Global Transitions Proceedings最新文献

In today's world, one of the reasons in rise of mortality among people is cancer. A cancerous disease is bound to occur due to the ungovernable growth of certain cells that can scatter to other parts of the body. The different types of cancerous diseases are lung cancer, breast cancer, brain cancer, skin cancer. One among them which is of major concern is the brain cancer. With the emergence of AI-ML techniques, detection of cancerous tumour can be automated. One of the efficient methods for the detection of brain tumour is convolutional neural network. Visual information from various viewpoints is frequently used by humans in their decision-making process. For the recognition of the brain tumour a single image showing an object is insufficient. Multi-view classification aims to improve classification accuracy by combining data from several perspectives into a uniform comprehensive representation for downstream tasks. To aim that it presents a trustworthy multi-view classification, a classification approach that dynamically integrates diverse perspectives at an evidence level, resulting in a new paradigm for multi-view learning. By incorporating data from each view, the method promotes both classification reliability and resilience by combining several viewpoints. The process of segmenting images involves separating areas within a picture into distinct classes in order to identify them and classify them. In CNN there are different architectures like E-Net, T-Net, W-Net to determine the ROI and perform the image segmentation. In order to automate detection of the brain tumour, MRI image segmentation plays vital role. In this paper, a survey on the various image segmentation approaches and its comparison is presented. The main focus here is on strategies that can be improved and optimized over those that are already in use.

Macro-based malware attacks are on the rise in recent cyber-attacks using malicious code written in visual basic code which can be used to target computers to achieve various exploitations. Macro malware can be obfuscated using various tools and easily evade antivirus software. To detect this macro malware, several methods of machine learning techniques have been proposed with an inadequate dataset for both benign and malicious macro codes which are not reproducible and evaluated on unbalanced datasets. In this paper, use of word embedding technique such as Word2Vec embedding is used for code analysis is proposed to analyze and process macro code written in visual basic language to understand and detect the attack vector before opening the documents. The proposed word embedding technique, called Obfuscated-Word2vec is proposed to detect obfuscated keywords, Obfuscated function names from the macro code and classify them as obfuscated or benign function calls which are later used as feature vectors to train models to extract the most relevant features from macro code and even to help the classifiers to detect more accurately as a downloader, dropper malware, shellcode, PowerShell exploits, etc. Experimental results show that proposed method is reproducible and could detect completely new macro malware by analyzing the macro code by the help of Random forest classifier with 82.65 percent accuracy.

In this article, a rhombus shaped fractal microstrip frequency reconfiguration patch antenna is presented. The proposed antenna depending upon the capacitance value of varactor diode resonates at ten distinct frequencies as follows: 1.375GHz, 1.525GHz, 1.725GHz, 2.45GHz, 3.45GHz, 4GHz, 5.3GHz, 5.45GHz, 5.5GHz and 5.825GHz. Design and optimization of microstrip antenna with analysis for different capacitance values of varactor diode is carried out using IE3D simulation tool. The proposed design is realized using FR4 (Dielectric constant ε_r = 4.4) substrate with dimension (41 × 41 × 1.6) mm³. A single varactor diode inserted upon the slot is used to switch the operating frequency. The proposed design of antenna, both simulated and fabricated is seen to have close agreement, and is appropriate to be used in L, S and C band applications.

Quantum Dot Cellular Automata (QCA) is one of the new technologies beyond CMOS. Among various other technologies, this is considered to be most feasible and viable due to its area and power advantages. In this paper the discussion about the origin and progress of research works is carried out with respect to QCA domain. Starting from the basic gate study and designing of Adders few other functional blocks are also discussed. This paper proposes QCA as it is considered as the upcoming technology after the saturation of CMOS technology. QCA is considered so due to its advantages in area, power and timing requirements. This domain is still under research and has not been carried to large extent. Hence the authors have made an attempt in exploring it through designing and have simulated the proposed designs. the working of the design is proved through the simulation results.

Extending lifetime for far off sensor coordinate through immaterial human perception is unreasonable. To comprehend this concern assorted researchers come up through assembly methodology which preserve construct up a far-off sensor put together extra adaptable, prolonged life time, proficient imperativeness. Nevertheless, an imposing part of planned computations overstuff the congregation chief in midst of pack diversion plan. Front attitude such a circumstance, proposal of fluffy analysis is superior the circumstance essential authority in distant sensor systematize. Fleecy reasoning is lesion up being additional usual for heap dispersion amongst sensor center points at last extending structure lifetime. Here Type2 fleecy reasoning is planned which handles uncertain level decisions enhanced than sort feathery reasoning. For the most part here essentialness smoothing out provoking extend structure lifetime using gather is cultivated. Likewise, the proposed fleecy reasoning which picks the gathering head just as show how organize life span can be stretched out close via immaterial cluster adversity in the midst of transmission process. Various computation as well as the connected structure lifetime is in like manner showed up through feathery analysis mounting most outrageous structure lifetime appeared differently in relation to other people.

The paper aims to develop a regression model using the NKI breast cancer data set. The methodology used to achieve the objectives includes three variations of regression methods viz., linear, multiple, and polynomial, respectively. Regression analysis is one of the efficient predictive modeling methods that help understand the mathematical relationship between the variables. The multiple and polynomial regression methods also work in line with the linear regression model, but the number of independent variables will be varying. Queries related to health care data are of practical interest. The outcome of the predictive model helps in analyzing the behavior of different features of the breast cancer data set and provides useful insights towards the diagnosis of a patient. 14 out of 1570 useful features of the NKI data set are selected for the regression analysis. With different combinations of independent and dependent variables, it is found that multiple regression performs better with 83% accuracy.

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.

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.

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.