Search

Home automation offers a good solution to help conserve our natural resources in a time when we are all becoming more environmentally conscious. Home automation systems can reduce power consumption and when they are not in use automatically turn off lights and appliances. With home automation, many repetitive tasks can be performed automatically or with fewer steps. For example, each time the person gets out of his computer desk, for instance, the fan and the lights need to be turned off and switched on when he comes back to the computer desk. This is a repetitive task, and failure to do so leads to a waste of energy. This paper proposes a security/energy saving system based on face recognition to monitor the fan and lights depending on the presence or absence of the authenticated user. Initially, the authenticated faces/users LBPH (Local Binary Pattern Histogram) features were extracted and modelled using SVM to construct the face profile of all authenticated users. The webcam catches the user's picture before the PC and the Haar-cascade classifier, a profound learning object identification technique is used to identify face objects from the background. The facial recognition techniques were implemented with python and linked to the cloud environment of Ada-Fruit in order to enable or disable the light and fan on the desk. The relay status is transmitted from Ada Fruit Cloud to Arduino Esp8266 using the MQTT Protocol. If the unidentified user in the webcam is detected by this device, the information in the cloud will be set to ' off ' status, allowing light and fan to be switched off. Although Passive Infrared Sensor (PIR) is widely used in home automation systems, PIR sensors detect heat traces in a room, so they are not very sensitive when the room itself is hot. Therefore, in some countries such as INDIA, PIR sensors are unable to detect human beings in the summer. This system is an alternative to commonly used PIR sensors in the home automation process.

Abstract Prosthesis is an optional alternative for the people and individuals, who have lost their legs by birth or by infections or by injury. Manufacture and alterations of prosthetic socket is possible by the weight estimation between amputee's remaining limb and prosthetic attachment. This work is to fabricate the socket and above knee prosthesis using the foot made up of polyurethane foam rubber and to distinguish the pressure, weight, position and movement of the remaining appendage as for the prosthetic attachment, and to create sensors and actuators based prostheses. Measurements for both amputed leg and normal leg is taken, and the sensors are placed at three important parts of the stump, namely ischial sheet, middle of stump and end point of stump, the maximum pressure is determined, which plays an important role in the prosthetic socket fabrication, modification and optimal fit.

Object detection plays a pivotal role in autonomous systems helps to build the machines to be intelligent as humans that leads to build an Artificial Intelligence application used for society,Industries,face-recognition and so-on. Nowadays, it is difficult to manage the waste generated by humans and industries which is increasing rapidly day by day needs to address the problem to make automation for separating the Bio-degradable and Non-Bio degradable waste. Although humans are tried to manage impact of waste management in society to maintain the eco-system by implementing a separate trash for Bio and Non-bio waste. Sometimes it is difficult to follow for the separation of waste manually by humans. There is no existing sensor to identify the types of wastes. In this proposed system, it evolves the implementation of the bio-degradable and non-bio degradable object detection method to help to detect these objects automatically with the augmented clustering NMS using Single-shot detector methods. The enhanced augmented clustering algorithm effectively detects the multiple objects in the video along with the respective bio or non-bio classification custom object detection model. With the build thousand images for each class custom dataset model to train the objects using deep learning neural network. A custom object detection data model is built with the help of NVIDIA GPU RTX 4 GB using tensorflow model. Here the results are interpreted with the mean average precision value of 0.965 with ACNMS Single shot object detector which is effectively detected with the new enhanced technique.

