An improved Urdu stemming algorithm for text mining based on multi-step hybrid approach

Stemming is the basic operation in Natural language processing (NLP) to remove derivational and inflectional affixes without performing a morphological analysis. This practice is essential to extract the root or stem. In NLP domains, the stemmer is used to improve the process of information retrieval (IR), text classifications (TC), text mining (TM) and related applications. In particular, Urdu stemmers utilize only uni-gram words from the input text by ignoring bigrams, trigrams, and n-gram words. To improve the process and efficiency of stemming, bigrams and trigram words must be included. Despite this fact, there are a few developed methods for Urdu stemmers in the past studies. Therefore, in this paper, we proposed an improved Urdu stemmer, using hybrid approach divided into multi-step operation, to deal with unigram, bigram, and trigram features as well. To evaluate the proposed Urdu stemming method, we have used two corpora; word corpus and text corpus. Moreover, two different evaluation metrics have been applied to measure the performance of the proposed algorithm. The proposed algorithm achieved an accuracy of 92.97% and compression rate of 55%. These experimental results indicate that the proposed system can be used to increase the effectiveness and efficiency of the Urdu stemmer for better information retrieval and text mining applications.     

Accurate and energy-efficient boundary detection of continuous objects in duty-cycled wireless sensor networks

With the development and wide adoption of industrial wireless sensor networks to support various domain applications, the boundary detection of continuous objects has become an important research challenge, where improving the accuracy of boundary area while reducing the energy consumption are the first-class citizens to be considered. To address this research challenge, this article proposes a two-stage boundary face detection mechanism, where sensor nodes are duty-cycled and to be deployed in a dense fashion. When the occurrence of potential events are recognized using the initially activated sensor nodes, the boundary faces of continuous objects are constructed through adopting planarization algorithms. Thereafter, sensor nodes contained in certain boundary faces are examined, where their sensory data are estimated using spatial interpolation methods. Certain sensor nodes are selected to be woken up, only when their sensory data suggest that they should be more appropriate candidates of boundary sensor nodes. Consequently, the size of boundary faces is reduced, and this coarse-to-fine refinement procedure iterates, until all sensor nodes contained in the boundary faces have been examined. Experimental evaluation result shows that the boundary area can be refined significantly and be more precise, where the half of the initial boundary face area should be reduced in most situations.     

New look on relay selection strategies for full-duplex multiple-relay NOMA over Nakagami-m fading channels

Wireless Networks - By removing the orthogonal use of radio-resources, non-orthogonal multiple access (NOMA) has been introduced to improve the spectral efficiency of fifth generation (5G) and...     

A comprehensive survey of AI-enabled phishing attacks detection techniques

In recent times, a phishing attack has become one of the most prominent attacks faced by internet users, governments, and service-providing organizations. In a phishing attack, the attacker(s) collects the client’s sensitive data (i.e., user account login details, credit/debit card numbers, etc.) by using spoofed emails or fake websites. Phishing websites are common entry points of online social engineering attacks, including numerous frauds on the websites. In such types of attacks, the attacker(s) create website pages by copying the behavior of legitimate websites and sends URL(s) to the targeted victims through spam messages, texts, or social networking. To provide a thorough understanding of phishing attack(s), this paper provides a literature review of Artificial Intelligence (AI) techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection. This paper also presents the comparison of different studies detecting the phishing attack for each AI technique and examines the qualities and shortcomings of these methodologies. Furthermore, this paper provides a comprehensive set of current challenges of phishing attacks and future research direction in this domain.

     

Biomimetic Nanomaterial Strategies for Virus Targeting: Antiviral Therapies and Vaccines

The ongoing coronavirus disease 2019 (COVID-19) pandemic highlights the importance of developing effective virus targeting strategies to treat and prevent viral infections. Since virus particles are nanoscale entities, nanomaterial design strategies are ideally suited to create advanced materials that can interact with and mimic virus particles. In this progress report, the latest advances in biomimetic nanomaterials are critically discussed for combating viral infections, including in the areas of nanomaterial-enhanced viral replication inhibitors, biomimetic virus particle capture schemes, and nanoparticle vaccines. Particular focus is placed on nanomaterial design concepts and material innovations that can be readily developed to thwart future viral threats. Pertinent nanomaterial examples from the COVID-19 situation are also covered along with discussion of human clinical trial efforts underway that might lead to next-generation antiviral therapies and vaccines.     

