Global Transitions Proceedings最新文献

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.

Arduino UNO-based Solar powered Grasscutter designed to cut healthy grass in places like parks, hotels, public places, etc., The Grasscutter is designed through IoT (Internet of Things) technology, which is controlled remotely through Blynk application supported with Bluetooth module. The proposed model consists of hardware components like Arduino UNO, Solar panel, DC motor, motor driver, rechargeable batteries and Bluetooth module. The designed model is programmed through Arduino IDE to control the operation of the Grasscutter. The control mechanism and movements such as Forward movement, Backward movement, Right movement, Left movement, On mechanism, Off mechanism and Stop function for the Grasscutter prototype. An ultrasonic sensor connected to the head of the model avoids the system from colliding with obstacles while in movement.

Recent advancements in the communication protocols and the networking technologies have enabled connectivity of a wide range of objects, resulting in the Internet of Things (IoT) network. The protocols like MQ Telemetry Transport (MQTT), as well as Constrained Application Protocol (CoAP) are moderately capable of providing the management of heterogeneous wireless sensor networks even in an environment with very limited bandwidth. In this paper, we develop a lightweight encryption algorithm to obtain reliable secure data transmission between IoT devices. We propose a Secure Reliable Message Communication (SEC-RMC) protocol using Mosquitto MQTT message broker with cryptographic enhancements to offer security services and also provide the mutual authentication in the IoT environment at the transport layer. The proposed scheme decreases the number of messages transmitted between the devices. Also, the authentication scheme provides resistance to DNS hacking, routing table poisoning and packet mistreatment. On comparison with the existing methods, the transmission time has been reduced by 80% in this work.

Twitter is a miniature writing for a blog site which gives phase to individuals to share as well as communicate their perspectives about point, activities, items plus other medicinal harms. Tweets can be arranged keen on assorted classes reliant on their significance through the tip looked. NLP for wellbeing linked exploration be at present utilize in combination of tweet keen on positive as well as negative classes reliant on their approach utilizing normal language handling strategy. This paper contain execution of NLP (Bag of words) for message alliance reliant on twitter omicron tweet informational catalog utilizing sentiment preparing information utilizing twitter statistics set as well as suggest a plan to further expand categorization. Utilization of Lemmatization alongside NLP can further expand accuracy of characterization of tweets, via bountiful encouragement, pessimism as well as impartiality score of vocabulary present in tweet. For genuine effecting of this structure python through NLP plus twitter informational compilation be used. In this paper we are concerning feelings exploration in twitter tweet for omicron datasets to arrange the survey of all consumers whether it is positive, negative or impartial.

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.

Cloud Computing has redefined the industrial side of application development and the delivery of software that has a strong base with the creation of both web apps and mobile apps. There is a significant shift in the mindset of technology organizations about the creation of specified services with the introduction of concepts like pay-for-use and subscription-based modeling. The distributed nature of resources makes it easier on the client-side to focus on the business aspects more than the maintenance of infrastructure and integrations scenarios. In a huge setup with a possibility of conflict among resources, it is essential to have a hierarchy that provides optimized execution time of processes and also ensures minimal waiting time of the resources involved in the processes. This paper proposes the use of Graph theory and the concepts of Graph coloring to schedule resources better and, at the same time, have lesser depended on tasks to ensure lesser conflicts among the resources. This paper also explores a scenario that helps understand the use of graph coloring techniques to handle better the dependency of involved processes in a cloud setup provided by the cloud vendors.

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.

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.

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.

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.