Objectives: To implement a hybrid feature descriptor construction (HFDC) model to exercise the CBIR. Method/analysis: The model that combines color and shape features of visual content to produce a feature level fusion scheme is introduced. The RGB histogram, HSV histogram and the canny edge histogram features are extracted and fused to produce a hybrid feature vector. Then the determined feature vector through the fusion of the entire dataset is used to train the SVM using RBF kernel to retrieve relevant visual content through identifying the color distribution, and shape is focused here as the main objective. Since from the very beginning of the data usage to surf web information the classification of the similar related objects has been potentially provided a helpful contribution towards helping the users to identify and determine required knowledge from the large corpus of available digital information. Many algorithms and improvements have been in implementation, but the large quantity of available information provides complexity to these techniques to enhance computational hike. This feature level fusion contributes to reducing the overhead. Finding: The HFDC evolutions significantly contribute to achieving an accuracy of 84.60%. The experimental results have proven the efficiency of the HFDC by providing the maximum classification accuracy. Novelty: The results evidently show that (1) HDFC improve the performance by enhancing the feature level fusion process, (2) the fusion procedure produces increased solid and high indicative rendering and (3) by performing feature level fusion of data a core dictionary is provided for better CBIR performance. Improvement: In addition to color and shape feature fusion the texture features can also be combined to improve the performance significantly.Keywords: CBIR, HFDC, RGB, HSV, Canny

In this digital era, technology has made its mark in almost every field. With the invention of prosthetic limbs and artificial organs among others have given people a better opportunity. The urbanization also has its downfalls. Traffic accidents can be considered as one. The initial stage treatment to a patient is very critical. The first hour is referred as the Golden Hour for this reason. Especially in the case of accidents the golden hour becomes very important. As per the Report on Road accidents in India 2019, the accident related deaths in India in 2019 were 1,51,113 in number [1]. The average time for an ambulance to reach hospital is going up each year due to increasing. So, it is imperative to find the possible ways to reduce the time. To achieve this, various measures are explored. In rescue times of greater than a half-hour, in half of the cases at least 21 min was required to communicate a need for an ambulance. [2] This article aims to reduce the time taken for the information to reach the ambulance services through a model which detects accidents and informs the ambulance service of the location of accidents.

This article discloses to you an approach to find and deal with the water stage in an above panzer or another box. The display units of the water degree of the panzer and robotically switch on the motors if the panzer is empty. The machine is switched off period the above panzer or box is full. At this moment, the water stage of the panzer is specifying on lcd. Working this machine, we will avoid the overabundance of the water. We're designing the circuit that is used to come across and manipulate the water stage mechanically in the suspended panzer working an arm processor microcontroller. the previously mentioned device water sensing can be completed by laborious and fast of four cables, that are positioned at representative stabilizes in the panzer. DC supply probe is placed at the bottom of the panzer. The four cables that are dipped into the panzer will testing the unique water ranges. So we should form our greatest efforts to keep masses of water in the panzer and avoid over abundanceing and now not waste it. Addressing this hassle confronted with the aid of humans, this painting focuses on slicing the water system whilst the cistern is full to detect the sensor when the water stabilizes is low. It can be powering a 1HP pump from the enter voltage, which can deliver a maximum output modern of 20A.

The objective of the present paper is to design an arm microcontroller settled totally on the ascendant whizz of the engines. Alternate the exterior of the succession of the DC engines. It using many systems in our daily existence. For instance, CPU fanatics, fume extinguishers, toy engines, and so on. All DC Engines that are worked by using a DC electricity source. More often than not we can have to alter the whizz of the autos as indicated by our prerequisite. Interfacing the DC engines to the microcontroller is a very crucial idea in Robotic programs. The embedded device the supremacy frustrate has additionally been designed and fabricated. The system cost is very low value and is appropriate for extraordinary commercial programs along with mini engines. Albeit a conjugated of frameworks have a programmed change gadget for fan whizz, not, at this point all structures have this usefulness. Along these lines, we can need to adjust the whizz of DC Engines ourselves every so often.

Energy harvesting (EH) is seen as the key enabling innovation for the mass sending of mobile ad hoc networks (MANETs) for the IoT applications. Effective EH methodologies could oust the necessities of successive energy source substitution, subsequently offering a near interminable system working condition. Advances in EH systems have moved the plan of routing conventions for EH-MANET from “energy-mindful” to “energy-harvesting-mindful.” Right now, Dynamic Congestion Control Routing Algorithm is using Energy Harvesting in MANET. The presentation of the Dynamic Congestion Control Routing Algorithm-Based MANET Algorithm scheme is evaluated using various measurements, for instance, Energy Consumption Ratio, Routing Overhead Ratio, and Throughput Ratio.

