Edge Analytics With Machine Learning Technique for Medical IoT Applications

Edge analytics are tools and algorithms that are deployed in the internal storage of IoT devices or IoT gateways that collect, process, and analyze the data locally rather than transmitting it to the cloud for analysis. Edge analytics is applied in a wide range of applications in which immediate decision making is required. In the case of general IoT data analytics on the cloud, the data need to be collected from the IoT devices and to be sent to the cloud for further processing and decision making. In life-critical applications such as healthcare, the time taken to send the data to the cloud and then getting back the processed data to take decisions will not be acceptable. Hence, in these kinds of MIoT applications, it is essential to have analytics to be done on the edge in order to avoid such delays. Hence, this chapter is providing an abstract view on the application of machine learning in MIoT so that the data analytics provides fruitful results to the stakeholders.

Forensic Analysis of Social Networking Applications on an Android Smartphone

Smartphone users spend a substantial amount of time in browsing, emailing, and messaging through different social networking apps. The use of social networking apps on smartphones has become a dominating part of daily lives. This momentous usage has also resulted in a huge spike in cybercrimes such as social harassing, abusive messages, vicious threats, broadcasting of suicidal actions, and live coverage of violent attacks. Many of such crimes are carried out through social networking apps; therefore, the forensic analysis of allegedly involved digital devices in crime scenes and social apps installed on them can be helpful in resolving criminal investigations. This research is aimed at performing forensic investigation of five social networking apps, i.e., Instagram, LINE, Whisper, WeChat, and Wickr on Android smart phones. The essential motivation behind the examination and tests is to find whether the data resides within the internal storage of the device or not after using these social networking apps. Data extraction and analysis are carried out using three tools, i.e., Magnet AXIOM, XRY, and Autopsy. From the results of these experiments, a considerable amount of essential data was successfully extracted from the examined smartphone. This useful data can easily be recovered by forensic analysts for future examination of any crime situation. Finally, we analyzed the tools on the basis of their ability to extract digital evidences from the device and their performance are examined with respect to NIST standards.

Optimization of Position and Number of Hotspot Detectors Using Artificial Neural Network and Genetic Algorithm to Estimate Material Levels Inside a Silo

To realize efficient operation of a silo, level management of internal storage is crucial. In this study, to address the existing measurement limitations, a silo hotspot detector, which is typically utilized for internal silo temperature monitoring, was employed. The internal temperature data measured using the hotspot detectors were used to train an artificial neural network (ANN) algorithm to predict the level of the internal storage of the silo. The prediction accuracy was evaluated by comparing the predicted data with ground truth data. We combined the ANN model with the genetic algorithm (GA) to improve the prediction accuracy and establish efficient sensor installation positions and number to proceed with optimization. Simulation results demonstrated that the best predictive performance (up to 97% accuracy) was achieved when the ANN structure was 9-19-19-1. Furthermore, the numbers of efficient sensors and sensors positions determined using the proposed ANN-GA technique were reduced from seven to five or four, thereby ensuring economic feasibility.

Introgression of the sesquiterpene biosynthesis from Solanum habrochaites to cultivated tomato offers insights into trichome morphology and arthropod resistance

