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The challenging task of wireless sensor network is to increase the lifetime of the network as they are equipped with critical battery power. Many protocols were proposed to efficiently use the battery power to extend the lifetime of the wireless sensor network. For optimizing the battery power of the sensor network, various energy efficient routing strategies are applied. In this paper, we studied and reviewed popular routing protocols LEACH, DEEC, DDEEC, EDEEC and EDDEEC as they use their own algorithm for energy efficiency. They use probability based cluster head selection, as a result, the nodes having low battery power may be selected as cluster head and the nodes having high battery power may not be selected as cluster head. This creates unbalancing condition in wireless sensor network for network lifetime enhancement point of view. Also, there are no observation on current energy level of the sensor nodes. To address this limitation, we proposed energy level estimated reactive clustering routing (ELERCR) algorithm in wireless sensor network which uses the concepts of energy level observation of nodes of cluster head selection. ELERCR uses ratio of current energy to initial energy for selection of cluster head in wireless sensor network. Simulation result shows that performance of our protocol gives significant energy efficiency and more network lifetime compared to other protocols.

Wireless Sensor Networks, Clustering, Energy Efficiency, Stable Election, Network Lifetime

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Saraswati Bhargava, Manoj Lipton, Himanshu Yadav, "Energy Level Estimated Reactive Clustering Routing Protocol in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1015-1022, 2019.

Face Recognition system identifies a person by comparing numerous face images accumulated in database records. Face Recognition is simply matching human beings by their faces. This technology has augmented in the field of security and law enforcement to track down criminals and terrorists. In our method, we use deep convolution neural network (deep CNN) and Euclidean distance for extracting the feature from face images. Euclidean Distance used for counting distance between images. We have used FEI dataset for face recognition. This paper gives brief information about face recognition techniques like OpenFace, EigenFace, LBPH, Fisher-Face, and Deep CNN. This paper contains basic information about CNN architecture like AlexNet, GoogleNet, VGGNet, ResNet, SENet, etc. that are used to recognize any type of pose variation in the image. CNN architecture plays an important role to achieve the best accuracy. This paper also focuses on some publicly available datasets: CelebFace (2014), Facebook (2014), Google (2015), MegaFace (2016), MS-Celeb-1M (2018).

Face Recognition, Face detection, Tensorflow, CNN architectures, Datasets, deep CNN

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Kashyap Patel, Miren Karamta, M. B. Potdar, "Performance Analysis and Evaluation of Face Recognition using Deep Convolutional Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1023-1031, 2019.

As the WORLD WIDE WEB (WWW) has been drawing in more business and transaction to be actualized over the Internet, it also has attracted web server attackers having malicious intends. Among different attacks possible against the web server, Distributed Denial of Service (DDoS) attack is the most harmful kind of thread, as it is hard to detect and dispose of completely. The use of spoofed IP addresses to launch DDoS makes it even much harder to identify the source of an attack. In DDoS, attack traffic resembles to legitimate traffic and difficult to differentiate. Still, there are some features which can be used to differentiate normal request from the malicious ones. The source IP distribution is one of the features. In the proposed mechanism a data structure is maintained at victim server which is used to decide priority among different requests during DDoS based on previous visit history and source IP distribution of that server. In the descending order of priority, the server serves the request of users at the time of DDoS.

DDoS; IP spoofing; Source distribution; Priority; Defence

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Lavlish Goyal, Nitika, "Mitigating the Effect of Distributed Denial of Service Attack," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1032-1035, 2019.

Clustering is the most broadly used performance resolution for Mobile Ad Hoc Networks enabling their scalability for a high quantity of mobile nodes. The intend of clustering schemes is pretty complex, appropriate to the very lively topology of such networks. The Proposed system is when the new node is entered the cluster in sometimes the fresh node will be the cluster head (CH). Because the head node can communicate to gateway in every transmission at the time cluster head energy level is decrease. When the clustering head reached in small energy level doesn’t to broadcasting the packets the new node will become a CH. Because the fresh node have a maximum level of energy. So the proposed algorithm CAODV is increased packet delivery ratio better performance than the existing Distributed Weighted Clustering Algorithm (DWCA) using MATLAB simulink.