This paper presents a protection scheme against single line-to-ground fault for radial distribution network based on relay agent (RA) to discriminate the fault section. Extraction of informative fault features from zero sequence component is used for discrimination of single line-to-ground fault in the system. To obtain fault features, clusters centers are calculated using fuzzy c-means algorithm and using that space relative distance (SRD) is also calculated. The protection scheme is based on the minimum SRD. Finally, coordination of RA is performed to discriminate the faulty section from the system ensuring the better performance without affecting the healthy section.

In data mining, major research topic is frequent itemset mining (FIM). Frequent Itemsets (FIs) usually generating a large amount of Itemsets from database it causing from high memory and long execution time usage. Frequent Closed Itemsets(FCI) and Frequent Maximal Itemsets(FMI) are a reduced lossless representation of frequent itemsets. The FCI allows to decreasing the memory usage and execution time while comparing to FMIs. The whole data of frequent Itemsets(FIs) may be derived from FCIs and FMIs with correct methods. While various study has presented several efficient approach for FCIs and FMIs mining. In sight of this, that we proposed an algorithm called DCFI-Mine for capably derive FIs from Closed FIs and RFMI algorithm derive FMIs to FIs. The advantages of DCFI-Mine algorithm has two features: First, efficiency, different existing algorithm that tends to develop an enormous quantity of Itemsets all through process, DCFI-Mine process the Itemsets straight without candidate generation. But in proposed RFMI multiple scan occurs due to search of item support so efficiency is less than proposed algorithm DCFI-Mine. Second, in terms of losslessness DCFI-Mine and RFMI can discover complete frequent itemset without lapse. Experimental result shows That DCFI-Mine is best deriving FIs in term of memory usage and executions time.

Home automation is the mechanism of controlling household appliances automatically using various control system techniques. Nowadays, humans have become much dependent on electronic appliances and technology related to it. This paper gives a proposal to control home appliances using voice commands. We plan to implement an IOT-based system to automate light and fan operations using speech. A model that comes up with smart automation using Google Assistant serves this purpose. The user issues the voice command to the Google Assistant which gets interpreted and based on that the proposed system instructs the relay to switch on/off the home appliance. Adafruit and IFTT application recognize voice commands and instruct the power relay for the appliances accordingly through ESP8266 Wi-Fi. The communication between the appliances and microcontroller is established via Wi-Fi and Adafruit library; whereas, IFTT is used to control and monitor the status of devices.

Autonomous self driving vehicles are getting greater attention and this would be the future requirement in Automotive domain. However, Fail proof driving is the only solution to reduce the rate of accidents and that makes the driverless vehicles as a possible one. In man handled vehicles, by using Advance Driver Authorization System (ADAS), accident free driving can be ensured. This paper focuses on one of the ways to contribute towards accident free driving of autonomous vehicles by deploying a novel Lane Keep Assist (LKA) system. A Machine Learning algorithm has been used in proposed LKA system for tracking the lane of the autonomous vehicles by providing the required inputs. Proposed LKA system has been demonstrated in Matlab/Simulink platform and the results have been presented in this paper.

Adaptive Neuro Fuzzy Inference System (ANFIS) is a well proven technology for predicting the output based on the set of inputs. ANFIS is predominantly used to track the set of inputs and output in order to achieve the target. In this paper, authors have proposed a Nonlinear ANFIS algorithm to track the distance between the autonomous vehicle and the obstacle while vehicle is moving and the brake force required. By tuning neuro fuzzy algorithm, accurate brake force requirement has been achieved and the results are captured in this paper. Back propagation algorithm based neural network & Sugeno model based Fuzzy inference system have been used in the proposed technique. Matlab/Simulink software platform is used to implement the proposed algorithm and proven the expected results.

