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Human race requires energy at very rapid rate for their living and wellbeing from the time of their arrival on this planet, because of this reason power resources have been worn out and enervated. One of the sectors that have gained much interest is devices that are able to convert ambient energy into electrical energy. This paper presents an experimental model for harvesting kinetic energy of footsteps using Piezoelectric. Piezoelectric materials are promising for energy harvesting from the force generated by human footsteps, into a useful form of electrical energy. Installing piezoelectric tile (Piezotile), we can achieve the little amount of power, a single piezotile, which has the capability to produce a maximum of 5-8 volts, eventually installing numerous piezotile, will provide us with much voltage, and application of additional current source we can generate more amount of power . Piezotile is one kind of piezoelectric transducer which transforms foot stress into electric energy. From the stored energy in the battery, the power is supplied to Radio-frequency identification (RFID) as it uses electromagnetic field to automatically identify and track tags attached to objects. And hence only authorized person with tag can login to RFID and use the energy for various applications

Footsteps, Piezoelectric tile, Power Generations; Renewable Energy; energy harvesting, Piezoelectric sensors, RFID.

[1]. P. Sirigireddy and P. B. Eladi, "Numerical Design of
Novel piezoelectric generating structure that effectively utilizes the force generated from human motion," 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE2020), Cochin, India, pp.1-6, 2020, doi:10.1109/PESGRE45664.2020.9070599.
[2]. K. Somalaraju, J. G. Singh, "Enhancement of Power
Generation From Electromagnetic Scavenging Tile", 2020 International Conference on Power Electronics & IoT
Applications in Renewable Energy and its Control (PARC), Uttar Pradesh, India, pp.405-410, 2020, doi:10.1109/PARC49193.2020.236638.
[3]. M. R. Ruman, M. Das and S. M. Istiaque Mahmud, "Human Footsteps for Energy Generation by using Piezoelectric Tiles", Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India, pp.1-6, 2019, doi:10.1109/i-PACT44901.2019.8960068.
[4]. T. B. A. Akib, H. Mehedi and M. Nazmuschayadat,
"Electrical Energy Harvesting from the Foot Stress On Foot Overbridge Using Piezoelectric Tile," 2019 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT), Dhaka, Bangladesh, pp. 1-5, 2019, doi:10.1109/ICASERT. 2019. 8934544.
[5]. D. Dalabeih, B. Haws and S. Muhtaseb, "Harvesting kinetic energy of footsteps on specially designed floor tiles," 2018 9th International Renewable Energy Congress (IREC), Hammamet, pp.1-4, 2018, doi:10.1109/IREC.2018.8362566.
[6]. Kamboj, A. Haque, A. Kumar, V. K. Sharma and A. Kumar, "Design of footstep power generator using piezoelectric sensors," 2017 International Conference on Innovations in Information, Embedded and Communication Systems (ICIIECS), Coimbatore, India, pp.1-3, 2017, doi:10.1109/ICIIECS.2017.8275890.
[8]. Divyakumar. N, Ganesh. V. S, Vishnuraju. G, Yogesh. P and Sangappa. S. B, “Production of Electricity through Pressure Based Sensors”, 2016 International Journal of Computer Sciences and Engineering (IJCSE), India, Vol.04, Special Issue 03, pp. 188-191, 2016.
[7]. R. M. Veena, B. H. Reddy and S. M. Shyni, "Maximum energy harvesting from electromagnetic micro generators by footsteps using photo sensor", 2016 International Conference on Computation of Power, Energy Information and Communication (ICCPEIC), Chennai, India, pp.757-761, 2016, doi:10.1109/ICCPEIC.2016.7557321.
[8]. T. R. Deshmukh, “Design and analysis of a Mechanical Device to harvest energy from human footstep motion”, International Journal of Innovative and Emerging Research in Engineering, Vol.3, Special Issue 1, 2016.
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Generation Using Piezoelectric Material”, International Journal of Advanced Research in Electronics and Communication Engineering (IJARECE), Vol.4, Issue 10, 2015.
[10]. Jose Ananth Vino, “Power Generation Using Foot
Step”, International Journal of Engineering Trends and Technology (IJETT), Vol.1, Issue.2, 2011.

