Gigantic Growth Research Articles

Generation of Electricity using Trash

Abstract: The gigantic growth in the amount of waste materials produced in India and their potentially dangerous results in the environment and human health Which have led to create several diseases in Human bodies, so we need to ingurgitate scholarly methods for safe arrangement of garbage. This is an innovative idea of generating electricity Using solid waste. Which lead to decrease pollution by stopping to produce almost all dangerous gasses like CO2, CO, SO2, NO2 and Heavy metals such as mercury to a huge number. With a population of millions India produces large number of wastes every day to Contemplate about this idea. So, we felt that it is valuable working on this project and it is high time to inject the idea of Waste to Energy in India. Firstly, a map plan was carried out for research to collect the fact of total waste generation in India. During this research it was found that some locals were generating electricity using this process ago which encouraged the researchers to carry forward this Project.

A new ranking approach for E-commerce websites based on fuzzy TOPSIS algorithm

With the gigantic growth of the E-commerce market, E-commerce websites are becoming more and more numerous. Customers of E-commerce websites are spoiled for choice and have encountered several problems in choosing not only the right products but also the E-commerce website from which they want to purchase the desired products. E-commerce websites ranking is recognized as a complex multi-criteria decision-making (MCDM) problem. In practice, clients of E-commerce websites generally have difficulty expressing their judgments in precise numbers because the criteria are some- times imprecise and sometimes uncertain and ambiguous. In this context, we propose to use fuzzy logic to allow clients to express their ratings in natural language and propose an approach based fuzzy technique for order preference by similarity to the ideal solution (TOPSIS) for E-commerce websites ranking. A numerical experimentation was conducted for validate the effectiveness of the proposed approach.

Circuit Modeling and Analysis of Wearable Antennas on the Effect of Bending for Various Feeds

The promising utilization of wearable antennas has experienced gigantic growth during the last decade. An antenna is one of the most significant and crucial components of wireless wearable devices. They need to be particularly designed to work while worn on and off the body. The wearable antenna embedded into clothing finds its use in wireless communications including tracking and navigation, mobile and wearable computing, and public safety and security. For user accessibility, there is a growing requirement for incorporating antennas on or in clothing. Determining the dielectric characteristics of the flexible substrates utilized in the design of the wearable antenna is also essential. In this paper, a Microstrip Ring Resonator (MRR) is employed to determine the dielectric properties of fabric substrates followed by state-of-the-art designs of wearable antennas and their bending effects at ISM band frequencies. An electrical equivalent model is designed to realize the potentials inside the geometry of an antenna under bending environment. This is followed by observing the effect of bending for different feeding methods on the wearable antenna's parameters when bending on a certain radius. The robustness of the proposed wearable antenna is examined by measuring the antenna under various bending curvatures for return loss, gain, and efficiencies. This will disclose the various contemplations for designing a wearable antenna from different feeding mechanisms with different materials and exemplifying the antenna's outcomes to dynamic moments of the human body. The performance of the proposed wearable antenna is acceptable even in a deformation environment, and there is a good agreement between the measured and the simulation results.

Domain and Challenges of Big Data and Archaeological Photogrammetry With Blockchain

With gigantic growth of data volume that is moved across the web links today, there has been a gigantic measure of perplexing information produced. Extremely huge sets of data including universities, organizations framework, institution gas, petroleum sector, healthcare, that have so enormous thus complex information with more differed structure. The major challenge is how to handle this significant volume of data, also in archaeological photogrammetry which alluded to as Big Data. Although big data has to be securely flying and conveyed through the internet. It cannot be controlled with regular conventional methods that fail to handle it, so there is a need for more up-to-date developed tools. The big data have frequently divided into V’s characteristics beginning from three V’s: volume, velocity and variety. The initial three V’s have been stretched out during time through researches to arrive 56 V’s till now. Among them are three newfound by the author that implies it multiplied near twenty times. Researcher had to dive to search for all of these characteristics in many researches to detect and build comparisons to answer the old, current, and restored essential inquiry, “how many V’s aspects (characteristics) in big data with archaeological photogrammetry and blockchain”. This paper provides a comprehensive overview of all secured big data V’s (characteristics) as well as their strength and limitations with archaeological photogrammetry and blockchain.