Mechanical fasteners are widely used in manufacturing of hardware and mechanical components such as automobiles, turbine & power generation and industries. Object detection method play a vital role to make a smart system for the society. Internet of things (IoT) leads to automation based on sensors and actuators not enough to build the systems due to limitations of sensors. Computer vision is the one which makes IoT too much smarter using deep learning techniques. Object detection is used to detect, recognize and localize the object in an image or a real time video. In industry revolution, robot arm is used to fit the fasteners to the automobile components. This system will helps the robot to detect the object of fasteners such as screw and nails accordingly to fit to the vehicle moved in the assembly line. Faster R-CNN deep learning algorithm is used to train the custom dataset and object detection is used to detect the fasteners. Region based convolutional neural networks (Faster R-CNN) uses a region proposed network (RPN) network to train the model efficiently and also with the help of Region of Interest able to localize the screw and nails objects with a mean average precision of 0.72 percent leads to accuracy of 95 percent object detection

Molecular Property Diagnostic Suite ( $$\text {MPDS}^{\mathrm{TB}}$$ ) is a web tool ( http://mpds.osdd.net ) designed to assist the in silico drug discovery attempts towards Mycobacterium tuberculosis (Mtb). $$\text {MPDS}^{\mathrm{TB}}$$ tool has nine modules which are classified into data library (1–3), data processing (4–5) and data analysis (6–9). Module 1 is a repository of literature and related information available on the Mtb. Module 2 deals with the protein target analysis of the chosen disease area. Module 3 is the compound library consisting of 110.31 million unique molecules generated from public domain databases and custom designed search tools. Module 4 contains tools for chemical file format conversions and 2D to 3D coordinate conversions. Module 5 helps in calculating the molecular descriptors. Module 6 specifically handles QSAR model development tools using descriptors generated in the Module 5. Module 7 integrates the AutoDock Vina algorithm for docking, while module 8 provides screening filters. Module 9 provides the necessary visualization tools for both small and large molecules. The workflow-based open source web portal, $$\text {MPDS}^{\mathrm{TB}}$$ 1.0.1 can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular. SYNOPSIS: A web-based $$\text {MPDS}^{\mathrm{TB}}$$ Galaxy tool is developed for assessing therapeutic potential of molecules. $$\text {MPDS}^{\mathrm{TB}}$$ is categorized into Data Library, Data Processing and Data Analysis. It can be a potential enabler for scientists engaged in drug discovery in general and in anti-TB research in particular.

A Discrete Wavelet Transform (DWT) based multiple watermarking technique for images using Back-Propagation neural networks (BPNN) are proposed. The successive/re- watermarking is one of the best method in multiple watermarking techniques. In successive/re-watermarking method, the various watermarks are embedded and extracted one by one. The wavelet coefficient is selected based on the weight factor by using Human Visual System (HVS). The BPNN is incredibly well-liked in neural networks and its variety of supervised learning neural networks. The two watermarks are embedding into the original image using improved BPNN, which can advance the speed of the erudition, reduce error and the trained neural networks be capable of extracting the two watermarks from the embedded images. The simulation results show that the proposed work achieves good quality on the embedded images and more robustness on extracted two watermarks.

This research work presents the influence of the rear wheel, in the Triangular Structured Omni-directional Mobile Robot (TSOMR). In this robot, two Dual Row Omni-directional wheels are fixed to the specially designed Triangular chassis having 60° apex angle and rear wheel influence is analysed. To achieve the forward motion, the Omni-directional Wheels in Left and Right side of the TSOMR have to rotate in the same sense and the same speed. In the previous research works to achieve the forward motion only left and right side wheels' motion have been considered. Hence this research work targets the influence of the rear wheel in the movement of TSOMR. The experimental analysis has been carried out by equipping the rear wheel as the same Dual Row Omni-directional Wheel, Dual Swivel Caster Wheel, Ball Caster wheel, and Six Wheel Casters. This Mobile Robot has been moved on a flat cement floor up to 5 meters and deviations from the desired path have been recorded. The reasons for the deviations have been addressed in this paper.

At present all the works of human beings are replaced by the robots. Generally robotics are classified into service robotics and industrial robotics. Nowadays all fields are occupied by robotics including, hospitals, agriculture, defense, hazardous environment and office. The Robots are used where ever human does not do their work efficiently and safely such as handling poisonous and explosive products in industries. The direction of the robotic vehicle and the spraying of water in the fire is controlled by the voice command. The communication between the vehicle and humans are established through the NODE MCU and ARDUINO. The vehicle consists of three major components such as the NODE MCU, ARDUINO, and water level indicator (on vehicle). This Robotic vehicle is involved to rescue the human beings and extinguishing the fire where fire fighters are not able to enter into the fire accidental area.

