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

The effect of air quality on wellbeing and on life comfort is entrenched. Indoor air quality parameters are critical to make a gainful and sound indoor condition anyway by and large the air quality parameters are altogether different from those characterized as solid qualities. Taking into account that we spend about 90% of our lives inside, it is critical to screen the indoor air quality progressively to distinguish issues in the nature of air and plan intercessions in the building, or in the ventilation frameworks so as to enhance air quality. In this paper we are identifying the indoor air quality with the help of internet of things by using the sensors BME680 from bosch, SGP30 and CCS811 and low power wide area network (LPWAN) for the data transfer. Here we are separating the gases CO2 equivalent, Ethanol, TVoC and so on. For transmitting this IOT information, we require high vitality productive sensor hubs that can impart crosswise over long separation. This propels the improvement of some Low-Power Wide Area Networks (LPWAN) advances, for example, LoRa, to satisfy these requirements. The reports are exhibiting the destructiveness of air that we are taking in step by step. Here we are trying to give a social awareness by using this paper.

Indoor air quality monitors using IOT sensors and LPWAN

In the present time deaths because of heart disease has become a significant issue roughly one individual kicks the bucket every moment because of heart disease. Machine learning includes man-made brainpower, and it is utilized in taking care of numerous issues in information science. One normal utilization of Machine learning is the expectation of a result dependent on existing information. The machine takes in designs from the current dataset, and afterward applies them to an obscure dataset so as to anticipate the result. Characterization is an amazing Machine learning strategy that is regularly utilized for forecast. Some order calculations anticipate with acceptable precision, while others show a constrained exactness. This paper explores a technique named outfit characterization, which is utilized for improving the exactness of frail calculations by consolidating different classifiers. Investigations with this apparatus were performed utilizing a heart disease dataset. The focal point of this paper isn’t just on expanding the exactness of frail order calculations, yet in addition on the execution of the calculation with a restorative dataset, to demonstrate its utility to anticipate infection at a beginning period. The consequences of the investigation show that group strategies, for example, stowing and boosting, are viable in improving the expectation precision of feeble classifiers, and display palatable execution in distinguishing danger of heart disease.

Improving the Accuracy in Prediction of Heart Disease using Machine Learning Algorithms

/pdf/visualizing-road-damage-by-monitoring-system-in-cloud-2mwn48a7y3.pdf

Visualizing road damage by monitoring system in cloud

Currently, bogus voting in elections is remaining as a serious issue. The question of casting a ballot as far as health and protection are concerned is still important. The undertaking is primarily planned for giving a most verified and easy to understand Online Election System. An online political decision system is a voting system. In Traditional political race condition casting a ballot procedure now and again become very badly designed because of the reluctances of specific voters to visit a surveying stall to cast casts a ballot other than including tremendous social and HR. This paper manages the development and progress of an electronic voting system using distributed computing and aadhar card to provide the voting system with a superior with high security. Additionally, web innovation is used to make the voting system reasonable. The Internet surveying system is the system that empowers the client to cast a ballot on the web. In particular, the Voting system comprised of server administrations which are each connected with a database for putting away persevering information. Voters can likewise utilize the administrations to sign into the electronic voting system site. The field official will send the voter data and their voting's and that data will be kept up by creator card check. Administrators will keep up all data concerning voter and tallies consequently their deciding in favor offs their chosen gatherings.

Aadhar Card Verification Base Online Polling

This paper studies about the impact of firework emission on air quality of Chennai. This paper considers the Diwali festival season for the studies as it is the time when a lot of burning of firework is happening. Researchers have observed that due to the heavy use of fire crackers the air quality during the Diwali festival season is dangerously low in comparison with other times of the year. The across the board consuming of fireworks discharges immense measure of harmful gases and colossal measure of pressurized canned products and particulate issue. The present examination canters around the impact of firecrackers on encompassing air quality amid Diwali Festival in Chennai City. In this investigation, PM 2.5, SO2, NOx, CO and O3 were assessed at two agent areas, for pre Diwali, Diwali day and days following the festival. The dimensions of these contaminations saw amid Diwali were observed to be higher when contrasted with Pre Diwali. Consequently it came to a most extreme fixation upon the arrival of the celebration and afterward the qualities steadily diminished after the celebration. The outcomes demonstrate that firecracker outflows amid Diwali celebration influenced the surrounding air quality hurtfully because of emanation and increment of PM2.5, SO2, NOx, CO and O3.

Jithina Jose

Papers

Indoor air quality monitors using IOT sensors and LPWAN

Improving the Accuracy in Prediction of Heart Disease using Machine Learning Algorithms

Visualizing road damage by monitoring system in cloud

Aadhar Card Verification Base Online Polling

Data analytics to find out the effect of firework emissions on quality of air: A case study