A New Approach for Deception Detection in Open Domain Text

The gigantic growth of platforms that give individuals the ability to write a review that is visible to everyone and the huge number of documents shared on the internet have triggered the researchers to try to detect if these platforms are trying to mislead and deceive people. There is a crucial need to find ways to automatically identify fake reviews and detect deceptive people or groups. The main aim of this research is to detect deception in open domain text by using a machine learning technique. Several sets of features are used to analyse the text including unigram, part of speech, and production rules. The experimental results showed that combined feature sets of (part of speech and production rules) using the support vector machine classifier achieve the best accuracy, and it clearly improves on the accuracy of the results reported in a previous study.

Cost Analysis and Productivity of Excavator, Bulldozer, Tipper Truck with using Fleet Management

The gigantic growth in Indian construction and development sector have been observed at the start of 21st century. Due to rapid development in engineering and technology numerous mechanical equipment has been utilized from micro to macro level infrastructural projects like industrial, roads and bridges, multi-storey steel structures etc. The following research is made for finding out cost-effectiveness and fecundity of heave and excavating mechanical equipment based on there use on different site locations by mean of proper modelling and cost analysis furthermore testing of computer software on the basis of there productivity. The research tends to focus on spontaneity in designing the software based fleet management technique of tool managing on different field of construction so that optimal results can be achieved through them. These advance system includes tool to be implemented with a code which is verified by the system then an algorithmic calculation is made by software to provide best available solution on site resulting in ease in decision making and reducing the time requirement and making it cost effective.

Contribution of wavelets to cybersecurity: Intrusion detection systems using neural networks

The gigantic growth of the exchanged digital data has raised important security challenges. In this ecosystem, connected objects, systems and networks are exposed to various cyber threats endangering sensitive data and compromising confidentiality, integrity and authentication. Modelling intrusion detection systems (IDS) constitute an important research field with a major goal to protect targeted systems and networks against malicious activities. Many network IDS have been recently designed with artificial intelligence techniques. Signal processing techniques have been applied in network detection systems due to their ability to help for a good intrusion detection. At the same context, the wavelet transform which is considered as a very efficient tool for the decomposition and reconstruction of signals can be recommended in the design of powerful network detection systems, and can be applied for data preprocessing denoising and extracting information. Wavelets combined to neural networks can be useful for modelling intrusion detection with the main challenges to reduce the false alarms, increase the test accuracy and increase novel attacks detection rate. In this work, we present a major contribution in the research field to better understand how wavelets and neural networks can be combined for modelling efficient IDS.

ПРЕИМУЩЕСТВА И ПЕРСПЕКТИВЫ РАЗВИТИЯ ФИНАНСОВОГО АУТСОРСИНГА В РОССИИ

Outsourcing as a socio-economic phenomenon has been showing gigantic growth rates in the world for many years. In Russia, the penetration of outsourcing is much lower, but the growth rate is also impressive. Against the background of the crisis and external pressure on the Russian economy, its role is even more important, since most researchers consider outsourcing as a means of increasing the efficiency and reducing the costs of the enterprise. The article considers the theoretical foundations of the phenomenon – different approaches to definition and classification, advantages and risks. The paper reveals the problems of legal support for outsourcing, as well as the prospects for the development of financial outsourcing in Russia.

Predicting real estate market trends and value using pre-processing and sentiment text mining analysis

Ubiquitous growth in the text mining field is unprecedented, where social media mining is playing a significant role. Gigantic growth of text mining is becoming a potential source of crowd wisdom extraction and analysis especially in terms of text pre-processing and sentiment analysis. The analysis of a potential influence of sentiment on real estate markets controversially discussed by scholars of finance, valuation and market efficiency supporters. Therefore, it’s a significant task of current research purview which not only provide an appropriate platform for the contributors but also for active real estate market information seekers. Text mining has gained the widespread attention of real estate market information users which is almost on explosion level. Accessibility of data on such behemoth scale mandates regular and critical analysis of this information for various perspectives’ plausibility. Rich patterns of online social text can be exploited to extract the relevant real estate information effectively. As text mining plays a significant and crucial role in discovery of these insights therefore its challenges and contribution in social media analysis must be explored extensively. In this paper, we provide a brief about the current summary of the modern state of text mining in pre-processing and sentiment for the real estate market analysis. Empha-sis is placed on the resources and learning mechanism available to real estate researchers and practitioners, as well as the major text mining tasks of interest to the community. Thus, the main aim of this chapter is to expound and intellectualize the domains of social media which are accessible on an extraordinary range in the field of text mining real estate for predicting real estate market trends and value.

