightweight model has been developed in this article to improve the accuracy and efficiency of scene text detection. The proposed model relies on ResNet50 and MobileNetV2 as backbones with quantization used to make the resulting model lightweight. During quantization, the precision has been changed from float32 to float16 and int8 for making the model lightweight. In terms of inference time and Floating-Point Operations Per Second, the proposed method outperforms the state-of-The-Art techniques by around 30-100 times. Here, well-known datasets, i.e., ICDAR2015 and ICDAR2019, have been utilized for training and testing to validate the performance of the proposed model. Finally, the findings and discussion indicate that the proposed model is more efficient than the existing schemes

    A novel cryptosystem based on DNA cryptography, hyperchaotic systems and a randomly generated Moore machine for cyber physical systems

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    Nowadays, a large amount of data is transmitted over the Internet and in Cyber–Physical Systems (CPS). A significant portion of this data is highly confidential. Bank account details, credit card details, One Time Passwords (OTP), financial data and other sensitive information are some examples of highly confidential data. A secure encryption and decryption technique is needed to ensure the secure transmission of data. These techniques use a secret key and guarantee that confidential data is only accessible to authorized individuals. This paper proposes a novel encryption process based on Deoxyribonucleic Acid (DNA) cryptography, a hyperchaotic system and a Moore machine. The hyperchaotic system generates four pseudo-random number sequences used in DNA-based operations. The Moore machine performs substitutions in the DNA sequence that makes the system more secure. The proposed technique can protect a system from various attacks, namely man-in-the-middle attacks, ciphertext-only attacks, known-plaintext attacks, brute force attacks and differential cryptanalysis attacks. The proposed scheme gives an average avalanche effect of 54.75%, which guarantees a high level of robustness. Moreover, experimental results show that the proposed scheme is more secure and efficient than the existing schemes

    Analyzing and classifying MRI images using robust mathematical modeling

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    Medical imaging is an exponentially growing field, which consists of a set of tools and techniques used to extract useful information from medical images. Magnetic Resonance Imaging (MRI) is one of the most popular techniques among image modalities. This paper develops a linear model for classifying MRI images into the tumor and non-tumor categories. The proposed algorithm supports automatic extraction of features from brain MRI images, and focuses on extracting grey matter and white matter, so that the unhealthy MRI images can be isolated from the healthy MRI images. This technique takes advantage of preprocessing strategies and various filters for viable extraction and for classifying the brain MRI images. The samples of MRI images are taken from the BRAINIX and Neuroimaging data sources. The results are validated by applying the mathematical equations on 46 patients categorizing into 24 subjects as healthy and the remaining 22 as unhealthy. The novelty lies in formulating a general equation for both groups, which can be further used as a tool in computer-assisted medical systems for classifying patient

    Blockchain-Based Medical Certificate Generation and Verification for IoT-Based Healthcare Systems

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    Nowadays, medical certificates are very important for many users as they want to avail health benefits like tax purposes, insurance claims, legal procedures, and many more. Generating, issuing, and maintaining medical certificates remain a significant problem; before the invention of the computer, they were available as hard copies. The digitization of medical certificates and documents leads to potential security issues, such as forging of certificates risks the privacy of healthcare documents. Moreover, individuals still need to be physically present and wait at the issuing healthcare centers to get the certificates. Currently, the infrastructure of any healthcare industry connects the Internet of Things (IoT) devices and application software that communicates with the information technology systems. Blockchain technology with IoT can significantly affect the healthcare industry by improving efficiency, security, transparency, and can provide more business opportunities. Therefore, a privacy-preserving technique has been proposed in this article for IoT-based healthcare systems using blockchain technology. The proposed architecture provides an interface between the users and healthcare centers to generate and maintain healthcare documents. Furthermore, the proposed scheme ensures security by specifying rules with a smart contract. Results and discussion show that the proposed scheme is more efficient than the existing schemes