Global Transitions Proceedings最新文献

Major backbone of today's competitive and upcoming market is definitely becoming Cloud computing & hence corporate utilize capabilities of cloud computing services. To improve security initiatives by cloud computing service or CRPs, novel types of tools and protocols finds themselves always in demand. In order to build comprehensive risk assessment methodology, extensive literature review was conducted to identify risk factors that may affect cloud computing adoption. In this context various risk factors were identified. After feature selection and identification of risk factors, utilized to select most effective features using linear regression algorithms. Then AI-ML techniques like Decision Tree (DTC), Randomizable Filter Classifier, k-star with RMSE method is used to analyse threats within CC environment. Experimental outcomes depicted that division of dataset to (95%-5%) provided best result out of every remaining partitioning and moreover put forth that DTC algorithm provided best outcomes out of entire data set used in experimental setups.

Consumers prefer to order from apps/websites at their ease. The existing solutions have affected the local vendors by providing this ease to the customers. The authors have made a working O2O, B2B logistics system and a deployed web platform ready to deliver products at consumer's ease and provide an online presence for the vendors. OOLY is the proposed model that has remodelled the way people order, transfer items, and move by providing an online presence to the vendors. This review has proved that the Customers get local support from local vendors since the customer knows the shop/vendor. Findings have shown that the Local vendors with an online presence provide the trust for customers and transfer items at ease. The products get replaced at ease without the long existent processes. Authors have succeeded by providing an online presence for vendors and have improved customer satisfaction.

Accurate characterization of the molecular nature of a tumour is important for its effective treatment. Therefore, the classification of tumours is an important research problem. The application of data science and machine learning techniques to the gene-expression data has enabled computational researchers to separate the gene-expression samples into different classes based on the difference in gene-expression patterns. This has also facilitated the discovery of new classes and new disease biomarkers. However, gene-expression data is very high-dimensional and noisy. The number of features is high in comparison to the number of samples. The classes in the data are often imbalanced. Out of thousands of genes, only a few are relevant to the disease. The machine learning approaches for the classification of gene-expression samples need to address all these issues to obtain reliable performance. This paper proposed a method using simulated annealing and partial least squares regression for gene selection from six open-source microarray cancer gene-expression datasets. Selected subset of genes was used to fit support-vector machines, random-forest, voting-classifiers, and multilayer-perceptron classifiers. A comparison with existing methods shows the superior performance of the proposed method.

The opportunities to exchange information in the current web era have risen. The increased popularity of the media has posed real challenges to security-related issues. Steganography is a technology for the secure exchange of information. A video, audio, or image intended to raise no suspicion may be the carrier. After concealing the secret information, steganography techniques produce an identical cover image. This will prevent outside observers from noticing the existence of secret information. In the proposed work, alpha is a scaling parameter. Cover and payload images of different types and dimensions, live images from a webcam, and predefined images of other formats have been normalized and preprocessed. A Haar Discrete Wavelet Transformation (DWT) is applied to both the cover and payload images. To generate a stego image, the payload image is encrypted and fused with the cover image. The result parameters such as PSNR, MSE, and Entropy are measured.

The development in the digital world has influenced training programs for the employees; this indicates that skills and information provided by the traditional training programs need up-gradation in the digital environment. Tools of digital technology act handy in the training programs of the employees. These digital technologies such as simulation in training promote creativity and high order thinking among the employees. The main focus of the present study is to indicate the level of effectiveness of digital simulation methods of trainingin the organization. The study was conducted on two groups of employees from information technology companies based in Bangalore. The first group responded with regards to the training method and the second group of trainees included the digital simulation method of training. The results indicated that the digital simulation method of training was more effective than the traditional method of training. This suggests that the application of digital simulation training tools assists in training programs to train employees in the digital economy.

