Miracle-dz/UniFAR-FTUs huggingface dataset:
Multiple Network and Double System Orthogonal Low-Ranking Training Melanoma Image Grouping

A significant method to identify and track early cancer of the breast in clinical practise is histopathological image analysis. The diagnosis of breast cancer is still facing issues with an open in healthcare sector, however, with a limited quality. We are creating a classification system based on histological picture pictures, integrating deep learning with mechanical methodologies of learning, in sequence to enhance the prediction of early recognized breast cancer and to minimize the work pressure of physicians. In particular, through pre-trained Deep Convection Neural Networks, we build a multi-network extraction model, create an efficient method of reducing features and train an ecosystem supporting vector machine (E-SVM). Next, we use scale transformation and colour improvement approaches to prepare histological pictures. Second, four pre-trained DCNNs extract the multi-network functionality. Thirdly, the Dual Network Orthogonal Low-Rank Learning (DOLL) role selection approach is further introduced to increase efficiency and to reduce unnecessary efficiencies. An E-SVM is at last instructed by melded usefulness and casting a voting procedure for characterization, what isolates the pictures into four gatherings considerate, in situ carcinomas, obtrusive carcinomas, and ordinary. The suggested procedure is tested by us for the public ICIAR 2018 Challenges Data Set on histology photographs of breast cancer. Our approach can offer very promising productivity and underperform province approaches through analytical outcomes.
AI Based Intrusion Detection System using Multi Sensors

In an intrusion detection system, the existing system uses single sensor that causes false alarms due to lack of accuracy and also leads to misuse of fuels that are stored in nuclear power plants/ reactors. The fuels that are used in the reactors are too expensive and high in price. The fuels use in nuclear power plants/ reactors must be protected under such circumstances that may reduce the detection and inactive secure system of false alarms under given area. In this paper multiple sensors are used to interface with Arduino to overcome false detection using KNN algorithm for the classification of nearest neighboring values that compares with the predefined and predicted values in the given database. Hence the KNN uses to forecast a new point in sample classification, a database where the data points are divided into many groups. This is the point where KNN is located in the Scikit-learning algorithm list. Therefore, the K Nearest Neighbors method stores all available cases and starting lineup cases on the basis of mutual information.
A Supply Chain Framework for Identified Internet Services Based on Blockchain

The Named Data Networking is designed to ensure that each Data entity named allows its producer to digitally sign it. Therefore, the NDN project proposed to delivery of poisoned material an important management paradigm of Testing ground NDN for the actual data validation. This model, however, raises two problems in reality to search for false content: (1) a single point of failure is easily triggered by a centralised architecture, in particular where a root-key fails, because of its lack of dependency on keys across sites, and (2) an unnecessary certificate chain overhead for signing verification. This research suggests a key organization framework built on a blockchain in NDN, which addresses lack of mutual confidence among sites with no trust-based anchors. In particular both site nodes shape a licence blockchain to save public key hatches to guarantee validity, and the proxy gateway takes part to check that contacts among the router then the blockchain are overly regular. In accumulation, NDN is revamped to hold, validate and revoke a publicly important information object and scheme. The findings of our review and appraisal suggest that fewer verification numbers and greater verification reliability can be supported in the proposed arrangement.
Study of Detection Analysis of Cardiac Amyloidosis Heart Disease Using Image Segmentation Technique

Cardiac amyloidosis is a collection of mayhem, which increases the less important disposition of abnormal proteins in the heart. It may happen either in segregation or like a fraction of systemic disease and can be hereditary or attained. The major forms of amyloid proteins, namely Amyloid Light Chain also Amyloid Transthyretin (ATTR), can penetrate the heart. With the enhanced utilization of advanced image techniques and procedures, the identification and diagnosis of cardiac amyloidosis, especially ATTR, has become very easier. In general, congestive heart failure disease has been diagnosed through an electrocardiogram. In this survey, the ultrasound heart image is gathered as an input image to diagnose whether the heart’s functioning is normal or abnormal. Based on input ultrasound heart image or picture, the ultrasound high-frequency sound waves afford the heart’s image and valves, which allows us to observe the heart-pumping action. The image segmentation technique was well performed in segmenting the specified heart image into high-intensity range, and low-intensity level deliberated to enhance patients’ lives with cardiac amyloidosis. The disorders or variations or any abnormalities happening in the heart can be done via a segmentation approach through that intensity level.
Service Oriented Virtual Machine for Maximising Quality of Service in Wireless Networks