MANET, CAODV Clustering, DWCA, Packet Delivery Ratio, Energy Consumption

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J. Vijayalakshmi, K. Prabu, "Analysis of Energy Based Cluster Head Selection Using CAODV Algorithm in MANET," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1036-1039, 2019.

In this paper authors describe about time and frequency division multiple access protocol (TFDMA). It is a new combinational MAC layer protocol for wireless sensor networks in which, the first effort to consider the working of both TDMA and FDMA schedules in the network full of constraints. Also, at the same time by allowing it to operate in an energy-efficient collision-free manner. However, TFDMA considers the multiple frequencies provided in the radio’s of recent sensor node hardware platforms. Authors also show how TFDMA performs protocols providing high throughput and small bounded end delay suitable for new introducing types of sensor network applications such as real-time voice streaming etc.

Scheduling, Tree based network, TFDMA, Wireless Sensor Network, Data Collection

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Vishal Bhatt, Namrata Dhanda, Kapil Kumar Gupta, "An Effective Approach for Tree Based Data Collection in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1040-1045, 2019.

VANET is a rapid development of wireless communication technologies; give now the design of this technology in vehicles, in order to enhance intelligent transport systems by great advantages that could be derived from this technology with the objective of enhance the variability of traffic and improve RS (Road Safety), this kind of network gives communication between vehicles or between vehicles and roadside access points. In existing work, problem formulation defined security in VANET plays a vital role. VANET normally refers to a wireless network of mixed sensors or other computing devices that are deployed in vehicles. This type of network enables constant observing and sharing of road conditions and status of the transportation systems. AODV is the most normally used topology based routing procedure for VANET. Using of broadcast packets in the AODV route discovery phase caused it is tremendously susceptible against DoS and DDoS flooding attacks. In proposed work, a method using ontologies and vehicle data traffic management or information to ensure the data transmission of packets as soon as possible and in the most reliable way. Simulation tool used MATLAB 2016a to evaluate the performance metrics like as a packet delivery rate, throughput, End to End Delay and Overhead. In research work, proposed in B-AODV and PSOA algorithm, to improve the communication range, minimum overhead and packet delivery rate.

VANET (Vehicular Ad-Hoc Networks), B-AODV (Balanced Ad-Hoc On Demand Distance Vector), DDoS (Distributed Denial of Service) and PSOA (Particle Swarm Optimization Algorithm).

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Paramjeet Kaur, Shelly, "Performance Enhancement in VANET using Balance Vector Protocol and Nature Swarm Algorithm," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1046-1052, 2019.

This paper presents a mechanism for flower species identification using machine learning’s regression algorithm. The main objective behind this proposed approach is to utilize flowers and garlands contributing towards the waste management of temples in rural as well as urban areas. The proposed approach provides both social and environmental aspect as the waste flowers can be used for vermicomposting to improve the quality of soil as well as the other types of flowers and can be used for creating organic colour and dyes.

Flower Species Identification, Machine Learning Algorithm, Regression Algorithm.

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Rupesh Bante, Dipak Nakhate, Praful Gade, Saurabh Tagde, Swati Dixit, Gopal Sakarkar, Sofia Pillai, "Image Analysis and Classification of Flower Using Machine Learning Algorithm for Creating Organic Color," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1053-1058, 2019.

Early diagnosis of Alzheimer’s Disease is important for the progress of more predominant treatments. Machine learning (ML), a branch of artificial intelligence, provides a variety of probabilistic and upsurge techniques that permits PCs to gain from vast and complex datasets. As a result, researchers concentrate on using machine learning often for diagnosis of early stages of Alzheimer’s Disease. This paper represents a review, analysis and critical evaluation of the recent work done for the early detection of Alzheimer’s Disease using Machine Learning techniques. Several methods achieved promising prediction accuracies, however they were calculated on different pathologically unproven data sets from different imaging modalities making it difficult to compare among them. Moreover, many other factors such as pre-processing, the number of important attributes for feature selection, class imbalance distinctively affect the computation of the prediction accuracy. To overcome these flaws, a model is proposed which comprise of initial pre-processing step followed by imperative attributes selection and classification is achieved using association rule mining. Furthermore, this proposed model-based approach gives the right path for research in early diagnosis of AD and has the potential to distinguish AD from healthy controls.