Main conclusion Cultivated tomatoes harboring the plastid-derived sesquiterpenes from S. habrochaites have altered type-VI trichome morphology and unveil additional genetic components necessary for piercing-sucking pest resistance. Abstract Arthropod resistance in the tomato wild relative Solanum habrochaites LA1777 is linked to specific sesquiterpene biosynthesis. The Sesquiterpene synthase 2 (SsT2) gene cluster on LA1777 chromosome 8 controls plastid-derived sesquiterpene synthesis. The main genes at SsT2 are Z-prenyltransferase (zFPS) and Santalene and Bergamotene Synthase (SBS), which produce α-santalene, β-bergamotene, and α-bergamotene in LA1777 round-shaped type-VI glandular trichomes. Cultivated tomatoes have mushroom-shaped type-VI trichomes with much smaller glands that contain low levels of monoterpenes and cytosolic-derived sesquiterpenes, not presenting the same pest resistance as in LA1777. We successfully transferred zFPS and SBS from LA1777 to cultivated tomato (cv. Micro-Tom, MT) by a backcrossing approach. The trichomes of the MT-Sst2 introgressed line produced high levels of the plastid-derived sesquiterpenes. The type-VI trichome internal storage-cavity size increased in MT-Sst2, probably as an effect of the increased amount of sesquiterpenes, although it was not enough to mimic the round-shaped LA1777 trichomes. The presence of high amounts of plastid-derived sesquiterpenes was also not sufficient to confer resistance to various tomato piercing-sucking pests, indicating that the effect of the sesquiterpenes found in the wild S. habrochaites can be insect specific. Our results provide for a better understanding of the morphology of S. habrochaites type-VI trichomes and paves the way to obtain insect-resistant tomatoes.

An Analysis for Selecting Best Smartphone Model by AHP-TOPSIS Decision-Making Methodology

This article presents the detailed study of integrated AHP-TOPSIS multiple-criteria decision-making (MCDM) methodology. For these purposes, a real-life example is taken where the best smartphone mobile model is proposed among 10 different available models by implementing integrated AHP-TOPSIS methodology. The 10 mobile models selected for this analysis are presently available in the market and are from different brands having different specifications and price range. The selection process is done based on four major criteria (i.e., price, internal storage, RAM, and brand). AHP is applied for the criteria weightage's calculation, whereas TOPSIS is adopted for selecting the best alternative and make a preference ranking order indicating the best model to the worst. The final result shows that Samsung J7 is the best smartphone model followed by Redmi 7A, and Redmi K20 pro occupies the last position; thus, it is the worst model among the group.

Klasifikasi Multi Output pada Harga Smartphone Menggunakan Learning Vector Quantization (LVQ) dan Backpropagation (BP)

There are many smartphones with various price sold in market. The price of smartphone is affected by some components such as weight, internal storage, memory (RAM), rear camera, front camera and brands. There are two methods for classifying price class of smartphone in market such as Learning Vector Quantization (LVQ) and Backpropagation (BP). From classifying price class of smartphone in market using LVQ and BP, there are the differences on the both of them. LVQ classifies price range of smartphone by euclidean distance of weight and data on its iteration. BP classifies price range of smartphone by gradient descent of target and output on its iteration. In multi output classification, one object may have multi output. Based on simulation results, BP gives the better accuracy and error rate in training data and testing data than LVQ.

Real-time monitoring epidemic trends and key mutations in SARS-CoV-2 evolution by an automated tool

ABSTRACTWith the global epidemic of SARS-CoV-2, it is important to monitor the variation, haplotype subgroup epidemic trends and key mutations of SARS-CoV-2 over time effectively, which is of great significance to the development of new vaccines, the update of therapeutic drugs, and the improvement of detection reagents. The AutoVEM tool developed in the present study could complete all mutations detections, haplotypes classification, haplotype subgroup epidemic trends and key mutations analysis for 131,576 SARS-CoV-2 genome sequences in 18 hours on a 1 core CPU and 2G internal storage computer. Through haplotype subgroup epidemic trends analysis of 131,576 genome sequences, the great significance of the previous 4 specific sites (C241T, C3037T, C14408T and A23403G) was further revealed, and 6 new mutation sites of highly linked (T445C, C6286T, C22227T, G25563T, C26801G and G29645T) were discovered for the first time that might be related to the infectivity, pathogenicity or host adaptability of SARS-CoV-2. In brief, we proposed an integrative method and developed an efficient automated tool to monitor haplotype subgroup epidemic trends and screen out the key mutations in the evolution of SARS-CoV-2 over time for the first time, and all data could be updated quickly to track the prevalence of previous key mutations and new key mutations because of high efficiency of the tool. In addition, the idea of combinatorial analysis in the present study can also provide a reference for the mutation monitoring of other viruses.