Burst: real-time events burst detection in social text stream.

Gigantic growth of social media and unbeatable trend of progress in the direction of the web seeking user’s interests have generated a storm of social text streams. Seeking information to know the phenomenon of various events in the early stages is quite interesting. Various kinds of social media live streams attract users to participate in real-time events to become a part of an immense crowd. However, the vast amount of text is present on social media, the unnecessary information bogs a social text stream filtering to extract the appropriate topics and events effectively. Therefore, detecting, classifying, and identifying burst events is quite challenging due to the sparse and noisy text of Twitter. The researchers' significant open challenges are the effective cleaning and profound representation of the text stream data. This research article's main contribution is to provide a detailed study and explore bursty event detection in the social text stream. Thus, this work's main motive is to present a concise approach that classifies and detects the event keywords and maintains the record of the event based on related features. These features permit the approach to successfully determine the booming pattern of events scrupulously at different time span. Experiments are conducted and compared with the state-of-the-art methods, which reveals that the proposed approach is proficient to detect valuable patterns of interest and also achieve better scoresto extract burst events on social media posted by various users.

Do Pregnant Women Using Smartphones Care About Their Privacy?

With a gigantic growth in internet and application usage, especially with the use of smartphones, ‘privacy' concerns reverberates and dealing with privacy issues from such an arena is a new task for many researchers. This wide usage of mobile application and internet opens up Pandora's box – ‘privacy concerns'. This paper tries to understand the privacy concern hovering among pregnant women. The study found that education, mHealth literacy, mobile app usage, and employment is a robust predictor of HIPC. However, age, social-economic status, maternity benefit scheme membership, pregnancy apps usage, mobile spending, mobile basic services usage does not play a significant role in HIPC. Overall, the results suggest that as users' awareness level on privacy policies and issues increase, privacy concern decreases.

Image Retrieval with Fusion of T hepade’s Sorted B lock Truncation C oding n ary based Colo r and Local Binary Pattern based Texture F eature s with D ifferent C olor Plac es

In these years, there has been a gigantic growth in the generation of data. Innovations such as the Internet, social media and smart phones are the facilitators of this information boom. Since ancient times images were treated as an effective mode of communication. Even today most of the data generated is image data. The technology for capturing, storing and transferring images is well developed but efficient image retrieval is still a primitive area of research. Content Based Image Retrieval (CBIR) is one such area where lot of research is still going on. CBIR systems rely on three aspects of the image content namely texture, shape and color. Application specific CBIR systems are effective whereas Generic CBIR systems are being explored. Previously, descriptors are used to extract shape, color or texture content features, but the effect of using more than one descriptor is under research and may yield better results. The paper presents the fusion of TSBTC n-ary (Thepade's Sorted n-ary Block Truncation Coding) Global Color Features and Local Binary Pattern (LBP) Local Texture Features in Content Based Image with Different Color Places TSBTC n-ary devises global color features from an image. It is a faster and better technique compared to Block Truncation Coding. It is also rotation and scale invariant. When applied on an image TSBTC n-ary gives a feature vector based on the color space, if TSBTC n-ary is applied on the obtained LBP (Local Binary Patterns) of the image color planes, the feature vector obtained is be based on local texture content. Along with RGB, the Luminance chromaticity color space like YCbCr and Kekre’s LUV are also used in experimentation of proposed CBIR techniques. Wang dataset has been used for exploration of proposed method. It consists of 1000 images (10 categories having 100 images each). Obtained results have shown performance improvement using fusion of BTC extracted global color features and local texture features extracted with TSBTC n-ary applied on Local Binary Patterns (LBP).

Cloud Qos Ranking Prediction using Tanimoto Coefficient Similarity Based Deep Learning Method

Cloud computing is a service which provides virtualized resources conforming to the end-user needs. Infrastructure, platform and software included in it. For the last two decades, it has achieved very gigantic growth. Currently, there are several cloud service providers in the market. The primary aim of this research is to minimize cloud service violation. It helps the service providers in exempting the penalty enhancing their reliability. So, cloud service QOS prediction is a research problem that must be solved. It is a very necessary thing for cloud service providers and cloud users. We have discussed several QoS prediction related to researches in the literature survey. But none of them has given a satisfactory QoS prediction. In this paper, we proposed a Tanimoto Coefficient Similarity-Based Deep Learning Method for QoS ranking prediction. The analysis helps service providers choose a suitable prediction method with optimal control parameters so that they can obtain accurate prediction results and avoid violation penalties. In comparison with the prior method in practice, the proposed method is more significant in terms of prediction accuracy, prediction time and error rate.