The world saw a health crisis with the onset of the COVID-19 virus outbreak. The mask has been identified as the most efficient way to prevent the spread of virus [1]. This has driven the necessity for a face mask recogniser that not only detects the presence of a mask but also gives the accuracy to which a person is wearing the face mask. Also, the face mask should be recognised in all angles as well. The goal of this study is to create a new and improved real time face mask recogniser using image processing and computer vision approach. A Kaggle dataset which consisted of images with and without masks was used. For the purpose of this study a pre-trained convolutional neural network Mobile Net V2 was used. The performance of the given model was assessed. The model presented in this paper can detect the face mask with 98% precision. This Face mask recogniser can efficiently detect the face mask in side wise direction which makes it more useful. A comparison of the performance metrics of the existing algorithms is also presented. Now with the spread of the infectious variant OMICRON, it is necessary to implement such a robust face mask recogniser which can help control the spread.

Quantum-dot cellular automata (QCA) is a raising nanotechnology with the potential for quicker speed, smaller size and low power consumption than CMOS based technology. This paper proposes the design of functional blocks for Arithmetic Logic Unit (ALU) and for majority gate (MV) using certain unique QCA characteristics. Previous works were based on the conventional design and have different approaches when compared with QCA method. This paper helps to resolve the conventional method by converting all Boolean logics into a NAND and NOR based majority voter by fixing one of the inputs. The proposed work presents a significant amount of reduction in the cell count, area of the cell, and clock delay phase, compared to previous work in QCA. The experimental result shows a reduction in number of cells is by 35.40%, Area of cells (um2) by 15.88% and clock delay phase by 48.21%. Simulations indicate an appealing performance in terms of area, delay, and complexity. Simulation results are validated using the quantum-dot cellular automata (QCA) Designer 2.0.3 tool.

This article is mainly focused to accurately detecting any abnormality of heart if present using echocardiographic image of the patient. Heart abnormalities are now a days very common not only in India but all over the globe irrespective of age and gender. The detection of abnormality is achieved by using Artificial neural network (ANN) Classifier. However, processing of the image is achieved through preprocessing, segmentation, feature extraction and then achieving classification. Processing of image for removal of noise and enhancement is carried out as Preprocessing of image followed by segmentation. The most significant processing task is segmentation which is discussed in detail and preferable algorithm which overcomes the drawbacks and limitations of previous algorithms is proposed. This algorithm is a solution to all problems faced in previous algorithms. carried out using different techniques, three different segmentation techniques are discussed where algorithm proposed Reaction Diffusion Level Set Segmentation (RDLSS) is better than other three methods also overcome the problems faced in previous algorithms, then feature extraction is done to extract energy features where the novelty of the research is use of symlet, Debauches and Bio orthogonal filters for feature extraction and these features are used to classify the images as normal or abnormal using ANN classifier. The ANN classifier is effective and efficient resulting in accuracies of greater than 98%. The results are also clinically validated by doctors.

Compressive sensing (CS) is a new branch of research with applications in signal processing, medical imaging, seismology, communications, and a variety of other fields. It assures successful data compression and faithful reconstruction by considering a smaller number of linear measurements compared to its dimensions. In this paper, we have shown CS paradigm for image compression and reconstruction. We have considered the Basis Pursuit (BP), Lp – Reweighted (Least Squares Method), Orthogonal Matching Pursuit (OMP) and Fusion of OMP & BP algorithms to obtain the compressive measurements and faithful reconstruction. The results are analyzed by varying sparsity level and Compression Ratio (CR) and then calculating the Peak Signal to Noise Ratio (PSNR) value. The obtained results show that OMP performs better for standard test images & satellite images and Fusion of OMP & BP performs better for biomedical images.

Reversible logic is also called information lossless logic, since the information embedded in the circuits can be recovered, if lost. Research carried out by Landauer and Bennett proved that the energy dissipation would not occur if computation is made reversible. With this aim a number of reversible gates were designed and invented. As examples like- the Fredkin gate, the Toffoli gate, the Peres gate, and the Feynman gate. Reversible logic has extensive applications and is considered as one of the futuristic technologies. But the logic circuit designing is based on logic gates, which are non-reversible. This paper presents design of logic gates using reversible gates. These logic gates help in future implementation of higher end circuits. In this paper an attempt is made to design logic gates using reversible gates and some of the higher end circuits are also designed such as Binary-to-Grey, grey-to-Binary, Adder, Subtractor etc.