In the recent times, there has been an increasing demand for efficient strategies in the field of data assimilation about groundwater. Data mining process is a discovery of hiding information that utilizes the prediction efficiently by stochastic sensing concept. This paper proposes an efficient assessment of groundwater level, rainfall, population, food grains and enterprises dataset by adopting stochastic modeling and data mining approaches. Firstly, the novel data assimilation analysis is proposed to predict the groundwater level effectively. Experimental results are done and the various expected ground water level estimations indicate the sternness of the approach.

Data mining is the way of separating data from stunning perspectives, and along these lines, abstracting them into important information which can be used to construct the yielding and improvement possible results in cultivating. The destinations have been assented of solidness in paddy advancement and to expand the development of creation in a maintainable way to meet the nourishment prerequisite for the developing populace. In any farming fields, it for the most part, happens that at whatever point the choices in regards to different methodologies of arranging is viewed as, for example, season-wise rainfall, region, production and yield rate of principal crops, and so forth. In this paper, it is proposed to discover the forecast level of concentration in paddy improvement for different years of time series data utilizing stochastic model approach. Numerical examinations are outlined to help the proposed work.

Black hole attack refers an attack by single or more number of malicious nodes which forcibly captures the route from source to destination by sending reply with largest sequence number and smallest hop count. In this paper, a novel technique using Localized Secure Architecture for MANET (LSAM) routing protocol is proposed to detect and prevent co-operative black hole attack. Security Monitoring Nodes (SMNs) would be activated only if the threshold value is exceeded. If malicious nodes are detected, other SMNs in its proximity area are intimated to isolate the malicious nodes. Network simulator tool is implemented to analyze the network performance of different scenarios with various number of nodes. Packet delivery ratio (PDR), routing overhead, control overhead, packet drop rate, throughput and end-to-end delay (EED) are the factors taken into consideration for performance analysis and it is shown that the proposed protocol is more secured and efficient. PDR is been increased by 27 % in the presence of 40 % misbehaving nodes, while it increases the percentage of overhead on proposed routing protocol from 1 to 4 %. EED is greatly reduced from 0.9 to 0.3 % in LSAM.

With huge amounts of biomedical data being generated day by day extracting statistical information about the chemicals mentioned in such huge databases manually is tedious and time consuming. Our system is mainly designed for naive users, which aims to automate data collection and knowledge extraction from chemical literature in a user friendly and efficient way on the hadoop platform. The system downloads the abstracts related to the disease of interest from Pubmed database. The text of the abstracts is then extensively parsed for chemicals such as protein/gene names and chemical compound names and classified into different classes. This analysis would prove to be helpful in various biomedical and pharmaceutical industries. The extraction of important information will be done using the Ling Pipe API wherein a training dataset is given to this Ling Pipe which classifies the extracted bioentities in the respective classes. The system being deployed on hadoop platform provides a scalable and distributed system which processes huge number of abstracts in a short time and with high efficiency. The system also provides a user friendly user interface for easy use of the hadoop system for non technical users.

In the present work, the multiple watermarking techniques have been executed to present information security. DWT - SVD based multiple watermarking for color images are proposed and has been implemented in cloud computing. The multiple watermarking is used to share the copyright of multiple users improves the robustness and high security. In the proposed work, multiple watermarks are embedded into the cover image. Image quality is computed using Normalized Correlation (NC) and Peak Signal to Noise Ratio (PSNR) for color images. The proposed scheme provides a secure and well imperceptibility to different image processing attacks.

Electric vehicles are increasing their market share from year to year due to increasing concerns over global warming, to seek freedom from oil based transportation. Considering Electric Vehicles (EVs) as an alternative for Internal Combustion Engines, it is essential to characterize electric vehicles in terms of real world usage and to overcome the range anxiety associated with electric vehicles. In this point of view, an attempt has been made to analyze the real world drive pattern to predict the battery current with multiple linear regression model.

