A deep learning method for automatic SMS spam classification: Performance of learning algorithms on indigenous dataset

SMS, one of the most popular and fast-growing GSM value-added services worldwide, has attracted unwanted SMS, also known as SMS spam. The effects of SMS spam are significant as it affects both the users and the service providers, causing a massive gap in trust among both parties. This article presents a deep learning model based on BiLSTM. Further, it compares our results with some of the states of the art machine learning (ML) algorithm on two datasets: our newly collected dataset and the popular UCI SMS dataset. This study aims to evaluate the performance of diverse learning models and compare the result of the new dataset expanded (ExAIS_SMS) using the following metrics the true positive (TP), false positive (FP), F-measure, recall, precision, and overall accuracy. The average accuracy for the BiLSTSM model achieved moderately improved results compared to some of the ML classifiers. The experimental results achieved significant improvement from the ground truth results after effective fine-tuning of some of the parameters. The BiLSTM model using the ExAIS_SMS dataset attained an accuracy of 93.4% and 98.6% for UCI datasets. Further comparison of the two datasets on the state-of-the-art ML classifiers gave an accuracy of Naive Bayes, BayesNet, SOM, decision tree, C4.5, J48 is 89.64%, 91.11%, 88.24%, 75.76%, 80.24%, and 79.2% respectively for ExAIS_SMS datasets. In conclusion, our proposed BiLSTM model showed significant improvement over traditional ML classifiers. To further validate the robustness of our model, we applied the UCI datasets, and our results showed optimal performance while classifying SMS spam messages based on some metrics: accuracy, precision, recall, and F-measure.publishedVersio

Study of the Yahoo-Yahoo Hash-Tag Tweets Using Sentiment Analysis and Opinion Mining Algorithms

Mining opinion on social media microblogs presents opportunities to extract meaningful insight from the public from trending issues like the “yahoo-yahoo” which in Nigeria, is synonymous to cybercrime. In this study, content analysis of selected historical tweets from “yahoo-yahoo” hash-tag was conducted for sentiment and topic modelling. A corpus of 5500 tweets was obtained and pre-processed using a pre-trained tweet tokenizer while Valence Aware Dictionary for Sentiment Reasoning (VADER), Liu Hu method, Latent Dirichlet Allocation (LDA), Latent Semantic Indexing (LSI) and Multidimensional Scaling (MDS) graphs were used for sentiment analysis, topic modelling and topic visualization. Results showed the corpus had 173 unique tweet clusters, 5327 duplicates tweets and a frequency of 9555 for “yahoo”. Further validation using the mean sentiment scores of ten volunteers returned R and R2 of 0.8038 and 0.6402; 0.5994 and 0.3463; 0.5999 and 0.3586 for Human and VADER; Human and Liu Hu; Liu Hu and VADER sentiment scores, respectively. While VADER outperforms Liu Hu in sentiment analysis, LDA and LSI returned similar results in the topic modelling. The study confirms VADER’s performance on unstructured social media data containing non-English slangs, conjunctions, emoticons, etc. and proved that emojis are more representative of sentiments in tweets than the texts.publishedVersio

Malignant skin melanoma detection using image augmentation by oversampling in nonlinear lower-dimensional embedding manifold

The continuous rise in skin cancer cases, especially in malignant melanoma, has resulted in a high mortality rate of the affected patients due to late detection. Some challenges affecting the success of skin cancer detection include small datasets or data scarcity problem, noisy data, imbalanced data, inconsistency in image sizes and resolutions, unavailability of data, reliability of labeled data (ground truth), and imbalance of skin cancer datasets. This study presents a novel data augmentation technique based on covariant Synthetic Minority Oversampling Technique (SMOTE) to address the data scarcity and class imbalance problem. We propose an improved data augmentation model for effective detection of melanoma skin cancer. Our method is based on data oversampling in a nonlinear lower-dimensional embedding manifold for creating synthetic melanoma images. The proposed data augmentation technique is used to generate a new skin melanoma dataset using dermoscopic images from the publicly available P H2 dataset. The augmented images were used to train the SqueezeNet deep learning model. The experimental results in binary classification scenario show a significant improvement in detection of melanoma with respect to accuracy (92.18%), sensitivity (80.77%), specificity (95.1%), and F1-score (80.84%). We also improved the multiclass classification results in melanoma detection to 89.2% (sensitivity), 96.2% (specificity) for atypical nevus detection, 65.4% (sensitivity), 72.2% (specificity), and for common nevus detection 66% (sensitivity), 77.2% (specificity). The proposed classification framework outperforms some of the state-of-the-art methods in detecting skin melanoma.publishedVersio

