Fukunaga-Koontz transform for small sample size problems

In this paper, we propose the Fukunaga-Koontz Transform (FKT) as applied to small-sample size (SSS) problems and formulate a feature scatter matrix based equivalent of the FKT. We establish the classical linear discriminant analysis (LDA) analogy of the FKT and apply it to a SSS situation. We demonstrate the significant computational savings and robustness associated with our approach using a multi-class face detection proble

Bioreactor Leaching of Uranium from a Low Grade Indian Ore

Bio-leaching studies were carried out in a 2L bioreactor- BIOSTAT-B® equipped with a PLC based controller at 20-40% (w/v) pulp density using enriched culture of A.ferrooxi-dans for Turamdih uranium ore (Jharkhand, India). With the enriched culture of Aferrooxidans adapted on Fe(II) at pH 2.0, 35°C and 20% (w/v) pulp density, a 98.3% uranium recovery was recorded in 14 days. The leaching of uranium in the bioreactor improved the dissolution rate by reduc-ing the time from 40 days in shake flask as per our ear-lier studies to 14 days. While investigating the import- ance of biogenic Fe (III) in the bio-leaching process a maximum recovery of 84.7% (1308 was observed at pH 2.0 and 20% (w/v) pulp density in 10h as compared to the uranium leaching of 38.3% in the control experiments. On raising the pulp density to 30%, uranium bio-recovery increased to 87.6% in 10/i at pH 2.0 with <76,um size material. This showed a distinct advantage because of better mixing of slurry in the bioreactor with auto-controlled conditions that improved the kinetics

Effect of Passive Ultrasonic Irrigation on the Cleanliness of Dentinal Tubules in Endodontic Retreatment With and Without Solvent: Sem Study

This Invitro study was done to evaluate the effect of ultrasonic irrigation on the cleanliness of dentinal tubules during endodontic retreatment with and without the use of solvent. Thirty six roots (mesiobuccal or distobuccal) of maxillary molars having curvature between 15º to 25º were obturated with gutta-percha and AH Plus sealer by lateral condensation method. After 2weeks of storage at 100% humidity for 2 weeks the samples were divided into four groups of 9 samples each. In group I retreatment was performed with protaper universal retreatment files with the aid of Endosolv-R solvent. In group II retreatment was performed with protaper universal retreatment without using any solvent. Time was recorded for endodontic retreatment. In group III retreatment was performed with protaper universal retreatment files with the aid of Endosolv-R solvent followed by passive ultrasonic irrigation with 3%NaoCl. In group 1V retreatment was performed with protaper universal retreatment without using any solvent followed by passive ultrasonic irrigation with 3%NaoCl. All 9 samples from each groups were split longitudinally using safe sided flexible diamond discs and the split root halves washed with 0.5ml of saline for removing any cutting debris. The coronal middle and apical thirds of root halves were examined using SEM at a standard magnification of 2000X. The total number of dentinal tubules and the number of dentinal tubules either completely or partially filled with materials were evaluated for the coronal,middle and apical third. CONCLUSION: Within the limitations of this study it can be inferred that 1. The use of Endosolv-R solvent with protaper NiTi rotary retreatment files during non surgical endodontic retreatment on curved root canals of mesiobuccal and distobuccal roots of maxillary 1st and 2nd molars revealed more gutta-percha /AH Plus sealer remnants on the root canal walls. 2. The use of passive ultrasonic irrigation after retreatment further enhances the removal of remaining debris inside the root canal walls and thereby reveals more number of open dentinal tubules in the SEM study, compared to other experimental groups. 3. In all the experimental groups, more number of open dentinal tubules were observed in the middle third, reduced number in the coronal third and least in apical third. 4. The use of solvent during retreatment is more time consuming in removing the gutta-percha/AH Plus sealer. 5. It was impossible to remove all the traces of gutta-percha/sealer remnants from the root canal walls of curved roots, regardless of the instrumentation and irrigation techniques used

A comparative study on the performance of neural networks in visual guidance and feedback applications

Vision-based systems increase the flexibility of industrial automation applications by providing non-touching sensory information for processing and feedback. Artificial neural networks (ANNs) help such conformities through prediction in overcoming nonlinear computational spaces. They transform multiple possibilities of outcomes or regions of uncertainty posed by the system components towards solution spaces. Trained networks impart a certain level of intelligence to robotic systems. This paper discusses two applications of machine vision. The 3 degrees of freedom (DOF) robotic assembly provides an accurate cutting of soft materials with visual guidance using pixel elimination. The 6-DOF robot combines visual guidance from a supervisory camera and visual feedback from an attached camera. Using a switching approach in the control strategy, pick and place applications are carried out. With the inclusion of ANN to make the strategies intelligent, both the systems performed better with regard to computational time and convergence. The networks make use of the extracted image features from the scene for different applications. Simulation and experimental results validate the proposed schemes and show the effectiveness of ANN in machine vision applications

