Context aware knowledge bases for efficient contextual retrieval: design and methodologies

Contextual retrieval is a critical component for efficient usage of knowledge hidden behind the data. It is also among the most important factors for user satisfaction. It essentially comprise of two equally important parts — the retrieval mechanism and the knowledge base from which the information is retrieved. Despite the importance, context aware knowledge bases have not received much attention and thereby, limiting the efficiency of precise context aware retrieval. Such knowledge bases would not only contain information that has been efficiently stored but the knowledge contained would be context based. In other words, machines would understand the knowledge and its context rather than just storing data. This would help in efficient and context aware retrieval. The current paper proposes rules and methodologies for construction of such context aware knowledge bases. A case study to demonstrate the application of the methodology and test the efficiency of the proposed methodology has also been presented. The results indicate that knowledge bases built on these principles tend to generate more efficient and better context aware retrieval results

Least square-support vector machine based brain tumor classification system with multi model texture features

Radiologists confront formidable challenges when confronted with the intricate task of classifying brain tumors through the analysis of MRI images. Our forthcoming manuscript introduces an innovative and highly effective methodology that capitalizes on the capabilities of Least Squares Support Vector Machines (LS-SVM) in tandem with the rich insights drawn from Multi-Scale Morphological Texture Features (MMTF) extracted from T1-weighted MR images. Our methodology underwent meticulous evaluation on a substantial dataset encompassing 139 cases, consisting of 119 cases of aberrant tumors and 20 cases of normal brain images. The outcomes we achieved are nothing short of extraordinary. Our LS-SVM-based approach vastly outperforms competing classifiers, demonstrating its dominance with an exceptional accuracy rate of 98.97%. This represents a substantial 3.97% improvement over alternative methods, accompanied by a notable 2.48% enhancement in Sensitivity and a substantial 10% increase in Specificity. These results conclusively surpass the performance of traditional classifiers such as Support Vector Machines (SVM), Radial Basis Function (RBF), and Artificial Neural Networks (ANN) in terms of classification accuracy. The outstanding performance of our model in the realm of brain tumor diagnosis signifies a substantial leap forward in the field, holding the promise of delivering more precise and dependable tools for radiologists and healthcare professionals in their pivotal role of identifying and classifying brain tumors using MRI imaging techniques

Critical security challenges in cloud computing environment: An appraisal

This paper mainly contributes a comprehensive survey on the climacteric security challenges imposed by cloud computing. The paper highlights the challenges/loopholes existing in cloud environment despite all the efforts adopted by organizations, and offers the recommendations for cloud providers as well as users. In this paper, more than 20 research papers pertaining to cloud security, in the span of past 5 years, have been studied and analyzed and twelve most important security threats to organizations have been identified that need attention by research community for encouraging their cloud adoption. This review concludes that some measures have to be adopted for accomplishing complete security in all aspects in the cloud environment. Unlike previous approaches, this effort is directed towards providing a thorough and inclusive review of the security vulnerabilities in cloud. Furthermore, this paper tries to suggest means how cloud federations can enhance security while mitigating risk and building customer trust at the same time

A study on performance evaluation of conventional cache replacement algorithms: A review

Cache Replacement Policies play a significant and contributory role in the context of determining the effectiveness of cache memory cells. It has also become one of the major key features for efficient memory management from the technological aspect. Hence, owing to the existing critical computing systems, it has become too essential to attain faster processing of executable instructions under any adverse situations. In the current scenario, the state of art processors such as Intel multi-core processors for application specific integrated circuits, usually employ various cache replacement policies such as Least Recently Used (LRU) and Pseudo LRU (PLRU), Round Robin, etc. However, fewer amounts of existing research works are found till date to utter about explicit performance issues associated with the conventional cache replacement algorithms. Therefore, the proposed study intended to carry out a performance evaluation to explore the design space of conventional cache replacement policies under SPEC CPU2000 benchmark suite. It initiates and configures the experimental Simple Scalar toolbox prototype on a wide range of cache sizes. The experimental outcomes obtained from the benchmark suite show that PLRU outperforms the conventional LRU concerning computational complexity and a variety of cache blocks organization

MANET – A cogitation of its design and security issues

With the fast proliferation of wireless devices e.g., cell phones, PDAs, laptops, wireless sensors, etc., the significance of wireless communication especially mobile adhoc networking is quite evident. The need to shun dependency on fixed infrastructure networks led to the evolution of Mobile Adhoc Network (MANET). MANETs have numerous advantages like dynamic topology, lack of centralization, speedy and affordable installation, etc. but the features of MANETs impart certain complications as well such as unreliability caused due to node mobility, bandwidth constraints of the wireless link making it vulnerable to attacks, lack of standardization of security solutions, etc. Notably, security in MANETs is a challenge to researchers. In this paper, an overview of MANETs is provided followed by a critical account of the relevant review work that has been carried out so far in this domain. This paper tries to highlight the various issues in the design and security requirements of MANETs. As such, the proposed study revolves around all the security aspects of mobile adhoc networks including routing protocols, mobile agents, malicious nodes, intrusion detection system and other factors that affect the quality of service in MANET

An efficient cache replacement algorithm for minimizing the error rate in L2-STT-MRAM caches

In the recent times, various challenges are being encountered during SRAM cache design and development which lead to a situation of converting the memory cell technologies into on-chip embedded caches. The current research statistics towards cache designing reveals that Spin Torque Transfer Magnetic RAMs, preferably termed as STT-MRAMs has become one of the most promising areas in the field of memory chip design. Hence, it gained a lot of attention from the researchers due to its dynamic direct map and data access policies for reducing the average cost i.e. time and energy optimization. Instead of having efficient main memory access capability, STTMRAMs suffer due to high error rate caused during stochastic switching in write operations. Cache replacement algorithms play a significant role in minimizing the error rate in write operations. The proposed study aims to offer a theoretical and analytical modeling of an efficient cache replacement scheme to overcome the error as mentioned above in L2-STT-MRAM caches. The performance analysis of the proposed algorithm ensures its effectiveness in reducing the error rate and cost overheads as compared to the conventional LRU technique implemented on SRAM cells