Comparison and analysis of ten static heuristics-based Internet data replication techniques

This paper compares and analyzes 10 heuristics to solve the fine-grained data replication problem over the Internet. In fine-grained replication, frequently accessed data objects (as opposed to the entire website contents) are replicated onto a set of selected sites so as to minimize the average access time perceived by the end users. The paper presents a unified cost model that captures the minimization of the total object transfer cost in the system, which in turn leads to effective utilization of storage space, replica consistency, fault-tolerance, and load-balancing. The set of heuristics include six A-Star based algorithms, two bin packing algorithms, one greedy and one genetic algorithm. The heuristics are extensively simulated and compared using an experimental test-bed that closely mimics the Internet infrastructure and user access patterns. GT-ITM and Inet topology generators are used to obtain 80 well-defined network topologies based on flat, link distance, power-law and hierarchical transit–stub models. The user access patterns are derived from real access logs collected at the websites of Soccer World Cup 1998 and NASA Kennedy Space Center. The heuristics are evaluated by analyzing the communication cost incurred due to object transfers under the variance of server capacity, object size, read access, write access, number of objects and sites. The main benefit of this study is to facilitate readers with the choice of algorithms that guarantee fast or optimal or both types of solutions. This allows the selection of a particular algorithm to be used in a given scenario.     

Automated Deep Learning Driven Crop Classification on Hyperspectral Remote Sensing Images

Hyperspectral remote sensing/imaging spectroscopy is a novel approach to reaching a spectrum from all the places of a huge array of spatial places so that several spectral wavelengths are utilized for making coherent images. Hyperspectral remote sensing contains acquisition of digital images from several narrow, contiguous spectral bands throughout the visible, Thermal Infrared (TIR), Near Infrared (NIR), and Mid-Infrared (MIR) regions of the electromagnetic spectrum. In order to the application of agricultural regions, remote sensing approaches are studied and executed to their benefit of continuous and quantitative monitoring. Particularly, hyperspectral images (HSI) are considered the precise for agriculture as they can offer chemical and physical data on vegetation. With this motivation, this article presents a novel Hurricane Optimization Algorithm with Deep Transfer Learning Driven Crop Classification (HOADTL-CC) model on Hyperspectral Remote Sensing Images. The presented HOADTL-CC model focuses on the identification and categorization of crops on hyperspectral remote sensing images. To accomplish this, the presented HOADTL-CC model involves the design of HOA with capsule network (CapsNet) model for generating a set of useful feature vectors. Besides, Elman neural network (ENN) model is applied to allot proper class labels into the input HSI. Finally, glowworm swarm optimization (GSO) algorithm is exploited to fine tune the ENN parameters involved in this article. The experimental result scrutiny of the HOADTL-CC method can be tested with the help of benchmark dataset and the results are assessed under distinct aspects. Extensive comparative studies stated the enhanced performance of the HOADTL-CC model over recent approaches with maximum accuracy of 99.51%.     

Sine Cosine Optimization with Deep Learning-Based Applied Linguistics for Sentiment Analysis on COVID-19 Tweets

Applied linguistics is an interdisciplinary domain which identifies, investigates, and offers solutions to language-related real-life problems. The new coronavirus disease, otherwise known as Coronavirus disease (COVID-19), has severely affected the everyday life of people all over the world. Specifically, since there is insufficient access to vaccines and no straight or reliable treatment for coronavirus infection, the country has initiated the appropriate preventive measures (like lockdown, physical separation, and masking) for combating this extremely transmittable disease. So, individuals spent more time on online social media platforms (i.e., Twitter, Facebook, Instagram, LinkedIn, and Reddit) and expressed their thoughts and feelings about coronavirus infection. Twitter has become one of the popular social media platforms and allows anyone to post tweets. This study proposes a sine cosine optimization with bidirectional gated recurrent unit-based sentiment analysis (SCOBGRU-SA) on COVID-19 tweets. The SCOBGRU-SA technique aimed to detect and classify the various sentiments in Twitter data during the COVID-19 pandemic. The SCOBGRU-SA technique follows data pre-processing and the Fast-Text word embedding process to accomplish this. Moreover, the BGRU model is utilized to recognise and classify sentiments present in the tweets. Furthermore, the SCO algorithm is exploited for tuning the BGRU method’s hyperparameter, which helps attain improved classification performance. The experimental validation of the SCOBGRU-SA technique takes place using a benchmark dataset, and the results signify its promising performance compared to other DL models.     

Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

Machine learning (ML) becomes a familiar topic among decision makers in several domains, particularly healthcare. Effective design of ML models assists to detect and classify the occurrence of diseases using healthcare data. Besides, the parameter tuning of the ML models is also essential to accomplish effective classification results. This article develops a novel red colobuses monkey optimization with kernel extreme learning machine (RCMO-KELM) technique for epileptic seizure detection and classification. The proposed RCMO-KELM technique initially extracts the chaotic, time, and frequency domain features in the actual EEG signals. In addition, the min-max normalization approach is employed for the pre-processing of the EEG signals. Moreover, KELM model is used for the detection and classification of epileptic seizures utilizing EEG signal. Furthermore, the RCMO technique was utilized for the optimal parameter tuning of the KELM technique in such a way that the overall detection outcomes can be considerably enhanced. The experimental result analysis of the RCMO-KELM technique has been examined using benchmark dataset and the results are inspected under several aspects. The comparative result analysis reported the better outcomes of the RCMO-KELM technique over the recent approaches with the of 0.956.     

Investigating quality routing link metrics in Wireless Multi-hop Networks

In this paper, we propose a new Quality Link Metric (QLM), “Inverse Expected Transmission Count (InvETX),” in Optimized Link State Routing (OLSR) protocol. Then, we compare performance of three existing QLMs which are based on loss probability measurements: Expected Transmission Count (ETX), Minimum Delay (MD), and Minimum Loss (ML) in Static Wireless Multi-hop Networks (SWMhNs). A novel contribution of this paper is enhancement in conventional OLSR to achieve high efficiency in terms of optimized routing load and routing latency. For this purpose, first we present a mathematical framework, and then to validate this frame work, we select three performance parameters to simulate default and enhanced versions of OLSR. The three chosen performance parameters are throughput, Normalized Routing Load, and End-to-End Delay. From the simulation results, we conclude that adjusting the frequencies of topological information exchange results in high efficiency.     

On multiprocessor task scheduling using efficient state space search approaches

Obtaining an optimal schedule for a set of precedence-constrained tasks is a well-known NP-complete problem in its general form. In view of the intractability of the problem, most of the previous work relies on heuristics that try to find reasonably high quality solutions in an acceptable amount of time. While optimal polynomial-time algorithms are known only for a few simple cases (and in other cases can only be obtained through an exhaustive search with prohibitively high time complexity), they may be critically important for applications in which performance is the prime objective. Optimal solutions can also serve as a reference to test the performance of various heuristics. Moreover, an optimal schedule for a program at hand needs to be determined only once (and off-line) but the program using that schedule is in general executed several times. In this paper, we propose optimal algorithms for static scheduling of task graphs with arbitrary parameters to multiple homogeneous processors. The first algorithm is based on the A^* search technique and uses a computationally efficient cost function for guiding the search with reduced complexity. Additionally, we propose a number of effective state-pruning techniques to reduce the search space. For further lowering the complexity, we propose an efficient parallelization of the search algorithm. We parallelize the algorithm with reduced interprocessor communication as well as with static and dynamic load-balancing schemes to evenly distribute the search states to the processors. We also propose an approximate algorithm that guarantees a bounded deviation from the optimal solution but executes in a considerably shorter time. Based on an extensive experimental evaluation of the algorithms, we conclude that the parallel algorithm with pruning techniques is an efficient scheme for generating optimal solutions of reasonably large problems while the approximate algorithm is effective if slightly degraded solutions are acceptable.     