Characterization of Progenies from Polycrosses of S. robustum Clones f. sanguineum

The contribution of S. robustum in the development of cultivated varieties of sugarcane is very less compared to other wild species of sugarcane. Some of the popular varieties of Hawaii were immensely benefited from this wild species but only the Port Moresby type could find a place in the ancestry. In India also, some of the recent Co canes have the improved clones of S. robustum in their pedigree. This paper discusses the characterization of interspecific progenies generated from polycross on two clones S. robustum forma sanguineum (red fleshed S. robustum) and their potential in the crop improvement programme of sugarcane. This is the first report of utilization of the red fleshed S. robustum clones in breeding programme of sugarcane. The progenies developed possess high variability for morphological and yield traits with considerable improvement over the S. robustum parents. The variability generated will be of utmost importance in crop improvement programme of sugarcane as the world gene bank holds only very limited variability for this group of plants (2n = 60) which are characterized by gigantic growth and red coloured flesh.

Dental Management of a Young Child Affected by Galactosialidosis and a Gigantic Abdominal Growth

Galactosialidosis (GS) is a rare form of lysosomal storage disease that involves a broad spectrum of skeletal and soft tissue abnormalities. We report here on a 4-year 7-month-old boy with mild mental retardation, exhibiting multiple caries cavities and associated infectious foci and macroglossia. A huge abdominal enlargement due to peritoneal ascites was evident. Behavioral management and patient positioning on the dental chair represented a true challenge. The patient was treated under general anesthesia. However, life-threatening postoperative complications occurred because of the impossibility of extubating the patient. A very careful preanesthetic assessment is crucial in children affected by general conditions associated with airway anomalies, such as GS.

PERFORMANCE ANALYSIS OF IPv4 AND IPv6 INTERNET TRAFFIC

The gigantic growth of the internet communication technology has illustrated its value and benefits to private businesses, government organizations, worldwide professionals, academic institutes and individuals over the past few years. The size and range of computing devices connected to the internet, substantially increased because of IPv6 and offers the potential to establish a much more powerful internet compared to the IPv4. IPv6 developed by the IETF to deal with a shortage of IP addresses under IPv4. New features of IPv6 enhance packet processing speeds over routers, switches and end systems. These improved features will have different traffic characteristics than IPv4. The internet traffic which was earlier assumed as Poisson is now shown to have fractal characteristics as; heavy tailedness, self-similarity and long range dependency. Internet traffic showing above characteristics are found to have burstiness at multiple timescales. This behavior impacts network performance and degrades it substantially. It also increases complexity for network design and create difficulties to maintain desired QoS. IPv4 traffic has been well established as self-similar traffic. Nowadays, IPv6 forming a larger share of the internet traffic and it is pivotal to asses IPv6 with regards to fractal behavior. This will enable network designers to do necessary changes in the existing network to reconcile with IPv6. In this paper we compared IPv4 and IPv6 with respect to fractal behavioral characteristics. It is found that IPv6 shows higher degree of heavy tailedness, higher values of Hurst parameter values, higher fractal dimension values i.e. it is more self-similar, greater autocorrelation achieved even at larger lag and thus showing more burstiness.

Performance Evaluation of Opportunistic Resource-Sharing Scheme Using Socially Oriented Outsourcing in Wireless Devices

With the gigantic growth of social-enabled web during the past years and the creation of new platforms that encompass the social communication, a new application paradigm is utilized. This application paradigm is enforced with the users’ tendency to associate social behavior with their daily demands. As this paradigm can be utilized in all communication environments (wired and wireless), there is a need to eliminate the opportunistic aggravation of the resource-sharing process in wireless communication and enable a mechanism that will consider the problem of content distribution by these devices. As wireless devices experience intermittent connectivity, resource exchange by these devices can rarely be successful due to the frequent variations in the communication and the dynamic changes in the topology. This work takes into account the proposed socio-technical model in order to strengthen the interactions among devices for enabling reliability and availability of the requested resources. In addition, the scheme considers the users’ modeled interaction metrics and their location profiling based on social parameters and allows on-demand resource sharing through the opportunistic socially oriented caching. Different concepts of social centralities are introduced and modeled in order to meet the improvements of resource sharing considering the temporal centrality characteristic of each node and the social interactions among users. The proposed model enables one, via the users’ social and location-aware interactions, and the introduced aging factor, to strengthen or weaken the diffusion process of highly ranked requested resources. The proposed collaborative opportunistic resource-sharing scheme is thoroughly evaluated through experimental simulation for the effectiveness of the resource-sharing efficiency as well as for the accuracy of end-to-end reliability and resource-sharing completeness.