VOIP vs GSM Technology: The Way of the Future for Communication

This chopter presents ~·(;/?as o disruptive lechnology to GSM technnfog_1· as \l'c/1 os the issues. controFersies, and problems surmunding its deployment. It gin•s o gent'ral introduction oftlze evolution o/ communication svsten1s fim11 thE! POTS. /u (}SAf, and IWW ~'(J/P Se1•ur~.d issues that swTound the deployment of Voir such tiS [Jrovision of PSTN equivalent sen ices hy Voir scf'l'i<:c pml'iclers. regulation ofthe service. introduction (~{latency and other counter measures bv some operators. threat posed io PSTN providers due to eme1gence ol T-iJ!P. the needfin· technical stundardi:::ation of Vu!P. securill' issues, different cost structure, and cjuality ofservh:e pruvided H'<:'re alsu discussed in details Solutions and recommendations wei·e suggested to overcome rheclwllenges outlined. Tli)[P is eri!sented us the trul· o/the.fillurcj(n' communication T1'hen thisfinallv happens depends 011 how .fits! I he choll<'llges outlined in this chapter are addressed Fulurc and ell/crging re.\'Curdltrends in the dep/ovmenl of Vn!P such cts locating users in a st!cure a11d reliahle wuy, monitoring IIJ!P 11Cllt'ork.1·. as ll;elf us inrrusion derecriun and prevention on SIP tt•ere also considered. alter whic/1. conc/usiun ti'US made This dWjJ/er is hnth informative and interestin

Dual-Sim Phones· A Disruptive Technology?

/)uul-.\'1;\f mnhile pllfmes U!ili::e lr:chnologv thut;Jennits the use olt>l'O SlAts ur u lime The leclmulogJ· pamits simultaneous uccess to !hi! muhile ne111:ork Sr.!rvices. Its di.ITIIJilil•e nuture is with re(erencc· !o the mohile phone market in Nigeria and other parts o/lhe Jrorld Earlier murket trend was inclinution to "nnver" and "better .. phones. inj{tvour ofestahlished sin;;;le-SJAJnwhile phone monll/ucturcr:. like Nokia and Samsung: lntroduclion ~~ldual-S"'-1 pizones mainlv manuj(JL'!llred bv Chinese mohile rhone manulacturingfirms propelled userprejerenceforphones acquis.irionl,rhich permits dual and simuitwwous access to mohi/e m:flFork. This technologv has compelled its culoption hy estuh/isiJCd monufucwring names in order thutthc1· 1110)' remoin competitive. it is a c/eur case ofa disrupt ire technology, and this clwplerlocuses on it need. cftixts. uml disrupt in: natur

Facial Image Verification and Quality Assessment System -FaceIVQA

Although several techniques have been proposed for predicting biometric system performance using quality values, many of the research works were based on no-reference assessment technique using a single quality attribute measured directly from the data. These techniques have proved to be inappropriate for facial verification scenarios and inefficient because no single quality attribute can sufficient measure the quality of a facial image. In this research work, a facial image verification and quality assessment framework (FaceIVQA) was developed. Different algorithms and methods were implemented in FaceIVQA to extract the faceness, pose, illumination, contrast and similarity quality attributes using an objective full-reference image quality assessment approach. Structured image verification experiments were conducted on the surveillance camera (SCface) database to collect individual quality scores and algorithm matching scores from FaceIVQA using three recognition algorithms namely principal component analysis (PCA), linear discriminant analysis (LDA) and a commercial recognition SDK. FaceIVQA produced accurate and consistent facial image assessment data. The Result shows that it accurately assigns quality scores to probe image samples. The resulting quality score can be assigned to images captured for enrolment or recognition and can be used as an input to quality-driven biometric fusion systems.DOI:http://dx.doi.org/10.11591/ijece.v3i6.503

