In machine learning, a computer first learns to perform a task by studying a training set of examples. The computer then performs the same task with data it hasn't encountered before. This article presents a brief overview of machine-learning technologies, with a concrete case study from code analysis.

http://ieeexplore.ieee.org/iel7/52/7548893/07548905.pdf

Machine Learning

During the few last years, indoor and outdoor Air Quality Monitoring (AQM) has gained a lot of interest among the scientific community due to its direct relation with human health. The Internet of Things (IoT) and, especially, Wireless Sensor Networks (WSN) have given rise to the development of wireless AQM portable systems. This paper presents the development of a LoRa (short for long-range) based sensor network for AQM and gas leakage events detection. The combination of both a commercial gas sensor and a resistance measurement channel for graphene chemoresistive sensors allows both the calculation of an Air Quality Index based on the concentration of reducing species such as volatile organic compounds (VOCs) and CO, and it also makes possible the detection of NO2, which is an important air pollutant. The graphene sensor tested with the LoRa nodes developed allows the detection of NO2 pollution in just 5 min as well as enables monitoring sudden changes in the background level of this pollutant in the atmosphere. The capability of the system of detecting both reducing and oxidizing pollutant agents, alongside its low-cost, low-power, and real-time monitoring features, makes this a solution suitable to be used in wireless AQM and early warning systems.

/pdf/lora-sensor-network-development-for-air-quality-monitoring-19nhoqf6hb.pdf

LoRa Sensor Network Development for Air Quality Monitoring or Detecting Gas Leakage Events

Distributed Denial of Service (DDoS) attacks have emerged as a popular means of causing mass targeted service disruptions, often for extended periods of time. The relative ease and low costs of launching such attacks, supplemented by the current inadequate sate of any viable defense mechanism, have made them one of the top threats to the Internet community today. Since the increasing popularity of web-based applications has led to several critical services being provided over the Internet, it is imperative to monitor the network traffic so as to prevent malicious attackers from depleting the resources of the network and denying services to legitimate users. This paper first presents a brief discussion on some of the important types of DDoS attacks that currently exist and some existing mechanisms to combat these attacks. It then points out the major drawbacks of the currently existing defense mechanisms and proposes a new mechanism for protecting a web-server against a DDoS attack. In the proposed mechanism, incoming traffic to the server is continuously monitored and any abnormal rise in the inbound traffic is immediately detected. The detection algorithm is based on a statistical analysis of the inbound traffic on the server and a robust hypothesis testing framework. Simulations carried out on the proposed mechanism have produced results that demonstrate effectiveness of the proposed defense mechanism against DDoS attacks.

A Robust Mechanism for Defending Distributed Denial OF Service Attacks on Web Servers

Internet of Things (IoT) technology has become ubiquitous in a multitude of applications and its use is growing. However, the expansion of IoT faces a major difficulty: scalability, that is, very dense deployment of communicating devices is currently limited. In long-range networks, such as LoRa, the downlink is critical because it limits the number of acknowledgements that can be sent, and consequently reliability. It also limits the possibility to update the devices, which could be critical when they are deployed for decades. To overcome those problems, we propose a solution, inspired by Non Orthogonal Multiple Access (NOMA) techniques, to increase by at least one order of magnitude the number of devices that can be addressed. While the approach differentiates the devices by the power allocated to them, it differs from the vast majority of previous works on power domain NOMA because it does not require interference cancellation. Instead, it benefits from the spectrum spreading of the modulation scheme (chirp spread spectrum), where, at the end of the decoding phase, the information carried by a symbol is found in the position of a peak in the Fourier domain. In the vast majority of cases, the information from different users results in different peak positions, not creating any interference. In that sense, we get closer to avoidance schemes such as time or frequency hopping, but without using a code. In this article, we propose a new solution for NOMA in the power domain that does not suffer from the limitations induced by interference cancellation residues. The proposed system, including preamble detection and channel estimation, is presented and evaluated by simulations. We demonstrate that our proposed scheme increases the number of devices by one order of magnitude compared to the current system which allows addressing only one user at a time and maintains full compatibility with the LoRa physical layer standard.

