Stability of the soil slopes is one of the most challenging issues in civil engineering projects. Due to the complexity and non-linearity of this threat, utilizing simple predictive models does not satisfy the required accuracy in analysing the stability of the slopes. Hence, the main objective of this study is to introduce a novel metaheuristic optimization namely Harris hawks’ optimization (HHO) for enhancing the accuracy of the conventional multilayer perceptron technique in predicting the factor of safety in the presence of rigid foundations. In this way, four slope stability conditioning factors, namely slope angle, the position of the rigid foundation, the strength of the soil, and applied surcharge are considered. Remarkably, the main contribution of this algorithm to the problem of slope stability lies in adjusting the computational weights of these conditioning factors. The results showed that using the HHO increases the prediction accuracy of the ANN for analysing slopes with unseen conditions. In this regard, it led to reducing the root mean square error and mean absolute error criteria by 20.47% and 26.97%, respectively. Moreover, the correlation between the actual values of the safety factor and the outputs of the HHO–ANN (R2 = 0.9253) was more significant than the ANN (R2 = 0.8220). Finally, an HHO-based predictive formula is also presented to be used for similar applications.

A novel Harris hawks’ optimization and k-fold cross-validation predicting slope stability

https://nottingham-repository.worktribe.com/preview/948614/ASC2018.pdf

Hybrid multi-objective evolutionary algorithms based on decomposition for wireless sensor network coverage optimization

Nature-inspired hybrid techniques of IWO, DA, ES, GA, and ICA, validated through a k-fold validation process predicting monthly natural gas consumption

Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms

Load forecasting is a vital part of smart grids for predicting the required electrical power using artificial intelligence (AI). Deep learning is broadly used for load forecasting in the smart grid using the artificial neural network (ANN). Generally, computing the deep learning in the smart grid requires massive data aggregation or centralization and significant computational time. This paper presents a survey of deep learning-based load forecasting techniques from 2015 to 2020. This survey discusses the studies based on their deep learning techniques, Distributed Deep Learning (DDL) techniques, Back Propagation (BP) based works, and non-BP based works in the load forecasting process. Consequent to the survey, it was determined that data aggregation dependency would be beneficial for reducing computational time in load forecasting. Therefore, a conceptual model of DDL for smart grids has been presented, where the HSIC (Hilbert-Schmidt Independence Criterion) Bottleneck technique has been incorporated to provide higher accuracy.

/pdf/hsic-bottleneck-based-distributed-deep-learning-model-for-2dc43gdryl.pdf

HSIC Bottleneck Based Distributed Deep Learning Model for Load Forecasting in Smart Grid With a Comprehensive Survey

/pdf/a-location-based-time-and-attendance-system-2ucwsxza36.pdf

A Location Based Time and Attendance System

Automatic recognition systems are generally applied successfully in speech processing to categorize observed utterances by the speaker identity, dialect and linguistic communication. A lot of research has been performed to detect speeches, dialects and languages of different region throughout the world. But the work on dialects of Bangladesh is infrequent to our research. These dialects, in turn, differ quite a bit from each other. In this paper, we present a method to detect Bangladeshi different dialects which utilizes Mel Frequency Cepstral Coefficient (MFCC), its Delta and Delta-delta as main features and Gaussian Mixture Models (GMM) to classify characteristics of a specific dialect. Particularly we extract the MFCCs, Deltas and Delta-deltas from the speech signal. Then they are merged together to form a feature vector for a specific dialect. GMM is trained using the iterative Expectation Maximization (EM) algorithm where feature vectors are served as input. This scheme is tested on 5 databases of 30 speech samples each. Speech samples contain dialects of Borishal, Noakhali, Sylhet, Chittagong and Chapai Nawabganj regions of Bangladesh. Experiments show that GMM adaptation gives comparable good performance.

Bangladeshi dialect recognition using Mel Frequency Cepstral Coefficient, Delta, Delta-delta and Gaussian Mixture Model

A model of an automated temperature prediction on smart AC system for a room has been designed, developed and implemented with an embedded system. In a room, temperature of object (like human being) with the environment is detected, identified and analyzed, with an ideal temperature. Based on data, a mathematical formula can be derived and an algorithm has been formed by using the mathematical formula of the predicted temperature data and the values of the two sensors, where sensors are used for object temperature detection and the AC perform automatically turned on or turned off. Python programming language with its default library has been used to code for the successful implementation of the algorithm. This proposed embedded system can be implemented in any smart AC room where anyone can utilize the AC system automatically switched on/off with the predicted temperature. Exploit this embedded system in all over the places including for disabled peoples, personal room, conference room, hall room, classroom and transports, where manually control of Air conditioner is not feasible.

A Smart Embedded System Model for the AC Automation with Temperature Prediction

Universal Networking Language (UNL) is a declarative formal language that is used to represent semantic data extracted from natural language texts. This paper presents a novel approach to converting Bangla natural language text into UNL using a method known as Predicate Preserving Parser (PPP) technique. PPP performs morphological, syntactic and semantic, and lexical analysis of text synchronously. This analysis produces a semantic-net like structure represented using UNL. We demonstrate how Bangla texts are analyzed following the PPP technique to produce UNL documents which can then be translated into any other suitable natural language facilitating the opportunity to develop a universal language translation method via UNL.

/pdf/unl-based-bangla-natural-text-conversion-predicate-2ospea3p6u.pdf

UNL Based Bangla Natural Text Conversion - Predicate Preserving Parser Approach

The continued growth of both mobile appliance and wireless Internet technologies is bringing a new telecommunication revolution and has extended the demand of various services with mobile appliance. However, during working with wireless access devices, users have a limited amount of storage available to them due to their limited size and weight. To relieve this problem iSCSI (Internet Small Computer Interface) remote storage system would be one solution but the question is high availability and performance. In this paper, we propose a new approach of Multi-Connection in one session based remote storage system for mobile appliance with error recovery method that avoids drastic reduction of transmission rate from TCP congestion control in wireless environment as compared to traditional iSCSI.

Shaikh Muhammad Allayear

Papers

A Location Based Time and Attendance System

Bangladeshi dialect recognition using Mel Frequency Cepstral Coefficient, Delta, Delta-delta and Gaussian Mixture Model

A Smart Embedded System Model for the AC Automation with Temperature Prediction

UNL Based Bangla Natural Text Conversion - Predicate Preserving Parser Approach

ISCSI Multi-connection and Error Recovery Method for Remote Storage System in Mobile Appliance