Alzheimer’s, Mental Disorder, Association Rule Mining

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[6] Authors-Ranjan Duara; Malek Adjouadi, Chen Fang ; Chunfei Li ; Mercedes Cabrerizo ; Armando Barreto ; Jean Andrian ; David Loewenstein A Novel Gaussian Discriminant Analysis-based Computer Aided Diagnosis System for Screening Different Stages of Alzheimer`s Disease, Published in: 2018 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE); Conference Location: Washington DC, USA.
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Yash Shetty, Linda John, Vikrant Patil, "Prevention of Alzheimer’s Disease using Decision tree and Association rule mining Algorithms," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1059-1064, 2019.

In this information age, Internet is growing exponentially with large amount of information through social media networks like Facebook, Blogs, Twitter, LinkedIn, etc. Most of the text we have seen in Internet are anonymous in nature. Analysis of such kind of text became crucial nowadays. Author Profiling is a technique which is used to analyse the anonymous text in Internet for finding out the characteristics of author like age, gender, country, native language, educational background etc. Style of writing of each author is utilized for the analysis of different characteristics of author’s profile. Researchers experimented with different types of features to improve the accuracy of prediction. The final accuracy of prediction depends on the feature which is extracted and on the machine learning algorithm used for prediction. The various application domains of author profiling are forensics, security, marketing and education. In this paper the various author profiling approaches and techniques are explained and their performances are analysed.

Author Profiling, Stylometric Features, Machine Learning Algorithms, Features, Accuracy

[1] Braja Gopal Patra, Kumar Gourav Das, and Dipankar Das, “Multimodal Author Profiling for Twitter,” Notebook for PAN at CLEF, 2018.
[2] Raju.Nadimpalli.V. G, Gopala Krishna. P, Yelleni Mounica, V Sahithi,” Authorship Profiling in Gender Identification on English editorial documents using Machine Learning Algorithms” International Journal of Engineering Trends and Technology (IJETT), April 2017.
[3] Dang Duc Pham, Giang Binh Tran, Son Bao Pham, “Author Profiling for Vietnamese Blogs”, International Conference on Asian Languages Processing, 2009.
[4] Roy Bayot, Teresa Goncalves, “Multilingual Author Profiling using Word Embedding Averages and SVMs”, 10th International Conference on Software, Knowledge, Information Management & Applications (SKIMA),2016.
[5] Satya Sri Yatam, T. Raghunatha Reddy, “Predicting Gender and Age from Blogs, Reviews & Social media”, International Journal of Engineering Research & Technology (IJERT) ISSN: 2278-0181, Vol. 3 Issue 12, December-2014.
[6] K Santosh, Romil Bansal, Mihir Shekhar, and Vasudeva Varma, “Author Profiling: Predicting Age and Gender from Blogs”, Notebook for PAN at CLEF, 2013.
[7] Yaritza Adame-Arcia, Daniel Castro-Castro, Reynier Ortega Bueno, Rafael Muñoz, “Author Profiling, instance-based Similarity Classification”, Notebook for PAN at CLEF ,2017.
[8] Ma. Jos ́e Garciarena Ucelay, Ma. Paula Villegas, Dario G. Funez, Leticia C. Cagnina1, Marcelo L. Errecalde, Gabriela Ram ́ırez-de-la-Rosa, and Esa ́u Villatoro-Tello,” Profile-based Approach for Age and Gender Identification”, Notebook for PAN at CLEF 2016.
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Vivitha Vijayan, Sharvari Govilkar, "A Survey on Author Profiling Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1065-1069, 2019.

Image segmentation is a very challenging task in digital image processing field. It is defined as the process of takeout objects from an image by dividing it into different regions where regions that depicts some information are called objects. There are different types of image segmentation algorithms. The segmentation process depends upon the type of description required for an application for which segmentation is to be performed. Hence, there is no universally accepted segmentation algorithm. This method is applied to many color images and experimental results show the effectiveness of the method.

image segmentation, edge detection, smoothness, seed selection, region growing, region merging

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A.V. Anjikar, K. Ramteke, S. Chauvan, "Color Image Segmentation using Region Growth and Merge Improved Technique," International Journal of Computer Sciences and Engineering, Vol.7, Issue.3, pp.1070-1072, 2019.