GENDER AND PARENTS’ EDUCATIONAL QUALIFICATIONS ON ACHIEVEMENT MOTIVATION OF COVENANT UNIVERSITY

Achievement motivation level among university students varies. Some are highly motivated and achieve success while others are lowly motivated and experience little success. This study examined the influence of gender and parents’ educational qualifications on achievement motivation level of Covenant University students. The scope was limited to undergraduate students of the four Colleges in Covenant University, namely - College of Business and Social Sciences, Engineering, Science and Technology, and Leadership Development Studies. To achieve the objective of this study, two research questions and four hypotheses were raised and formulated respectively to guide the investigation of the study. The sample of the study consisted of three hundred (300) students comprising 206 males and 94 females randomly selected. Questionnaire forms were used for data collection. The study made use of the ex-post factor method which consists of survey and descriptive designs. Results show that female students’ achievement motivation level is stronger than that of the males at 1% level of significance (0.626). There is no significant correlation between father’s highest educational qualification and students’ achievement motivation level for both males and females at (0.064). However, there was a significant relationship between mother’s highest educational qualification and students’ achievement motivation level. (0.105). Based on the findings, it was recommended that male students should be given the same level of attention as the females by parents. In addition, the university should introduce sustainable mentorship programmes with faculty as role models to motivate the male students

Mel-Frequency Cepstral Coefficients and Convolutional Neural Network for Genre Classification of Indigenous Nigerian Music

Music genre classification is a field of study within the broader domain of Music Information Retrieval (MIR) that is still an open problem. This study aims at classifying music by Nigerian artists into respective genres using Convolutional Neural Networks (CNNs) and audio features extracted from the songs. To achieve this, a dataset of 524 Nigerian songs was collected from different genres. Each downloaded music file was converted from standard MP3 to WAV format and then trimmed to 30 seconds. The Librosa sc library was used for the analysis, visualization and further pre-processing of the music file which includes converting the audio signals to Mel-frequency cepstral coefficients (MFCCs). The MFCCs were obtained by taking performing a Discrete Cosine Transform on the logarithm of the Mel-scale filtered power spectrum of the audio signals. CNN architecture with multiple convolutional and pooling layers was used to learn the relevant features and classify the genres. Six models were trained using a categorical cross-entropy loss function with different learning rates and optimizers. Performance of the models was evaluated using accuracy, precision, recall, and F1-score. The models returned varying results from the classification experiments but model 3 which was trained with an Adagrad optimizer and learning rate of 0.01 had accuracy and recall of 75.1% and 84%, respectively. The results from the study demonstrated the effectiveness of MFCC and CNNs in music genre classification particularly with indigenous Nigerian artists

BiLSTM with data augmentation using interpolation methods to improve early detection of Parkinson disease

Serija: Annals of computer science and information systems, vol. 21The lack of dopamine in the human brain is the cause of Parkinson disease (PD) which is a degenerative disorder common globally to older citizens. However, late detection of this disease before the first clinical diagnosis has led to increased mortality rate. Research effort towards the early detection of PD has encountered challenges such as: small dataset size, class imbalance, overfitting, high false detection rate, model complexity, etc. This paper aims to improve early detection of PD using machine learning through data augmentation for very small datasets. We propose using Spline interpolation and Piecewise Cubic Hermite Interpolating Polynomial (Pchip) interpolation methods to generate synthetic data instances. We further investigate on reducing dimensionality of features for effective and real-time classification while considering computational complexity of implementation on real-life mobile phones. For classification we use Bidirectional LSTM (BiLSTM) deep learning network and compare the results with traditional machine learning algorithms like Support Vector Machine (SVM), Decision Tree, Logistic regression, KNN and Ensemble bagged tree. For experimental validation we use the Oxford Parkinson disease dataset with 195 data samples, which we have augmented with 571 synthetic data samples. The results for BiLSTM shows that even with a holdout of 90%, the model was still able to effectively recognize PD with an average accuracy for ten rounds experiment using 22 features as 82.86%, 97.1%, and 96.37% for original, augmented (Spline) and augmented (Pchip) datasets, respectively. Our results show that proposed data augmentation schemes have significantly (p < 0.001) improved the accuracy of PD recognition on a small dataset using both classical machine learning models and BiLSTMInformatikos fakultetasKauno technologijos universitetasVytauto Didžiojo universiteta