Self-sampled image resolution enhancement using dual-tree complex wavelet transform

European Signal Processing Conference2017-202

Solar-powered automated road surveillance system for speed violation detection

10.1109/TIE.2009.2038395IEEE Transactions on Industrial Electronics5793216-3227ITIE

Self-sampled image resolution enhancement using dual-tree complex wavelet transform

European Signal Processing Conference2017-202

ERCP : Energy-Efficient and Reliable-Aware Clustering Protocol for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been around for over a decade and have been used in many important applications. Energy and reliability are two of the major problems with these kinds of applications. Reliable data delivery is an important issue in WSNs because it is a key part of how well data are sent. At the same time, energy consumption in battery-based sensors is another challenge. Therefore, efficient clustering and routing are techniques that can be used to save sensors energy and guarantee reliable message delivery. With this in mind, this paper develops an energy-efficient and reliable clustering protocol (ERCP) for WSNs. First, an efficient clustering technique is proposed for sensor nodes’ energy savings considering different clustering parameters, including the link quality metric, the energy, the distance to neighbors, the distance to the sink node, and the cluster load metric. The proposed routing protocol works based on the concept of a reliable inter-cluster routing technique that saves energy. The routing decisions are made based on different parameters, such as the energy balance metric, the distance to the sink node, and the wireless link quality. Many experiments and analyses are examined to determine how well the ERCP performs. The experiment results showed that the ECRP protocol performs much better than some of the recent algorithms in both homogeneous and heterogeneous networks. © 2022 by the authors.open access</p

Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning

Breast cancer is one of the most common invading cancers in women. Analyzing breast cancer is nontrivial and may lead to disagreements among experts. Although deep learning methods achieved an excellent performance in classification tasks including breast cancer histopathological images, the existing state-of-the-art methods are computationally expensive and may overfit due to extracting features from in-distribution images. In this paper, our contribution is mainly twofold. First, we perform a short survey on deep-learning-based models for classifying histopathological images to investigate the most popular and optimized training-testing ratios. Our findings reveal that the most popular training-testing ratio for histopathological image classification is 70%: 30%, whereas the best performance (e.g., accuracy) is achieved by using the training-testing ratio of 80%: 20% on an identical dataset. Second, we propose a method named DenTnet to classify breast cancer histopathological images chiefly. DenTnet utilizes the principle of transfer learning to solve the problem of extracting features from the same distribution using DenseNet as a backbone model. The proposed DenTnet method is shown to be superior in comparison to a number of leading deep learning methods in terms of detection accuracy (up to 99.28% on BreaKHis dataset deeming training-testing ratio of 80%: 20%) with good generalization ability and computational speed. The limitation of existing methods including the requirement of high computation and utilization of the same feature distribution is mitigated by dint of the DenTnet. © 2022 Musa Adamu Wakili et al.open access</p

A Hybrid Approach for the Sentiment Analysis of Turkish Twitter Data

Social media is now playing an important role in influencing people’s sentiments. It also helps analyze how people, particularly consumers, feel about a particular topic, product or an idea. One of the recent social media platforms that people use to express their thoughts is Twitter. Due to the fact that Turkish is an agglutinative language, its complexity makes it difficult for people to perform sentiment analysis. In this study, a sum of 13K Turkish tweets has been collected from Twitter using the Twitter API and their sentiments are being analyzed using machine learning classifiers. Random forests and support vector machines are the two kinds of classifiers that are adopted. Preprocessing methods were applied on the obtained data to remove links, numbers, punctuations and un-meaningful characters. After the preprocessing phase, unsuitable data have been removed and 10,500 out of the 13K downloaded dataset are taken as the main dataset. The datasets are classified to be either positive, negative or neutral based on their contents. The main dataset was converted to a stemmed dataset by removing stopwords, applying tokenization and also applying stemming on the dataset, respectively. A portion of 3,000 and 10,500 of the stemmed data with equal distribution from each class has been identified as the first dataset and second dataset to be used in the testing phase. Experimental results have shown that while support vector machines perform better when it comes to classifying negative and neutral stemmed data, random forests algorithm perform better in classifying positive stemmed data and thus a hybrid approach which consists of the hierarchical combination of random forest and support vector machines has also been developed and used to find the result of the data. Finally, the applied methodologies have been tested on both the first and the second dataset. It has been observed that while both support vector machines and random forest algorithms could not achieve an accuracy of up to 77% on the first and 72% on the second dataset, the developed hybrid approach achieve an accuracy of up to 86.4% and 82.8% on the first and second dataset, respectively. © 2020, Springer Nature Switzerland AG