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 doud supplier doesn't have recommendations concerning the data and the doud information put away and kept up internationally anyplace in the cloud. The security assurance plans are normally founded on encryption innovation. There are numerous security saving techniques on the side to forestall information in the cloud. A three-layer stockpiling structure dependent on mist registering is proposed. The proposed structure can both exploit distributed storage and secure the protection of information. Here Hash-Solomon code calculation is intended to isolate information into various parts. If one information part missing, the information data is tend to loss. Right now is utilizing pail idea based calculations and secures the information data and afterward, it can show the security and proficiency in our plan. Also, in light of computational knowledge, this calculation can figure the conveyance extent put away in a cloud, mist, and neighborhood machine, individually Software as a Service (SaaS) client discharges their application on a facilitating situation which can be gotten to through system from different customers by application clients. The customer doesn't oversee or control the basic cloud foundation with the conceivable exemption of constrained client explicit application arrangement settings. Google Apps and Microsoft Office 365 are the models for S aaS.

Implementation of a Privacy Preserving and Loss Data Retrieval System for Cloud Environment

In a developing country like India, agriculture is one of the most significant sectors in terms of financial gain and healthy foods to survive. Agriculture has always been our country's primary occupation. Smart food plays a vital role once it involves food security and that's littered with food loss and food wastage. Reports suggests that, almost fifty proportion of the food produced world-wide doesn't even reach the individuals as they're wasted throughout harvest, transportation or storage. One in all the key threats to the farmers is that the loss throughout storage of crops in granaries and warehouse. If crop losses are reduced, it'll mechanically increase the number of food accessibility. Our work takes into thought this drawback that affects a lot of individuals globally and has tried to bring out an easy answer by building a sensing element based mostly module that monitors and additionally controls the deposition system. In order to assist the farmer, WHO protects the crops that are stored in a warehouse, an IoT enabled observation system is planned to be deployed in remote areas, wherever the accessibility is incredibly minimum for farmers with smart storage facilities to scale back the food losses and increase food safety.

IoT based Smart Warehouse and Crop Monitoring System

The most commonly diagnosed and second leading cause of cancer fatalities is breast cancer in women. AI and IoT integrated system that can help diagnose earlier breast cancer. The key tool for detecting breast cancer is mammograms. Yet cancer cells in breast tissue are difficult to identify. Since it has less fat and more muscle. To examine the irregular areas of density, mass and calcification that signify the presence of cancer, digitized mammography images are used. Several imaging techniques have been developed to detect and treat breast cancer early and to decrease the number of deaths, and many methods of diagnosis of breast cancer have been used to increase diagnostic accuracy. The current technique does not detect breast cancer reliably in the early stages, and most women have suffered from this.

Breast Cancer Detection Using Machine Learning

A film advice is vital in our social lifestyles due to its electricity in presenting more suitable leisure. One of these gadget can recommend a hard and fast of films to customers founded totally on their hobby, or the popularities of the films. Even though, a set of movie advice systems were proposed most of these both can’t suggest a film to the existing customers effectively or to a new consumer. On this paper we advocate a film advice network that has capability to suggest films to a recent consumer also the others. It mines film databases to that have to gather all of the important information, which include reputation and attractiveness, required for recommendation. It generates film swarms no longer handiest handy for film producer to plot a new brand film but also useful for movie recommendation Experimental research on the real statistics displays the performance and effectiveness of the proposed machine.

/pdf/movie-success-prediction-using-recommendation-techniques-3izo140tc5.pdf

Movie success prediction using recommendation techniques

The interface permits the client to scan for a subjective utility on the Play Store; the authorizations posting and the protection arrangement are then routinely recovered, on all events imaginable. The client has then the capability of choosing an interesting authorization, and a posting of pertinent sentences are separated with the guide of the privateer's inclusion and introduced to them, alongside a right depiction of the consent itself. Such an interface allows the client to rapidly assess the security-related dangers of an Android application, by utilizing featuring the pertinent segments of the privateer's inclusion and by introducing helpful data about shrewd authorizations. A novel procedure is proposed for the assessment of privateer's protection approaches with regards to Android applications. The gadget actualized widely facilitates the way toward understanding the security ramifications of placing in 1/3 birthday celebration applications and it has just been checked in a situation to feature troubling examples of uses. The gadget is created in light of expandability, and correspondingly inclines in the strategy can without trouble be worked in to broaden the unwavering quality and adequacy. Likewise, if your application handles non-open or delicate individual information, it would be ideal if you also allude to the extra necessities in the “Individual and Sensitive Information” territory underneath. These Google Play necessities are notwithstanding any prerequisites endorsed by method for material security or data assurance laws. It has been proposed that, an individual who needs to perform the establishment and utilize any 1/3 festival application doesn't perceive the significance and which methods for the consents mentioned by method for an application, and along these lines sincerely gives all the authorizations as a final product of which unsafe applications furthermore get set up and work their malevolent leisure activity in the rear of the scene.

Jithina Jose

Papers

Implementation of a Privacy Preserving and Loss Data Retrieval System for Cloud Environment

IoT based Smart Warehouse and Crop Monitoring System

Breast Cancer Detection Using Machine Learning

Movie success prediction using recommendation techniques

Analysing Android App Privacy Using Classification Algorithm