A traditional big data network is a wireless multimedia network, as multiple video/audio streaming account for 70 percent of mobile traffic and the fifth generation wireless networks are projected to rise 500 times. However, it does raise various new and transparent challenges for multimedia big data communication over 5G wireless systems because multimedia big data services provide timely, throughput broadcasts through time-sensitive communication networks with restricted wireless resources. In order to address the above issues, we suggest in this paper information-centric virtualization architectures for a statistical latency consistency of content delivery across 5G wireless large media platforms for technology. In particular, three successful nominee strategies to promise the mathematical time limit for interactive large-data communications are implemented in our proposed scheme: information-centered network which extracts an optimum in-network storage location for multimedia big data; a virtualization of the network feature which transforms PHY architecture into many virtualized channels. In our architectures we build the 3 primary computer-generated machine collection besides power distribution plans to collectively simplify the application of NFV and SDN techniques in the ICN architecture: to improve performance for single users, to integrate effective combining capability with equity allocation for all users besides under non-cooperative betting Via models and computational assesses, we prove that our suggested designs and applications greatly perform better other existing systems to enable QoS provisioning with statistical delays over 5G immersive Large Data Wireless communication.
Gini Index Test Metric Based Collaborative Sensing of Spectrum for Cognitive Networks

The key confront for a cognitive radio network is to identify the presence of primary users consistently so as to curtail the intrusion to license approved communications. Consequently, sensing of spectrum is a main significant requisite of a cognitive network. Nevertheless, because of the ambiguity in the channel, local interpretations don’t provide a trustworthy solution and therefore collaboration is needed between the users. Collaborative spectrum sensing is a competent scheme to enhance the spectrum utility in wireless networks, which utilizes cooperation between multiple nodes to prevail over the inadequacy of single-node to improve the performance of detection. In this paper, a detailed investigation, simulation and comparative analysis of collaborative scheme for sensing the spectrum of cognitive networks based on Gini Index test metrics is done and the results are compared with various other detecting schemes. The simulation is accomplished by means of Matlab R2014 software.
Medical Quora Tagging using MATAR and LDA Algorithm

The success of clustering or classification methods the detection of relevant textual formats is incredibly meaningful. The high dimensionality and irrelevance of textual materials was subjected to text records. Existing methods lack integration and are particularly vulnerable to original value. Metaheuristic algorithms are also applied to solve the challenges of standard classification algorithms. In this paper, an enhanced Latent Dirichlet Assignment clustering method & Inter Modeling for Tag Suggestion rating system is documented to boost correlation - based & identification efficiency to suggest labels with material modern web labels that promotes the exchange of medical information using unmonitored data through question-answering. For accurate tagging, Methods like POS marking, Hopping, Whistles& Stopping words are being used for speech recognition. The efficiency of the evolved architectures is compared to the standard methods, by using specificity of the recommendation, defining features, sensitivity, plain word and speed. The findings reveal that the classification and grouping scheme of the proposed structure succeeds traditional textual record approaches.
A Novel Compression Framework for Electro Cardiac Signals

Electrocardiography (ECG) is a method of measuring cardiovascular electrical movement over a span of time using skin-situated electrodes. In patient surveillance, large volumes of ECG information must be preserved. So as long as we essential to track a patient heart working condition, we should have to save more facts. However, it is very expensive to storage, transmit in addition distribute bandwidth. A powerful compression strategy has been practical to solve this problem so that all the excellent scientifically important functionality can be conserved. We used the MIT-BIH ECG information base for ECG research.
Segmentation Based Biomedical Image Retrieval with Low-Level Feature Extraction

Content based image retrieval (CBIR) is an imperative and testing errand in numerous fields, for example, military, common, restorative and even in web applications. Here, we implemented medical image retrieval focused on novel material with decimated bi-orthogonal spline wavelet filter banks. Proposed algorithm is an extension for the existing low-level feature extraction method done by using spline wavelet filters, also utilized iterative partitioning (IP) for extracting the similar patterns of Pictures of query & database. In terms of consistency & memory, simulation results indicate that the approach suggested was better than the current scheme.
Polyol-based biodegradable polyesters: a short review

Abstract Catalyst-free thermal polyesterification has recently emerged as a potential strategy for designing biodegradable thermoset polymers, particularly polyol-based polyesters for biomedical applications. These thermoset polyesters are synthesized through polycondensation of polyol and polyacid without the presence of catalyst or solvents. The mechanical properties, degradation rates, crystallinity, hydrophilicity, and biocompatibility can be controlled by adjusting the monomer feed ratios and curing conditions. These polyesters often degrade via surface erosion that allows the polymers to maintain structural integrity throughout hydrolysis. Additionally, polyol-based polyesters demonstrated good biocompatibility as non-toxic catalysts and/or solvents involved in the reaction, and the monomers used are endogenous to human metabolism which can be resorbed and metabolized in various physiological pathways. This review summarizes the polyol-based biodegradable polyesters that were synthesized by catalyst-free polyesterification.
Fingerprint Presentation Attack Detection Approaches in Open-Set and Closed-Set Scenario