China’s Maritime Security Policy in the Indian Ocean: Assessment and Implication for Korea

China became the second largest economic power. But the gigantic growth of China has triggered another concern that it will project its influence around the Oceans in order to have maritime hegemony. Actually, China recently provoked conflicts with the Japan, Philippines, Vietnam as well as the U.S in South and East China Sea. However, it would be premature to decide that Chinese maritime security policy is quite offensive for securing maritime hegemony. This is because maritime environment in the Indian Ocean is quite different from that of the Pacific Ocean. First, in the Indian Ocean, there is no dominant maritime power, unlike in the Pacific Ocean where bilateral military alliances led by the U.S are playing a key role in maritime status quo. Second, there is much room for cooperation among nations because non-traditional security issues such as piracy, armed robbery against ships and illegal transportation of WMD cannot be tackled by one or some nations. Third, Africa becomes an attractive region to China for its national resources. Against this backdrop, China is pursuing ‘tailored strategy’ by becoming ‘multilateral’ and ‘cooperative’ in the Indian Ocean, while staying ‘unilateral’ and ‘conflicting’ in the Pacific Ocean. For example, China is trying to make a positive contribution to peacekeeping, disaster relief and count-piracy operations in the Indian Ocean. Considering this, Korea should prepare multi-leveled maritime policy. At a bilateral level, Korea should consolidate military alliances with the U.S and Japan. At a multilateral level, it needs cooperation among nations to fight against non-traditional security threats like piracy. Finally, at a unilateral level, it should strengthen capability of navy forces by equipping with modernized equipment with cutting-edge technologies.

The Technological Metaphysics of Planetary Space: Being in the Age of Globalization

In this paper I look at globalization in terms of the metaphysical event of being, arguing that in order to discuss the issue of globalization the ontological conditions of possibility for its historical appearance need to be prioritized. Using the philosophy of Martin Heidegger allows us to see how globalization belongs to the modern calculable understanding of space, and therefore to the particular metaphysical event of technological existence. Through an acknowledgment of the metaphysics of gigantic growth and totalitarian magnitude, globalization is seen as a planetary consummation of the kind of technological disclosure of being. This is a period in which the entire globe stands at the service of complete and manipulative calculability. In this paper I argue that in globalization the world is ontologically understood as a picture in which everything becomes gathered together as a reserve to be ordered and challenged by anthropocentric will and value-creation.

The largest growth cones in the animal kingdom: an illustrated guide to the dynamics of Aplysia neuronal growth in cell culture

The marine mollusc, Aplysia californica is a powerful experimental model in cellular and systems neuroscience. Aplysia neurons are large, colored, and located at the ganglionic surface. Because of this, many neurons can be easily identified in terms of their physiological properties, synaptic connections, and behavioral roles. Simple networks can be reconstructed in cell culture and have been widely useful for cellular and molecular biological studies of neuronal growth, synaptogenesis, and learning and memory mechanisms. Here, we show that Aplysia neurons can form truly gigantic growth cones reaching up to 630 μm in diameter making them the largest growth cones ever reported in the animal kingdom. Second, using time-lapse video microscopy we have characterized the dynamics of neuronal outgrowth for 3 identified cell types (mechanosensory neurons, L7 motoneurons, and modulatory MCC neurons) representing 3 major functional classes of neurons. We show both cell-specific and neurite-specific growth characteristics and an irregular oscillatory rate of outgrowth ranging from 20 to 100 μm/h. Third, we characterized the dynamics of axotomy-induced neurite outgrowth as well as extrasomatic localization of β-tubulin mRNA in restricted regions of neuronal processes including growth cones and varicosities. The extrasomatically located mRNAs can be an important pool of neuronal transcripts supporting semiautonomous behavior of growth cones and localized synthesis of proteins in distinct and distant neuronal compartments. The reported data are compared with the existing literature from Lymnaea and Helisoma neurons as well as vertebrate preparations. Finally, our observations can provide an illustrated guide to complex behavior of neurons and glia in cell culture as well as their dependence upon various trophic factors and responses to neuronal injury.