/pdf/multiuser-detection-for-downlink-communication-in-lora-like-5cot8q74pt.pdf

Multiuser Detection for Downlink Communication in LoRa- Like Networks

Thermal envelope analysis for new code compliance of Saudi Arabian dwellings

Computer vision plays a vital role in human health care area. The various applications of these techniques have been playing a key role in reducing physicians judgment in diagnosis. Especially, in the case of brain related cases, it is suggested and required to take utmost care and a small error in diagnosis leads to disastrous. Although various methods like X ray, ct scan are available, mri is more reliable and danger free. High contrast images of brain tissues can be taken by using mri technique. Brain Tumor segmentation involves separation of tumors from normal brain tissues. The segmentation process helps in fetching required information for the purpose of diagnosis and what treatment has to be taken. It plays a vital role in medical image processing. The process involves risk because of abnormal form and irregularities at the boundaries. Determination of these tumors at the earlier stage helps to give better treatment to the patients. Usually, Physicians deal with the affected people by performing manual way of segmentation which results in the absence of time management. To overcome this manual segmentation problem, we developed CNN method of segmentation procedure which performs well by taking various factors like accuracy, recall and precision into consideration. MRI is one of the conservative techniques for prior diagnostic tool of brain tumor. The necessary measures for diagnosis and treatment planning have to be taken place quickly if the tumor is malignant in order to enhance successful treatment. By applying the following method, the doctors are capable of making accurate decision and required treatment can be done in an effective manner. The computer aided diagnostic system, MRI helps in reduction of workload by doctors.

Analysis Of Brain Tumor Segmentation using Convolutional Neural Network with Magnetic Resonance Imaging

The objective of this article is to detect gas leakage and control the airflow of the exhaust automatically to vent out the gas. An alert would also be sent to the concerned person and an emergency request will be placed directly to the fire service along with the appropriate location. The project is carried out using the Internet of Things (IoT). This system has been proposed to minimize the accidents caused by gas leakage and exhaust the gas by automatically controlling the airflow. The proposed system also comes with the ability to automatically book a new gas if the current one extinguishes. We have made use of Google’s Firebase cloud services to constantly update the data obtained from the various sensors used in the system.

Airflow Control and Gas Leakage Detection System

In software engineering, the single point is to deliver top notch yield while enhancing the expense and the time expected to finish the application development. To accomplish this objective, software groups will play out the test on their application before live creation. For test automation documentation assumes a critical job. This paper center around mechanize experiment age dependent on accessible test assets, challenges which could be conveyed by methods for orderly practice improvement and characterizing test system for any software application. One noteworthy motivation is to ensure perceptibility among necessities and system experiments. Along these lines, the significance of experiments is dull and testing, especially under time objectives and when there are progressive changes to necessities. Right now, customized test age diminishes the cost of testing just as guarantees that experiments properly spread all necessities, a huge objective in prosperity essential systems and for the measures they need to come. This proposed application guarantees proficient test inclusion of software surrenders, where key usefulness won't be missed in the automatic test absconds expectation. Right now, experiments can be produced after application development completes a component or a lot of highlights.

A Robust and Intelligent Machine Learning Algorithm for Software Testing

In the recent era, a large number of private and corporate users redistribute the data to the cloud providers. In this extremely large amount of data, there exists large amount of copies of repeated duplicate data. The term data duplication is nothing but compressed repeated data. In the existing system, the process of access control is implemented along with data deduplication which can be done on encrypted data as well. In the proposed system, the user will upload the files to the cloud and it will get changed into an encrypted format by the process of hashing which is done by using SHA-256 algorithm. Based on the existence of the file on the cloud server the user may upload or download the same. In addition, DDoS attack detection and implementation of two types of attacks, collusion attack and sybil attack detection for user identity is addressed. Experimental analysis shows that the storage utilization can be maximized and the efficiency and security of cloud server is improved.

A Primitive Solution to Avoid Data Deduplication in Cloud and Overcome Security Threats

This paper describes an implementation of a trusted, self-sorted, open and natural nourishment recognizability framework dependent on blockchain and Internet of things (IoT) innovations, which includes all gatherings of a shrewd agribusiness biological system, regardless of whether they may not confide in one another. We use IoT gadgets to supplant manual chronicle and check whatever number as could be allowed, which can diminish the human intercession to the framework adequately. Right now, propose a blockchain enlivened web of things design for making a straightforward nourishment inventory network. The total design was acknowledged by coordinating a BARCODE-based sensor at the physical layer and blockchain at the digital layer. The BARCODE gives a one of a kind personality of the item and the sensor information, which helps continuously quality checking. For this reason, a little component size 900 MHz BARCODE coupled sensor was manufactured and exhibited for continuous sensor information obtaining.

Jithina Jose

Papers

Analysis Of Brain Tumor Segmentation using Convolutional Neural Network with Magnetic Resonance Imaging

Airflow Control and Gas Leakage Detection System

A Robust and Intelligent Machine Learning Algorithm for Software Testing

A Primitive Solution to Avoid Data Deduplication in Cloud and Overcome Security Threats

An Efficient Implementation of a Proposed Food Quality Ensuring Architecture Using Blockchain Technologies