Measurement of Onset of Structural Relaxation in Melt-Quenched Phase Change Materials

Chalcogenide phase change materials enable non-volatile, low-latency storage-class memory. They are also being explored for new forms of computing such as neuromorphic and in-memory computing. A key challenge, however, is the temporal drift in the electrical resistance of the amorphous states that encode data. Drift, caused by the spontaneous structural relaxation of the newly recreated melt-quenched amorphous phase, has consistently been observed to have a logarithmic dependence in time. Here, it is shown that this observation is valid only in a certain observable timescale. Using threshold-switching voltage as the measured variable, based on temperature-dependent and short timescale electrical characterization, the onset of drift is experimentally measured. This additional feature of the structural relaxation dynamics serves as a new benchmark to appraise the different classical models to explain drift.     

Sustainable Green Fog Computing for Smart Agriculture

Wireless Personal Communications - Medical Body Area Networks or MBANs are gaining popularity in healthcare circles because of the convenience they provide to patients and caregivers and assist in...     

Short term solar insolation prediction: P-ELM approach

Abstract

The accurate forecasting of solar irradiance with hybrid machine learning algorithm is presented in this paper. A novel Persistence-Extreme Learning Machine (P-ELM) algorithm is used for training of the system. The Clearness Index (CI) value of the 22 districts of Andhra Pradesh (India) is calculated and out of which four areas are identified with highest CI values. The Global Horizontal Irradiance (GHI) is forecasted for the selected areas with different weather conditions such as winter, summer and rainfall seasons using a P-ELM algorithm. The input parameters are Temperature, Diffuse horizontal irradiance, pressure and past GHI and GHI for the next instant as the output is considered. The real time data is obtained for every one hour interval for a period of one month. The performance of the P-ELM algorithm is evaluated in terms of Mean Absolute Error and Root Mean Square Error. From the obtained results, it is observed that P-ELM algorithm offers better performance over the fundamental P-ELMs. The P-ELM algorithm gives good forecasting accuracy with minimum simulation time. The simulation of P-ELM algorithm is carried out using MATLAB 2013a environment. The P-ELM algorithm is very much beneficial for accurate and reliable real time solar forecasting.     

Forecasting wheat yield from weather data and MODIS NDVI using Random Forests for Punjab province,Pakistan

Wheat is the staple food of Punjab province of Pakistan, which contributes more than 75% of the total national production. Accurate and timely forecasting of wheat yield is a cornerstone for monitoring food security and planning for agricultural markets, but the efficiency of the current system for near real-time forecasting should be improved. In this research paper, we developed a model to forecast wheat yield before harvest for each of eight individual districts and for Punjab province as a whole. The model uses weather and Moderate Resolution Imaging Spectroradiometer (MODIS)-derived normalized difference vegetation index (NDVI) data for 2001–2014 (14 years) to calculate Random Forest (RF) statistical models using 15 independent variables. Temperature, rainfall, sunshine hours, growing degree days, and MODIS-derived NDVI for each of the eight districts of Punjab province were used to forecast yield for the year 2014. The same independent variables were used to forecast wheat yield of the whole Punjab from 2001 to 2014 by excluding the respective year from training set. Sunshine hour data were not available for all districts and therefore we tested using temperature data and average latitude-based solar radiation as surrogates. The root mean square errors (RMSEs) of the forecast results of the whole of Punjab province were 147.7 kg ha^?1 and 148.7 kg ha^?1 with a mean error of less than 5% using average and generic RFs, respectively. Forecasts for individual districts showed R² of 0.95 with RMSE of 175.6 kg ha^?1 and 5.86% mean error.     

Constructive Visual Analytics for Text Similarity Detection

Detecting similarity between texts is a frequently encountered text mining task. Because the measurement of similarity is typically composed of a number of metrics, and some measures are sensitive to subjective interpretation, a generic detector obtained using machine learning often has difficulties balancing the roles of different metrics according to the semantic context exhibited in a specific collection of texts. In order to facilitate human interaction in a visual analytics process for text similarity detection, we first map the problem of pairwise sequence comparison to that of image processing, allowing patterns of similarity to be visualized as a 2D pixelmap. We then devise a visual interface to enable users to construct and experiment with different detectors using primitive metrics, in a way similar to constructing an image processing pipeline. We deployed this new approach for the identification of commonplaces in 18th‐century literary and print culture. Domain experts were then able to make use of the prototype system to derive new scholarly discoveries and generate new hypotheses.