Volume-7 , Issue-6 , Jun - 2019 Go Back

Abstract— The IOT concept came into existence in 1999 and from there it has grown into one of the most important parts of the new fields and technologies sector. Artificial Intelligence being included to the IOT systems is the next step and it is being used in daily life. This technology is used in various fields and it is a wide range concept which can be used anywhere. Artificial intelligence is the first and most popular option to manipulate huge data outflow and storage within the IOT community. Currently IOT is most popular with cutting edge sensors and high speed internet which are all included in a microcontroller. The streams of data flowing through Internet gather sensor and customer data which are sent and attain by terminals or workstations. With sudden popularity of cutting edge sensors and workstations, some data can have problems on storage, delays, routing problems with its network traffic etc. To avoid all these problems, many techniques have been recommended in the past 4 years and in that AI algorithms are the most accurate solution to the data mining, management and control in handling network traffic. When the Internet of Things is power-driven by AI, then it is known as Intelligent IOT. In this paper we will review the basic components of Intelligent IOT and some vital system fields where it is used. We examine the role of AI strategies to enable such networks with intelligent communication. This paper is targeted on evaluating current solutions for the intelligent IOT connected and communicating with each other. It conveys AI techniques and algorithms which are used to create such intelligent IOT, and network solutions to use the advantages given by such capabilities.

IoT Applications, Smart Homes, Smart Buildings, Smart Manufacturing, Smart Healthcare, Intelligent IOT, Artificial Intelligence, Machine Learning

[1] Aneta Maranda, Daniel Kaczmarek, “Selected methods of artificial intelligence for Internet of Things conception”, 2015.
[2] Artur Arsenio, Fernando Nabais, “Internet of Intelligent Things: Bringing Artificial Intelligence into Things and Communication Networks”, August 2014.
[3] Bharti Nathani, Rekha Vijayvergia, “The Internet of Intelligent Things: An Overview”, Dec 2017.
[4] Zeinab Kamal Aldein Mohammed, Elmustafa Sayed Ali Ahmed, “Internet of Things Applications, Challenges and Related Future
Technologies”, Jan 2017.
[5] Pallavi Sethi, Smruti R. Sarangi, “Internet of Things: Architectures,
Protocols, and Applications”, Jan 2017.
[6] https://www.pwc.com/gx/en/industries/communications/assets/pwc-ai- and-iot.pdf

Suhasini Vijaykumar, Avinash Yadav, "Artificial Intelligence Powering Internet of Things," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.449-456, 2019.

In this paper, reconstruction of speech signal is performed using a novel technique Empirical Mode Decomposition (EMD). EMD is mainly applicable for non-linear and non –stationary signals and therefore applicable for speech which is non linear and non-stationary. EMD is applied for finding the glottal source signal of speech which provide us the source information and then vocal tract filter is found out and original speech is reconstructed. Speech samples are collected from different Assamese dialect and the experimental result derived establishes the effectiveness of the proposed method

Speech, Empirical Mode Decomposition (EMD), Intrinsic Mode Function (IMF), Fourier Transform (FT)

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[13] N. Goswami, M. Sarma and K.K.Sarma, "Empirical Mode Decomposition Based Reconstruction of Speech Signal in Noisy Environment", IEEE International Conference on Signal Processing and Integrated Network, Noida, India, 2014.
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Nisha Goswami , "EMD Based Speech Reconstruction for Different Assamese Dialect," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.457-461, 2019.

This system uses Context-Aware Local Binary Feature Learning (CA-LBFL) Method for face recognition. Learning based methods such as DFD and CBFD learn features representation from raw pixel and they are more susceptible to noise where existing local feature descriptors are hand crafted and they require strong prior knowledge and heuristic. Proposed system uses contextual information for face recognition because context provides strong prior knowledge. It helps to enhance the robustness and stableness of various visual analysis tasks. to jointly learn multiple projections matrices for mapping we make use of context-aware local binary multi-scale feature learning (CA-LBMFL), where each projection matrix corresponds to a specific scale of pixel difference vector (PDV). PDVs are extracted from image and stored in a text file in the binary form. Face recognition is performed on the basis of this extracted features. For heterogeneous face matching we implement coupled learning methods based on CA-LBFL and CA-LBMFL. Experimental result is based on two widely used datasets LWF and YTF.