MPEG-4 Video Transmission over Wireless Networks: A Link Level Performance Study

With the scalability and flexibility of the MPEG-4 and the emergence of the broadband wireless network, wireless multimedia services are foreseen to become deployed in the near future. Transporting MPEG-4 video over the broadband wireless network is expected to be an important component of many emerging multimedia applications. One of the critical issues for multimedia applications is to ensure that the quality-of-service (QoS) requirement to be maintained at an acceptable level. This is further challenged in that such a service guarantee must be achieved under unreliable and time-varying wireless channels. In this paper we study the link level performance of MPEG-4 video transmission over the uplink of an unreliable wireless channel. We introduce the discrete time batch Markovian arrival process (DBMAP) with two types of arrivals to model the MPEG-4 video source, which takes into account the inherent nature of the adaptiveness of the video traffic. We prove that in a hidden Markov modeled (HMM) wireless channel with probabilistic transmission, the service time for an arbitrary radio link control (RLC) burst follows phase type (PH-type) distribution. We show that the link level performance of a wireless video transmission system can be modeled by a DBMAP/PH/1 priority queue, and present computation algorithm and numerical results for the queueing model. Extensive simulations are carried out on the queueing behavior of the video transmission buffer, as well as on the packet level error behavior of the video data. The results demonstrate that video quality can be substantially improved by preserving the high priority video data during the transmission.     

Coordination of PV Inverters and Voltage Regulators Considering Generation Correlation and Voltage Quality Constraints for Loss Minimization

Abstract

Distributed generation sources (DGs) are widely considered as important sources of power generation in distribution systems during the last few decades. Despite the substantial benefits of DGs, increasing the penetration level of the DGs can cause dramatic voltage magnitude fluctuations. Coordination of the use of dynamic reactive power sources such as photovoltaic (PV) inverters and voltage control equipment can mitigate rapid voltage magnitude fluctuations. A coordinated volt-var control method is proposed herein to achieve the optimal expected performance (e.g., system losses) while considering the spatial correlation among PV source powers and constraining the variability of voltage magnitudes throughout the distribution network within permissible ranges. The proposed strategy formulates chance constraints on the voltage magnitude and considers the uncertainty of PV power injections over the interval of interest to maintain voltage magnitudes within acceptable limits. The proposed method has been tested on the IEEE 123-node radial distribution system for validation. Moreover, the simulation results demonstrate that the proposed method can effectively mitigate the fast voltage magnitude deviations with an acceptable reduction in system losses in the presence of intermittent renewable resources.     

An overview on marine anti-allergic active substances for alleviating food-induced allergy

Abstract

Food provides energy and various nutrients and is the most important substance for the survival of living beings. However, for allergic people, certain foods cause strong reactions, and sometimes even cause shock or death. Food allergy has been recognized by the World Health Organization (WHO) as a major global food safety issue which affect the quality of life of nearly 5% of adults and 8% of children, and the incidence continues to rise but there is no effective cure. Drug alleviation methods for food allergies often have shortcomings such as side effects, poor safety, and high cost. At present, domestic and foreign scientists have turned to research and develop various new, safe and efficient natural sources of hypoallergenic or anti-allergic drugs or foods. There are many kinds of anti-allergic substances obtained from the plants and animals have been reported. Besides, probiotics and bifidobacteria also have certain anti-allergic effects. Of all the sources of anti-allergic substances, the ocean is rich in effective active substances due to its remarkable biodiversity and extremely complex living environment, and plays a huge role in the field of anti-food allergy. In this paper, the anti-food allergic bioactive substances isolated from marine organisms encompassing marine microbial, plant, animal sources and their mechanism were reviewed and the possible targets of anti-allergic substances exerting effects are illustrated by drawing. In addition, the development prospects of marine anti-allergic market are discussed and forecasted, which can provide reference for future research on anti-allergic substances.