Convolutional Neural Network (CNN) models are a type of deep learning architecture introduced to achieve the correct classification of breast cancer. This paper has a two-fold purpose. The first aim is to investigate the various deep learning models in classifying breast cancer histopathology images. This study identified the most accurate models in terms of the binary, four, and eight classifications of breast cancer histopathology image databases. The different accuracy scores obtained for the deep learning models on the same database showed that other factors such as pre-processing, data augmentation, and transfer learning methods can impact the ability of the models to achieve higher accuracy. The second purpose of our manuscript is to investigate the latest models that have no or limited examination done in previous studies. The models like ResNeXt, Dual Path Net, SENet, and NASNet had been identified with the most cutting-edge results for the ImageNet database. These models were examined for the binary, and eight classifications on BreakHis, a breast cancer histopathology image database. Furthermore, the BACH database was used to investigate these models for four classifications. Then, these models were compared with the previous studies to find and propose the most state-of-the-art models for each classification. Since the Inception-ResNet-V2 architecture achieved the best results for binary and eight classifications, we have examined this model in our study as well to provide a better comparison result. In short, this paper provides an extensive evaluation and discussion about the experimental settings for each study that had been conducted on the breast cancer histopathology images.

Water pipeline leakage detection is still an important issue, particularly for the development of smart cities. Thus, this paper reviews water pipeline leakage detection techniques, which can be classified into three different categories, namely, software-based, hardware-based, and conventional methods. We compare the advantages and disadvantages for all the methods in the groups and thoroughly discuss the hardware-based method, which is our focus. Specifications on water pipeline testbeds used in the previous works are also highlighted. Since many recent techniques are based on accelerometer or vibration sensors, a comparative study that includes the cost and accuracy in identifying the pipeline leaks is presented. The theoretical computation of the vibration induced from our water pipeline testbed is also demonstrated and compared with the actual vibration data collected from the experimental works using three different sensors, namely, MPU6050, MMA7361, and ADXL335.

With the emergence of Internet-of-Things (IoT) technologies, the application of Wireless Sensor Network (WSN) has become pervasive in our physical environment. These technologies rely on the WSN to capture environmental data and transmit it through the network to the specified location or storage. Given the massive data that captured by the WSN and the data transmission that happen in the process, there is a challenge for any developers and researchers in WSN in terms of a continuous operation of the WSN device. This is because WSNs are battery-powered and often deployed in a remote location that is hard to access for a battery replacement. The purpose of this paper is to survey the energy harvesting for improving the lifetime of the wireless sensor networks powered by the environmental energy. Energy harvesting takes advantages of the stray energy from the environment as the sustainable source of power.

The Global Positioning System demonstrates the significance of Location Based Services but it cannot be used indoors due to the lack of line of sight between satellites and receivers. Indoor Positioning Systems are needed to provide indoor Location Based Services. Wireless LAN fingerprints are one of the best choices for Indoor Positioning Systems because of their low cost, and high accuracy, however they have many drawbacks: creating radio maps is time consuming, the radio maps will become outdated with any environmental change, different mobile devices read the received signal strength (RSS) differently, and peoples’ presence in LOS between access points and mobile device affects the RSS. This research proposes a new Adaptive Indoor Positioning System model (called DIPS) based on: a dynamic radio map generator, RSS certainty technique and peoples’ presence effect integration for dynamic and multi-floor environments. Dynamic in our context refers to the effects of people and device heterogeneity. DIPS can achieve 98% and 92% positioning accuracy for floor and room positioning, and it achieves 1.2 m for point positioning error. RSS certainty enhanced the positioning accuracy for floor and room for different mobile devices by 11% and 9%. Then by considering the peoples’ presence effect, the error is reduced by 0.2 m. In comparison with other works, DIPS achieves better positioning without extra devices.

Android platform has been targeted by cyber-criminals due to the increase number of Android users in 2017. More than 8,000 Android malware were identified everyday making it is difficult for the malware analyst to detect them. Traditional malware detection techniques are no longer reliable to detect newly created malware in short period of time. In this paper, we use a different approach to detect Android malware. The Android malware will be visualised into gray scale images and their image features will be extracted using GIST descriptor. The detection will be done and compare using three different classifiers namely k-nearest neighbor (KNN), Random Forest (RF), and Decision Tree (DT).

Hash functions are considered key components of nearly all cryptographic protocols, as well as of many security applications such as message authentication codes, data integrity, password storage, and random number generation. Many hash function algorithms have been proposed in order to ensure authentication and integrity of the data, including MD5, SHA-1, SHA-2, SHA-3 and RIPEMD. This paper involves an overview of these standard algorithms, and also provides a focus on their limitations against common attacks. These study shows that these standard hash function algorithms suffer collision attacks and time inefficiency. Other types of hash functions are also highlighted in comparison with the standard hash function algorithm in performing the resistance against common attacks. It shows that these algorithms are still weak to resist against collision attacks.