Face representation,Face recognition,Heterogenous face,Context-Aware,Binary feature learning

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Sushmitamai K. Ahire, Nilesh R. Wankhade, "Context-Aware Local Binary Feature Learning : An Approach For Face Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.462-465, 2019.

Person Re-identification (re-ID) is a critical problem in video analytics applications such as security and surveillance. Although many approaches have been proposed, it remains a challenging problem since persons appearance usually undergoes dramatic changes across camera views due to changes in view angle, body pose and background clutter. Person re-id aims to retrieve a person of interest across spatially disjoint cameras. The system focuses on tackling the person re-ID problem with the proposed metric learning scheme. There is a discriminant metric learning strategy for this testing issue. Most existing metric learning algorithms, it takes both original data and auxiliary data during training which is motivated by the new machine learning paradigm - Learning Using Privileged Information. This system is based on features aggregation. Image dataset is load and the basic operation is performing that is to convert those load images into gray scale. And also create the HOG (Histogram of oriented gradient) descriptor, in this features extraction task completed based on EHD (Edge of histogram descriptor), CLD (Color Layout descriptor), and SCD (Scale Color descriptor). The system aggregates all Features and Generate Train metric. After that an unknown image is load which is comes through gray scale process and HOG descriptor. Classify that images and identify the correct image. Such system is used in many sectors for security purpose

Person Re-identification, Metric Learning, Feature Aggregation, HOG descriptor

[1] Xun Yang, Meng Wang, Dacheng Tao, “Person Re-identification with Metric Learning using Privileged Information”, IEEE transactions on Image processing, 2017.
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Approximate rank component analysis”, in European Conference
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Madhavi Dayaram Bhamare, Nilesh R. Wankhade, "Person Re-identification with feature Aggregation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.466-469, 2019.

This paper proposes a new classifier named Extreme Learning Machine (ELM) on a hybrid framework for developing a Content Base Image Retrieval (CBIR) system to improve the accuracy problems faced with the earlier image retrieval system. This system mainly aims towards the accuracy with less consumption of time. In this system, Wang database is used with Local Binary Pattern (LBP), color moment, canny edge and region props for the extraction of texture, color, edge and shape feature respectively. After extracting all the features from the image, distance matrix will be determined to use it for further implementation. And then ELM classifier is used in this proposed CBIR to categorize all the images. Score Level Fusion is used as similarity measure for finding similar images. The obtained results proved that the accuracy and efficiency of CBIR system increased at a very high rate after using ELM classifier in terms of precision, recall, f-measure and retrieval time than just using similarity measure of the extraction features. The elapsed time and the average precision value is 0.277391 and 97.2500 respectively which is much accurate than the state-of-the-art techniques.

CBIR, color moment, canny edge detection, Local Binary Pattern(LBP), Extreme Learning Machine (ELM), Score Level Fusion

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Kajol Dahiya, Gaurav Gautam, "Content Based Image Retrieval System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.470-475, 2019.

In agriculture, despite large-scale funding with extension of irrigation services and it is a major concern that many sectors are facing deficits in management of water. Irrigation system is one of the major aspects to be enriched meeting the economic and sustainable challenges of the farmers. Recent trends in the area of Wireless Sensor Networks (WSN) have influenced a wide implementation of various applications in the area of precise agriculture. WSNs for environmental condition monitoring with defined knowledge are used for estimating crop growth and yield properties. The proposed system automates the irrigation and fertigation using WSN to make comparatively high yield than the traditional methods. Irrigation scheduling is estimated by use of WSNs real time monitoring of weather and soil properties. The exigent need for solving the constraints, Evapotranspiration (ET) system is integrated with irrigation module which uses Penman-Monteith FAO-56 model for calculating crop water need. The disadvantages of traditional agricultural systems remove by utilizing water resource efficiently. As a result, the proposed system helps in water conservation to a great extent and also reduces soil erosion as only the required fertilizers are injected via the drip system. The paper also includes the implementation and results of surface drip irrigation and sub-surface drip irrigation are implemented in maize and sugarcane field respectively.

irrigation System, WSN, Crop Selection

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M. M. Sardeshmukh, Vrushali Warkhedkar, "Review on Smart Drip Irrigation and Fertigation using IOT & WSN," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.476-478, 2019.