This study has been done to find whether there is a significant relationship between corporate social responsibility rating and tax avoidance. Sample includes 79 companies based on availability of ratings data. Companies financial data was collected from Prowess for the year 2004 to 2017. CSR data was collected fromCSRHub. CSRHub is third party site where we can collect CSR ratings data. Regression analysis was used to analyze the significance between corporate social responsibility rating and tax avoidance. Our results suggest that companies with higher CSR ratings have lower involvement in tax avoidance practices.Companies that are showing higher commitment towards environment are more tax responsible.

The proposed system introduces new social network privacy management models and it measures their human effects. Here it introduces a mechanism using clustering techniques which helps users to group their friends using policy management. Then it introduces new privacy management model which will give policies to other friends to find similar friends in the network. And thereby explored various ways that help users to find example friends. In addition, it will help to find privacy management models which can be further enhanced and also it helps to detect privacy sentiment of user. Assistant friend grouping will be done for effective friendship establishment. In a network user privacy will be maintained by setting privacy techniques. Privacy management models can be routinely customized to the privacy sentiment and done according to the needs of the user.

This approach has taken a review of time slot based password technique in which the password is used only once for each and when time slot ends the password is not useful. The proposed time slot based password scheme uses Text time slot based password. The time slot based password scheme uses pair-based authentication scheme. Textual passwords generally used for login authentication. Graphical password is introduced exactly opposite technique to textual passwords. As most users are well known about textual passwords than pure graphical password.

Objectives: This paper elucidates the analytical study of the diversity schemes and the channel estimation techniques. Methods/Analysis: The future generation wireless communication systems require high speed data connection with lower bandwidth usage and high QoS with minimal system complexity. So, Multiple-Input Multiple-Output (MIMO) is one of the best and suitable solutions for future wireless system demands and not disturbing the bandwidth. MIMO offers spatial multiplexing and diversity gains. Findings: The combination of Orthogonal Frequency Division Multiplex (OFDM) with MIMO enables the users to get the advantages of both OFDM and MIMO and Spatial multiplexing concept is used in this system to increasing the spectral efficiency of a system. The signal fading is caused by destructive interference. Novelty/ Improvement: To improve the signal quality, we can use the different diversity schemes and channel estimation techniques and discussed in this paper detail.

In the recent world with the advances in multimedia technologies such as compression, display, and visualization technologies and the increased emphasis on multimedia application, the production of image information has resulted in large volume of images that need to be properly indexed for retrieval in future. Hence, there is a need for Content Based Image retrieval application which makes the retrieval process very efficient. Current systems generally make use of low level features like colour, texture, and shape. In this paper, a novel approach for generalized image retrieval based on semantic contents is presented. A combination of two feature extraction methods namely colour and edge histogram descriptor is proposed. The retrieval efficiency is computed and compared by using four methods such as k-means, colour histogram, edge histogram and sobel method. For colour, the histogram of images is computed and for edge, edge histogram descriptors (EHD) are found. For retrieval of images, a novel idea is developed based on greedy strategy to reduce computational complexity. The proposed system stores the content of database images automatically and query image’s content is extracted during runtime and it is used to match against those in database. The result of the query is a set of images that are similar to the query image.

Solar power is widely available around the globe but efficient transfer of solar power to the load becomes a challenging task. There are various methods in which the power transfer can be done, the following work proposes a method for efficient tracking of solar power. MPPT [ maximum power point tracking] algorithm applied on three phase voltage source inverter connected to solar PV array with a three phase load. MPPT is applied on inverter rather than conventionally applying MPPT on DC-DC converter. Perturb and Observe method is applied in the MPPT algorithm to find the optimal modulation index for the inverter to transfer maximum power from the panel. Sine pulse width modulation technique is employed for controlling the switching pattern of the inverter. The algorithm is programmed for changing irradiation and temperature condition. The system does not oscillate about the MPP point as the algorithm set the system at MPP and does not vary till a variation in irradiation is sensed. The proposed system can be installed at all places and will reduce the cost, size and losses compared to conventional system.

