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Infectious diseases like typhoides, dengues, etc. are caused by water and environmental changes. It is important to provide medications for such infection patients. Conventional approaches for the correct diagnosis of patients are not acceptable. To make an appropriate diagnosis it is important to accurately identify patient status. Although advancement in reliable diagnostics of technology remains an essential mission. This thesis suggested a classification approach for certain infectious diseases. 173 Typhoid fever patients and 119 Dengue Fever patients were collected clinically. Crafted a hybrid method using voting set technique that was constructed as base classification in machine learning techniques. The diagnostic results can be classified according to the classificator's majority of votes. Data set tests were carried out and the results were contrasted with traditional computer teaching methods, including the Group Baive Bayesian, K-Nearest Neighbor and the Decision Tree. Analysis indicates that, in the case of typhoid and dengue-fever infected patients, the suggested hybrid solution has a 97.2% precision. Focusing on infectious diseases, several traditional approaches were analysed and the proposed hybrid methodology focused on an ensemble voting strategy that showed the highest efficiency in classifying infectious diseases.

Leukemia – a type of blood cancer which was caused by abnormal growth of White Blood Cells (WBC) in bone marrow. Accurate identification of effected patients and diagnosing within time is most important otherwise it causes to death. Machine learning algorithms are most popular for accurate identification of patient status. Presenting 152 patients data which was clinically collected. In this paper, proposing Bagging with Random split function for classification of leukemia patients. Also compared the classifier performance with various ensemble techniques – Multiclass classifier, Logit Boost, Stacking and Random Committee classifiers. Experiments are conducted and proven that bagging with Random split function shows 95% accuracy on classification of leukemia disease dataset.

Trend prediction is and has been one of the very important tasks in the stock market since day one. For a sophisticated trend prediction using real time stock market data, stock sentiment news and technical analysis plays a vital role. While predicting the trend in the conventional way, technical indicators are delayed due to temporal data and less historic data. All the conventional stock trend predicting methods sustained without sentiment scores, technical scores and time periods for trend prediction. Considering the fact that all the previous conventional methods of stock trend predictions are bound to take single stock for trend prediction due to high computational memory and time, this prototype of highly functioning algorithms focus on trend prediction with multi stock data breaking all the conventional rules. This multi stock trend prediction model commissions and implements the effectively programmed algorithms on real time stock market data set. In this multi-stock trend prediction model, a new stock technical indicator and new stock sentiment score are proposed in order to improve the stock feature selection for trend prediction. In order to find the best real time feature selection model, a technical feature selection measure and stock news sentiment score are developed and incorporated. We used integrated stock market data to make a hybrid clustered model to find the relational multi stocks. Giving a final verdict, this is a cluster based nonlinear regression multi stock framework in order to predict the time-based trend prediction. The multi stock trend regression accuracy is bettered by 12% and recall by 11% while we cross check the experimental outcomes, henceforth making this model more accurate and precision furnished.

As the recent advancement in communication technologies revolutionizes the world, Email is emerging as a wide-known communication paradigm in various business processes. Email is an effective, brisk and minimal effort correspondence approach. Email Spam is non-asked for information sent to the E-letter drops. Spam could be an enormous downside each for clients and for ISPs. As per examination these days client gets a considerable measure of spam messages then non-spam messages. In some cases spam messages may damage the reputation of a particular business process. It can be observed that in most of the popular mailing services the spam filters are being biased for the profit from the ads, i.e. they are allowing some exception for some companies that pay for advertising. This is not an ethical practice, but it is very profitable for their future business processes. Our aim is to build a spam detector using machine learning in python with the packages NLTK, Matplotlib, Word cloud, Math, pandas, NumPy. With this proposed model, we can state a specified message as spam or non-spam. It can be implemented by using Bayes’ Theorem, a simple yet powerful theorem.

Image enhancement is a technique to give a better quality of an image in terms of its clarity, brightness, and to give the human eye comfortable to look at. There are different types of techniques to give good quality to an image. Global image contrast enhancement is one of the most commonly used techniques to enhance the quality of an image, but it has some disadvantages with the fact that it does not consider the local details of an image, that is the detailed texture of an image. In local contrast enhancement, it addresses the local details of an image and preserves the local details of the image. Local details of an image are very important while analyzing an image, which is that of the scientific study of an image like the image taken from planetary bodies, satellite image, and also in medical images. Local details of an image are very important for diagnosing a particular ailment. When we used either local contrast enhancement or global contrast enhancement alone, we faced the loss of brightness of the image. In order to address and reduce this discrepancy of individual enhancement methods, a new proposal that uses both these methods on the same image. First, the image is locally enhanced and the output is again processed by the global enhancement method, thereby giving a properly enhanced image without losing the brightness of the image. This enhancement method is simulated in MATLAB, and results are verified on the parameters of image.

The rapid advancements in technology have led to an increase in the exchange of data (image, video, audio, etc.) between portable devices. This invokes the necessity of building the algorithms which consume low power with no compromise in the performance. In this paper, the abovementioned issue is taken into account and accordingly an image compression technique using Repetitive Iteration CORDIC (RICO) architecture has been proposed. The proposed method is power efficient as it uses RICO for Discrete Cosine Transform (DCT) coefficient generation and performs equally well as compared to JPEG. Results have been obtained via MATLAB and they show that our proposed technique performs equally well and consumes less power compared with the other.