In the biometric system, fingerprints are widely used to recognise an individual’s identity and are vulnerable to presentation attacks such as spoofing. Spoof fingerprints are fake fingerprints created using artificial materials like play-do, silicone, etc., which fool the biometric identification system. Spoof handling is an urgent need nowadays, as these attacks’ success rate is more than 70%. Much work has been done on fingerprint spoof detection, but mostly involved closed-set problems, i.e. spoof materials used are the same for training and testing set, but they face poor generalisation problem. Hence to overcome closed-set problems, Open-set solution came into existence in which some novel spoof materials were introduced in the testing set, which was not seen during training. This paper will give a brief review of various closed-set and open-set solution techniques along with the tabular view of their Average Classification Error (ACE) scores.
Understanding and Analysing Resource Utilization, Costing Strategies and Pricing Models in Cloud Computing

This paper is primarily focused on understanding the basics of cloud computing economics – specific pricing models and general cost structure and compares between basic cost structures. It also examines 15 pricing models dependent on various bases and attempts to consolidate the different pricing methods used to guarantee maximum profitability to both the service provider and consumer. To avail the services of cloud computing from the cloud service provider, the organization has to incur individual costs. There are three types of costs: fixed, variable, and semi-variable costs. Each type is computed and analyzed. There are more than 30 pricing models though the pay-as-you-go model, subscription model, and pay-for-resources models are in demand. To consider and discuss the basics of cloud computing, emphasizing its economics and pricing models considering various aspects and costing components, using and estimating plans from various cloud Registering suppliers. The paper also considers identifying gaps, if any, present in the current pricing models that are highly preferred with other pricing models that are designed but not implemented due to practical conditions and fear of higher percentage of risk and error and to compare the basic prices provided by the cloud service providers.
Synchronized Multi-Rate Test Pattern Generation Using Hybrid Twisted Ring Counter and LFSR for BIST Application

This work presents a new approach for built-in test pattern generation (BIST) based on the variable rate clock-driven linear feedback shift register (LFSR) and twisted-ring counters (TRCs). The proposed technique operates on different operating clocks for generating deterministic test pattern sequence for the circuit under test (CUT). The test vectors generated by LFSR are transformed and reproduced with TRC results. The TRC design constitutes of only flip-flops, which be used as a potential alternative to improved seed storage and complex multi polynomial configurations in LFSR. Moreover, the control logic is simplified with synchronized multi-rate clock generation and can be easily extended for any test cubes among multiple CUTs. The randomness characteristics of LFSR are retained with improved memory efficiency and considerable complexity reduction. Experimental results validate the proposed test pattern generator (TPG) over existing well-known LFSRs models, and its randomness is proved with ISCAS benchmark circuits.
Augmentation of Mapping and Autonomous Navigation for Hexapod Robots by using a Visual Inertial System

In our previous work, we focused on the development of Razbot, a hexapod platform, and achieving its autonomy using the Robot Operating System (ROS). Therein, we also explained the difficulties faced with Razbot while trying to execute complex operations autonomously. Hence, in this work we address the problem and make an attempt at improving the odometry measurements of Razbot by introducing a Visual Inertial System (VIS) that includes a pair of synchronized monocular camera and inertial measurement unit which makes it capable of mapping the environment and autonomously navigating in it with increased effectiveness. To introduce the VIS, we made minor modifications in the components used and the control scheme of Razbot. In this work, we take help of the Extended Kalman Filter (EKF) for the fusion of odometry sources. Further, we also analyse the accuracy of the fused odometry by comparing the same and evaluating the errors or drifts in odometry before and after introducing the VIS in Razbot. Based on these results, we make a conclusion that the odometry source from VIS can significantly lower errors and drifts in fused odometry data, thus improving autonomous navigation and mapping of the hexapod robot. The implementation of this system will allow Razbot to be used in even more varied applications than those specified earlier.
Usability of Blockchain Technology in Higher Education: A systematic review identifying the current issues in the education system

Blockchain facilitates the creation of a shared document of a digital transaction in a distributed way where no third-party regulates any data or related activity. Earlier blockchain was used to transmit money, but today it has a huge scope of utilization in various domains like banking, health care, the Internet of Things (IoT), and many more. Inside the educational domain, blockchain offers many circumstances for disperse administration of records in an interoperable way in higher educational institutions. The primary purpose of the study is to address the current issues relevant to educational institutions and to identify the appropriate blockchain characteristics that can solve these issues. To recognize and extract significant knowledge from the selected research studies, a systematic literature review approach has been followed by us. Our research study represents the current problems in three forms, i.e., financial, digital, and physical. The consequences of the study show that the main problems faced by education institutions are the risk of manipulation, the tough of authenticating, and difficulty in interchanging records among institutions. This research analyzes the blockchain characteristics such as fragmentation, traceability, and consensus system, focusing on a few problems relevant in this domain.
Comparative Analysis of Convolutional Neural Network Architectures for Real Time COVID-19 Facial Mask Detection