Saliency detection is one of the prominent areas of research since it is used in many computer vision applications. In this paper a visual saliency model is proposed which is bottom-up approach that is used to detect salient regions of an image. The proposed system combines two properties to find the salient regions. First, the proposed system uses a special image descriptor called image signature which approximates the foreground of the image and the foreground separated image is filtered to highlight visually salient regions. Secondly, it utilizes the property of human visual system called human visual acuity to enhance the saliency map. This saliency approach predicts human fixations in a better way than other existing methods on Bruce and Tsotsos[1] benchmark dataset.

Economic load dispatch (ELD) problem is an important issue in the operation and control of modern control system. The ELD problem is complex and nonlinear with equality and inequality constraints which makes it hard to be efficiently solved. This paper presents a new modification of harmony search (HS) algorithm named as dynamic harmony search with polynomial mutation (DHSPM) algorithm to solve ORPD problem. In DHSPM algorithm the key parameters of HS algorithm like harmony memory considering rate (HMCR) and pitch adjusting rate (PAR) are changed dynamically and there is no need to predefine these parameters. Additionally polynomial mutation is inserted in the updating step of HS algorithm to favor exploration and exploitation of the search space. The DHSPM algorithm is tested with three power system cases consisting of 3, 13, and 40 thermal units. The computational results show that the DHSPM algorithm is more effective in finding better solutions than other computational intelligence based methods.

The objective of the research work is to propose a trigger-action-reaction (TAR) based impact analysis model with the outcome due to Artificial Energy Drinks (AED) on Indian youth. The AED causes a drastic behavioural changes leading to potential health risks in India and the studies suggest that young people or college students are therefore vulnerable to increased problems from ingesting these products. The young community is more likely to take risks than adults and to suffer high rates of alcohol problems, including alcohol-related traffic accidents, violence, sexual assault, and suicide as per the studies. The reaction induces faster metabolism in youth by AEDs resulting to uncontrollable emotional outbursts leading to different health and legal problems. A parallel action reaction based impact analysis with number of AEDs sold out in India that results in criminal activities due to massive consumption of AEDs by the society. A formal model of trigger-action-reaction is applied and validated by multi-core programming using CUDA.

The purpose of Text Mining is to process unstructured (textual) information, extracting meaningful numeric indices from the text, and, thus, make the information contained in the text accessible to the various data mining (statistical and machine learning) algorithms. Information can be extracted to derive summaries for the words contained in the documents or to compute summaries for the documents based on the words contained in them. Hence, we can analyze words, clusters of words used in documents, etc., or we can analyze documents and determine similarities between them or how they are related to other variables of interest in the data mining project. This topic that might be integrated with massive files of very disparate joke clusters, bound only because someone thought that they were funny or someone had an interesting thing for light-bulb jokes. Therefore, there are probably tons of doubles and triples and what you have in this collection, and there's just not enough time in the day to sort them, manually. This paper is going to analyze the different variation and analysis of funny or humorous words using data mining techniques.

The selection of reverse logistics service provider (RLSP) can bring economic benefits. Considering the current development of reverse logistics in manufacturing, process and domestic appliances industries, this paper proposes a set of factors influencing selection of service provider and uses a DEMATEL method to establish relation matrixes reflecting the relationships among those factors. The interaction among the criteria is analysed before arriving at a decision for the selection of RLSP. The research enables better understanding of the complex relationships of the key attribute in the decision making environment and improves the reliability of the decision making process.

The world of Ethernet has experienced a number of changes in the last few decades. The introduction of new protocols and new ways of data transfer has caused major changes in the Ethernet world. With the current trend, there will be a time when data is moved from various datacenters at less cost. Efficient protocols would be proposed to see this change in data migration. Performance and delay issues would be a thing of the past. Existing technologies will continue to play a very dominant role in these situations. This is because they have worked well in old protocols, and have very promising features which worked well when incorporated into the current protocols produce great results. This paper proposes one of the ways by which this can be achieved. We attempt to combine two emerging protocols which have changed the phase of networking in these few years. These are, Shortest Path bridging and Network Coding. A combination of the two can be very promising as they both offer very appealing properties to the world of Ethernet technology. We first observe the properties offered by shortest path bridging and optimize its performance using network coding.