The Internet of Things pertains to connecting currently unconnected things and people. It is the new era in transforming the existed systems to amend the cost effective quality of services for the society. To support Smart city vision, Urban IoT design plans exploit added value services for citizens as well as administration of the city with the most advanced communication technologies. To make emergency response real time, IoT enhances the way first responders and provides emergency managers with the necessary up-to-date information and communication to make use of those assets. IoT mitigates many of the challenges to emergency response including present problems, like a weak communication network and information lag. In this paper, it is proposed that an emergency response system for fire hazards is designed by using IoT standardized structure. To implement this proposed scheme a low-cost Expressive wi-fi module ESP-32, Flame detection sensor, Smoke detection sensor (MQ-5), Flammable gas detection sensor and one GPS module are used. The sensors detects the hazard and alerts the local emergency rescue organizations like fire departments and police by sending the hazard location to the cloud-service through which all are connected. The overall network utilizes a light weighted data oriented publish-subscribe message protocol MQTT services for fast and reliable communication. Thus, an intelligent integrated system is designed with the help of IoT.

This paper will exhibit smarter ways of saving electricity in cities by using solar energy for smart street lights. Photovoltaic source will be used as gaining energy and distributing it to the LED's of the street lights. The energy occupied will be stored in a rechargeable battery which will automatically turn itself ON whenever the sunlight is not received to the detectors. Every street light will then work automatically with respect to the presence of sunlight, time and also by sensing the presence of any object using PIR sensors and also explain how the cost of electricity using street lights can be minimized using the above the mentioned parameters for developing the Smart Street.

Sentiment analysis deals with identifying and classifying opinions or sentiments expressed in main text. It mainly refers to a text classification. Social media is generating a vast amount of sentiment rich data in the form of tweets, blog posts, comments, status updates, news etc. Sentiment analysis of this user generated data is very useful in knowing the opinion of the public. Knowledge base approach and Machine learning approach are the two strategies used for analyzing sentiments from the text. In this paper, Machine learning approach has been used for the sentiment analysis of movie review dataset and is analysed by Naïve Bayes, Decision tree, KNN, and SVM classifiers. Commencing the most efficient classification technique is the moto of the paper. Efficiency of the classifier is decided based on some regular parameters that are outputs of the classification techniques.

Body area network (BAN) is a promising technology for monitoring different physiological parameters of the patients in real time. Particularly, when the BAN integrated with wireless technologies provides the complete medical infrastructure. Android is the most popular operating system in the smartphone. The wireless BAN combined with an Android-based smartphone offers a large functionality. Various medical parameters can be analyzed, stored, and visualized using the GUI of an Android application designed for the end user. The different sensors placed on the body of the patient acquire physiological data from patient. This acquired data is then, gone through signal processing and data analysis and results are sent to coordinator node. The physiological data are transferred, via bluetooth to an Android-based smartphone. The physiological parameters of the patients are continuously monitored by the system and if any variation occurred, it sends alert messages to the doctor. The alert is of two types, SMS alert and email alert. Using this alert system, the emergency situation can be handled effectively and the patient will get the medical care as soon as possible.

Nowadays, location-aware security policies play a key role in wireless sensor networks. In this paper, we propose to develop a distributed secured localization scheme that validates the reliability of location information associated with event reports. We make the anchor nodes are deployed such that they form connected sensor coverage and their routing information to these anchor nodes, thereby avoiding the sink overload problem. Since this scheme involves distributed anchor nodes for verification, it has less overhead and delay. By simulation results, we show that our proposed scheme attains good delivery ratio with reduced delay and overhead.

Now-a-days, the primary focus of the search techniques in the first generation of the Web is accessing relevant documents from the Web. Though it satisfies user requirements, but it is insufficient as the user sometimes wishes to access actionable information involving complex relationships between two given entities. Finding such complex relationships (also known as semantic associations) is especially useful in applications such as National Security, Pharmacy and Business Intelligence etc. Therefore the next frontier is discovering relevant semantic associations between two entities present in large semantic metadata repositories. Given two entities, there exist a huge number of semantic associations between two entities. Hence ranking of these associations is required in order to find more relevant associations. For this Aleman Meza et al. proposed a method involving six metrics viz. context, subsumption, rarity, popularity, association length and trust. To compute the overall rank of the associations this method computes context, subsumption, rarity and popularity values for each component of the association and for all the associations. However it is obvious that, many components appears repeatedly in many associations therefore it is not necessary to compute context, subsumption, rarity and popularity values of the components every time for each association rather the previously computed values may be used while computing the overall rank of the associations. This paper proposes a method to reuse the previously computed values using a hash data structure thus reduce the execution time. To demonstrate the effectiveness of the proposed method, experiments were conducted on SWETO ontology. Results show that the proposed method is more efficient than the other existing methods.

Now-a-days the increasing burden of maintaining both the topic-specific software and the framework that houses the basic components has become uncomfortably expensive. Most of the framework for legacy software has not been migrated to modern programming languages or new computational platforms. The flexibility of using this legacy infrastructure becomes more difficult. This situation is complicated by the use of government created software tools, which may also be out of date or generally are awkward to modify/manage. These conditions have driven an unstoppable movement to COTSS-based tools. In view of the above it is proposed to develop an Electronic Warfare simulator using COTS tools like STAGE for EW Scenario Generation and VAPS XT for building Dynamic displays.