Machine learning approaches are powerful techniques commonly employed for developing cancer prediction models using associated gene expression and mutation data. Our survey provides a comprehensive review of recent cancer studies that have employed gene expression data from several cancer types (breast, lung, kidney, ovarian, liver, central nervous system and gallbladder) for survival prediction,tumor identification and stratification. We also provide an overview of biomarker studies that are associated with these cancer types. The survey captures multiple aspects of machine learning associated cancer studies,including cancer classification, cancer prediction, identification of biomarker genes, microarray, and RNA-Seq data.We discuss the technical issues with current cancer prediction models and the corresponding measurement tools for determining the activity levels of gene expression between cancerous tissues and noncancerous tissues. Additionally, we investigate how identifying putative biomarker gene expression patterns can aid in predicting future risk of cancer and inform the provision of personalized treatment.

Amyotrophic lateral sclerosis, also known as ALS, is a progressive nervous system disorder that affects nerve cells in the brain and spinal cord, resulting in the loss of muscle control. For individuals with ALS, where mobility is limited to the movement of the eyes, the use of eye-tracking-based applications can be applied to achieve some basic tasks with certain digital interfaces. This paper presents a review of existing eye-tracking software and hardware through which eye-tracking their application is sketched as an assistive technology to cope with ALS. Eye-tracking also provides a suitable alternative as control of game elements. Furthermore, artificial intelligence has been utilized to improve eye-tracking technology with significant improvement in calibration and accuracy. Gaps in literature are highlighted in the study to offer a direction for future research.

Hybrid wired-wireless Network-on-Chip (WiNoC) has emerged as an alternative solution to the poor scalability and performance issues of conventional wireline NoC design for future System-on-Chip (SoC). Existing feasible wireless solution for WiNoCs in the form of millimeter wave (mm-Wave) relies on free space signal radiation which has high power dissipation with high degradation rate in the signal strength per transmission distance. Moreover, over the lossy wireless medium, combining wireless and wireline channels drastically reduces the total reliability of the communication fabric. Surface wave has been proposed as an alternative wireless technology for low power on-chip communication. With the right design considerations, the reliability and performance benefits of the surface wave channel could be extended. In this paper, we propose a surface wave communication fabric for emerging WiNoCs that is able to match the reliability of traditional wireline NoCs. First, we propose a realistic channel model which demonstrates that existing mm-Wave WiNoCs suffers from not only free-space spreading loss (FSSL) but also molecular absorption attenuation (MAA), especially at high frequency band, which reduces the reliability of the system. Consequently, we employ a carefully designed transducer and commercially available thin metal conductor coated with a low cost dielectric material to generate surface wave signals with improved transmission gain. Our experimental results demonstrate that the proposed communication fabric can achieve a 5 dB operational bandwidth of about 60 GHz around the center frequency (60 GHz). By improving the transmission reliability of wireless layer, the proposed communication fabric can improve maximum sustainable load of NoCs by an average of 20:9 and 133:3 percent compared to existing WiNoCs and wireline NoCs, respectively.

The rise of Telemedicine has revolutionized how patients are being treated, leading to several advantages such as enhanced health analysis tools, accessible remote healthcare, basic diagnostic of health parameters, etc. The advent of the Internet of Things (IoT), Artificial Intelligence (AI) and their incorporation into Telemedicine extends the potential of health benefits of Telemedicine even further. Therefore, the synergy between AI, IoT, and Telemedicine creates diverse innovative scenarios for integrating cyber-physical systems into medical health to provide remote monitoring and interactive assistance to patients. Data from World Health Organization reports that 7.4 million people died because of Atrial Fibrillation (AF), recognizing the most common arrhythmia associated with human heart rate. Causes like unhealthy diet, smoking, poor resources to go to the doctor and based on research studies, about 12 and 17.9 million of people will be suffering the AF in the USA and Europe, in 2050 and 2060, respectively. The AF as a cardiovascular disease is becoming an important public health issue to tackle. By using a systematic approach, this paper reviews recent contributions related to the acquisition of heart beats, arrhythmia detection, IoT, and visualization. In particular, by analysing the most closely related papers on Convolutional Neural Network (CNN) and IoT devices in heart disease diagnostics, we present a summary of the main research gaps with suggested directions for future research.

Recently, Through-Silicon-Via (TSV) has been more popular to provide faster inter-layer communication in three-dimensional Networks-on-Chip (3D NoCs). However, the area overhead of TSVs reduces wafer utilization and yield which impact design of 3D architectures using a large number of TSVs such as homogeneous 3D NoCs topologies. Also, 3D routers require more memory and thus they are more power hungry than conventional 2D routers. Alternatively, hybrid 3D NoCs combine both the area and performance benefits of 2D and 3D router architectures by using a limited number of TSVs. Existing hybrid architectures suffer from higher packet delays as they do not consider the dynamic communication patterns of different application and their NoC resource usage. We propose a novel algorithm to systematically generate hybrid 3D NoC topologies for a given application such that the vertical connections are minimized while the NoC performance is not sacrificed. The proposed algorithm analyses the target application and generates hybrid architectures by efficiently redistributing the vertical links and buffer spaces based on their utilizations. Furthermore, the algorithm has been evaluated with synthetic and various real-world traffic patterns. Experimental results show that the proposed algorithm generates optimized architectures with lower energy consumption and a significant reduction in packet delay compared to the existing solutions.