Study on the Recent Cyber Security-Attacks and the Economic Loss Due to the Growing of Cyber-Attacks

Economic damages caused by cyberattacks have been on the rise recently, on a global scale. However, there has not been enough research on the economic damage caused by cyberattacks to other industries; most studies have concentrated on the attacked enterprises. As a means of better damage prediction and other national measures, this study examined the economic damages inflicted on Japan by cyberattacks by employing the production function and the input-output model. We begin by outlining a production function–based approach to estimating the yearly direct harm by industry. Working hours lost due to cyber events are the major input dataset. Second, we used the input-output model to create a model that could estimate the national spillover damage. Thirdly, we explained how to estimate direct and spillover harm in all Japanese industries, even if the data on cyber damage was confined to interview data from the JNSA and the IPA. Therefore, we think our approach of estimating is workable and efficient on a nationwide scale

An Empirical Paradigm on Cybersecurity Vulnerability Mitigation Framework

Current cybersecurity vulnerability assessment tools were developed in accordance with guidelines established by entities like the National Institute of Standards and Technology (NIST) and the United States Department of Energy. When assessing their facility's cybersecurity maturity, owners and operators of critical infrastructure frequently use frameworks like the NIST Cybersecurity Framework (CSF) and the cybersecurity capability maturity model (C2M2). These frameworks are great at finding vulnerabilities and doing qualitative cybersecurity analysis, but they don't help you get to the level of cybersecurity maturity you want by letting you prioritise how you fix those flaws. Cyber dangers pose a significant risk to businesses and are becoming more pervasive in our everyday lives. In this way, businesses may devise a strategy and set of guidelines by simulating a breach attack. But these strategies are based on experts' tacit knowledge. In response to this problem, the authors of this study suggest an automated and formal process for creating prioritised action plans to enhance environmental transparency. An experiment proving the validity of the proposed method was conducted, yielding consistent and applicable results to the tested scenario. Through testing against a real-world cyberattack that targeted industrial control systems at a critical infrastructure facility, this article presents a thorough architecture of CyFEr and demonstrates its application to CSF

Applications of Deep Learning Approaches to Detect Advanced Cyber Attacks

The number and sophistication of cyber attacks have grown, making it tougher to detect and prevent them using traditional security technologies. Improving cyber threat identification and response has been greatly enhanced by deep learning, a subset of machine learning. Learn how to spot advanced cyberattacks with the help of Deep Learning algorithms in this article. The proposed approach collects, categorises, and arranges network traffic data using convolutional neural networks (CNNs) and intermittent neural networks (RNNs). Combining the RNN with the CNN allows us to capture temporal dependencies and derive spatial properties from the network data. In order to find the most important qualities for classification, the proposed method also incorporates a feature selection stage. To demonstrate that the proposed system outperforms signature-based and example AI systems in terms of exactness, accuracy, review, and F1-score, we conduct an exhibition survey using various datasets. An effective tool for improving cyber defences, the proposed method can detect zero-day and previously unknown attacks

Analysis on Security Vulnerabilities of the Modern Internet of Things (IOT) Systems

The IoT, or Internet of Things, has quickly grown in popularity as a means to collect data in real-time from any and all linked devices. These networked physical objects can exchange data with one another via their respective sensor technologies and have their own unique identifiers. Insightful data analytics applied to the obtained information also presents a substantial possibility for many organisations. Embedded devices, authentication, and trust management are all areas where the Internet of Things has shown a significant security hole. This study delves into the problems with the Internet of Things (IoT), covering topics such as its privacy and security, its vulnerability, its analytics at the moment, the impending ownership threat, trust management, IoT models, its roadmap, and its security issues. It then offers solutions to these problems

The Empirical Analysis on Proposed Ids Models based on Deep Learning Techniques for Privacy Preserving Cyber Security

In AI, the deep learning (DL) method of machine learning (ML) places an emphasis on large-scale, scalable models that can learn distributed representations from their input data. The scope and effectiveness of these techniques are demonstrated in this thesis through a number of case studies pertaining to cyber security. By the end of each study, the neural network models had been fine-tuned and expanded to provide better results. The key arguments presented and discussed in this thesis are as follows: 1) Creating an all-inclusive database for domain name detection using domain generation algorithms (DGAs) and a new architecture to improve DGA domain name detection overall performance. 2) Constructing a hybrid intrusion detection warning system that incorporates deep neural networks (DNNs) to examine host-level and network-level behaviours within an Ethernet LAN. thirdly, analysing data from social media platforms, email, and URLs to create a single DL-based framework for detecting spam and phishing. 4) ScaleMalNet, a novel hybrid framework proposal, is part four. This is a two-step process: first, we use static and dynamic analysis to determine if the executable file is malicious or not. Then, we categorise the malicious executable file into the appropriate malware family. Malware and ransomware analysis for Android is accomplished using a hybrid DL framework that is comparable to this one