The paper proposes a novel picture portrayal for surface characterization. The ongoing headways in the field of fix based highlights compressive detecting and highlight encoding are joined to plan a hearty picture descriptor. In our methodology, we initially propose the neighbourhood highlights, Dense Micro-square Difference (DMD), which catches the nearby structure from the picture patches at high scales. Rather than the pixel we process the little squares from pictures which catch the miniaturized scale structure from it. DMD can be figured productively utilizing vital pictures. The highlights are then encoded utilizing Fisher Vector strategy to get a picture descriptor which thinks about the higher request measurements. The proposed picture portrayal is joined with straight SVM classifier. The analyses are led on the standard surface datasets (KTH-TIPS-2a, Brodatz, and Curet). On KTH-TIPS-2a dataset the proposed strategy beats the best revealed outcomes by 5.5% and has a practically identical exhibition to the best in class techniques on the different datasets.

Compressive Sensing, Descriptors,SURF, Texture classification

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Ankita Boni, Sagar Shinde, "Performance Analysis of Dense Micro-block Difference and SURF Method for Texture Classification," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.479-482, 2019.

In the present post-industrial information epoch where the regular changing environment is depend upon technological abbreviations. Early world was related information with only joining two computers to each other, upto this secure cloud computing policies. Present world races are in racing mode where everyone want to become a leader. Today’s world war is based on information. Information is based on privacy concern over Assess, Compliance, Storage, Monitoring and privacy breaches. Over this phenomena world decide many rules and regulations as per their feasibility to provide information over cloud that still world is in need of better policies over geographical boundaries and country based political issues. Many major rules and regulation are define in this paper to maintain worldwide cloud boundaries for use of fast safe and secure.

Cloud, Rapid Adoption of cloud, Barriers to cloud, Legal and Political Issues[40]

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Saroj Kumar, Santosh Kumar, "World Wide Web - Cloud Boundaries," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.483-490, 2019.

Abundance and obtainability of audiovisual capturing devices, like mobile phones and loop camera, have prompted analysis in videocassette face appearance perseption, that is extraordinarily relevant in impostion solicitations .While this methodologies are declared high precisions at equivalent error rates, enactment at lesser lying acceptance rates wants significant development. So, we tend to introduced a completely unique face verification rule, 1st the feature-rich frames are designated from a video sequence .Frame choice done by illustration learning-based feature extraction, is finished by using: 1) deep learning, combining of stacked demising distributed auto-encoder 2) deep Boltzmann classifier (DBC) 3) apprising the loss purpose of DBC by as well as distributed and short rank regularization. Finally, the results verified on 2 wide conferred databases, YouTube and little videos and Shoot Challenge.

Face Verification, Neural Networks, DBC, YouTube, Tiny videos

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Sonika Koganti, Talluri Sunil Kumar, "Video Face Recognization Using Autoencoder and Softmax Classifications," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.491-496, 2019.

In software engineering, continuous integration (CI) is the routine with regards to blending all engineer working duplicates to a common mainline a few times each day. The principle point of CI is to avert coordination issues. Jenkins is a driving open source constant coordination server worked with Java. It is utilized to manufacture and test programming ventures constantly making it simpler to incorporate changes to the task. It provides more than 985 plugins to help to build and to test any project. Continuous Integration (CI) requires developers to integrate code into a shared repository several times a day. GUI will maintain all latest jobs with their build status and continuous notification referring to design issues, code, failures, etc. Once the project is configured in Jenkins then all future builds are automated. Hence GUI will help to represent basic reporting like run status (successful, failure, unstable) and weather reports (job health). GUI will also provide automatic continuous regression run which will give flag details of new relevant changes in build compared to last run and easy access for code change mapping and auto-selection. GUI will help to eliminate manual verification.

Jenkins, Continuous Integration and Continuous Delivery, Auto Build Trigger, Python Django, Perforce

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Vaishnavi R. Mali, Anil R. Surve, "GUI for Continuous Integration and Automatic Bug Verification of Jenkins Server," International Journal of Computer Sciences and Engineering, Vol.7, Issue.6, pp.497-500, 2019.