The late 2019 outbreak of Coronavirus Disease (COVID-19) had an indelible imprint on the humanity. The world is recovering from the outbreak but there is danger of a second wave of the outbreak. To get rid of the outbreak it is necessary to prevent the viral transmission and it is need of the hour to maintain social distancing and wear masks in public areas. The governments are providing strict guidelines to wear masks in public places. It is not manually feasible to check if people are wearing masks or not. In this paper, process of detecting face masks in public places is automated using Convolutional Neural Networks by performing comparative analysis on Sequential bi-layered CNN, VGG-16 CNN and MobileNetV2 CNN architectures. Among these three architectures MobileNetV2 outperformed with a performance accuracy of 99.2%. The efficient Deep Learning architecture of detecting face masks can be achieved with the help of IoT (Internet of Things) devices and cameras, of those who are not following guidelines in public places. Such a system is very useful in post outbreak period and can be installed in public places such as Railway Stations, Airports, Parks, Schools, colleges, offices etc. to track and ensure wearing of masks by people. The contribution of this paper is not to reel-off the finding from the original paper on Face Mask detection with various architectures rather to provide results on the efficiency of using the MobileNetV2 architecture in comparison with Sequential CNN and VGG-16 architectures for crowd analysis mask detection.
Vision-based Position estimation and Indoor scene recognition algorithm for Quadrotor Navigation

In this paper, an effective and simple Grid based vanishing point detection position estimation algorithm and Enhanced GIST descriptors based indoor scene recognition algorithm for navigation of MAV in indoor corridor environment is described. Two different classifiers, k-nearest neighbour classifier and support vector machine is employed for the categorization of indoor scenes into corridor, staircase or room. Indoor scene classification was performed on Dartaset-1. In the training phase of the indoor scene recognition algorithm, GIST, HODMG and Enhanced-GIST feature vectors are extracted for all the indoor training images in the Dataset-1 and indoor scene classifiers are trained for the extracted image feature vectors and assigned image labels of the indoor scenes (corridor-1, staircase-2 and room-3). In the testing phase of the indoor scene recognition algorithm, for each unknown test image frame GIST, HODMG and Enhanced-GIST feature vectors are extracted and the indoor scene classification is performed using a trained scene recognition model. The proposed indoor scene recognition algorithm using SVM with Enhanced GIST descriptors produced high recognition rates of 99.33% compared to the KNN classifiers. After recognizing the indoor scene as corridor, the MAV has to estimate its position based on the detection of vanishing point in the indoor corridor image frames. Experimental results show that the proposed method is suitable for real time operations.
Simulations of NBI fast ion loss in the presence of toroidal field ripple on EAST

NBI fast ion losses in the presence of the toroidal field ripple on EAST have been investigated by using the orbit code GYCAVA and the NBI code TGCO. The ripple effect was included in the upgraded version of the GYCAVA code. It is found that loss regions of NBI fast ions are mainly on the low field side near the edge in the presence of ripple. For co-current NBIs, the synergy effect of ripple and Coulomb collision on fast ion losses is dominant, and fast trapped ions located on the low field side are easily lost. The ripple well loss and the ripple stochastic loss of fast ions have been identified from the heat loads of co-current NBI fast ions. The ripple stochastic loss and the collision-induced loss are much larger than the ripple well loss. Heat loads of lost fast ions are mainly localized on the right side of the radio frequency wave antennas from the inside view toward the first wall. For counter-current NBIs, the first orbit loss due to the magnetic drift is the dominant loss channel. In addition, fast ion loss fraction with ripple and collision for each NBI linearly increases with the effective charge number, which is related to the pitch angle scattering effect.
Impregnation of Microwave treated exfoliated MoS2 nanosheets into PMMA matrix by solution casting for reinforcing applications

In the present work, poly methyl methacrylate (PMMA)-MoS2 nanocomposite films were prepared by impregnation of exfoliated MoS2 nanosheets into PMMA matrix by solution casting method. Synthesis of exfoliated MoS2 nanosheets through microwave irradiation techniques was found to be simple, less time consuming, eco-friendly and cost effective. We have identified the optimization conditions by varying the parameters like microwave heating time, power and concentration of bulk MoS2. The structural and optical properties of the exfoliated MoS2 nanosheets were investigated. These optimized exfoliated MoS2 nanosheets were introduced into the polymer matrix which was then subjected to different characterization techniques. The effect of exfoliated MoS2 nanosheets on the mechanical properties of the polymer-MoS2 composite was evaluated in detail.