Somashekhar G.C., Anu Reddy K.H, Bini Mariam Biju, Prateeka L., "Energy Generation From Footsteps Using Piezoelectric Sensors," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.54-58, 2021.

Big Data analysis and processing is a popular tool for Artificial Intelligence and Data Science to extract applicable solution from data across a broad range of application domains. Even though Big data is in the mainstream of operations as of 2020, With the increase in data processing and storage capacity, a large amount of data is available and because of that potential issues or challenges the researchers can address, some of these issues overlap with the data science field. One of the key issue is the inevitable existence of uncertainty in stored or missing values. Any uncertainty in a source causes its disadvantageous, complexity or inapplicability to use. It is importance to ensure the reliability and a value of data source. That is why it is crucial to eliminate uncertainty or to lower uncertainty influence because data without any analysis does not have much value. In this paper we review previous work in big data analytics and Survey of many theories and techniques which have been developed to model its various forms. We have described several common techniques such as Bayesian model and fuzzy set, Shannon’s entropy. We present a discussion of open challenges and future directions for handling and eliminating uncertainty in this profile.

Big Data, Data Sciences, Data Uncertainty, Uncertainty Elimination, Machine learning, NLP, Computational Intelligence.

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[5] Athmaja S, Hanumanthappa M, Vasantha Kavitha,” A SURVEY OF MACHINE LEARNING ALGORITHMS FOR BIG DATA ANALYTICS” IEEE International Conference on Innovations in Information, Embedded and Communication Systems 2017.
[6] M. U. Bokhari, M. Zeyauddin and M. A. Siddiqui, “An effective model for big data analytics”, 3rd International Conference on Computing for Sustainable Global Development, pp. 3980-3982, 2016.
[7] Monali R. Baviskar, Priya N Nagargoje, Priyanka A. Deshmukh, Rina R. Baviskar, “A Survey of Data Science Techniques and Available Tools”, International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056 Volume: 08 Issue: 04 | Apr 2021 p-ISSN: 2395-0072
[8] R. Farias and E. Clua,” Parallel Image Segmentation using Reduction-Sweeps on Multi-core Processors and GPUs IEEE” 26th Conference on Graphics, Patterns and Images (SIBGRAPI), Rio de Janeiro, Brazil.

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[7] J. L. Berral-Garcia, “A quick view on current techniques and machine learning algorithms for big data analytics”, 18th International Conf. on Transparent Optical Networks, pp.1-4.
2016. DOI: 10.1109/ICTON.2016.7550517.
[8] B. Kulis and M. I. Jordan A New k-Means algorithms via Bayesian Non parametrics Proceedings of the 29th International Conference on Machine Learning, Edinburgh, Scotland, UK, 2012.
[9] N. Marz , and J. Warren, Big Data: Principles and best practices of scalable real time data systems. Manning Publications, 2015.
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[11] Saidulu D, Sasikala R. Machine learning and statistical approaches for Big Data: issues, challenges and research directions. Int J Appl Eng Res. 2017;12(21):11691–9.
[12] J. L. Berral-Garcia, “A quick view on current techniques and machine learning algorithms for big data analytics”, 18th International Conf. on Transparent Optical Networks, pp.1-4, 2016. DOI: 10.1109/ICTON.2016.7550517.
[13] Parmar, V. & Gupta, I., 2015. Big data analytics vs Data Mining analytics. IJITE, 3(3), pp.258–263.
[14] G. Cavallaro, M. Riedel, M. Richerzhagen, J. A. Benediktsson, and A. Plaza, "On Understanding Big Data Impacts in Remotely Sensed Image Classification Using Support Vector Machine Methods," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 8, pp. 4634-4646, 2015.
[15] Joseph D. Prusa* andTaghi M. Khoshgoftaar, ”Improving deep neural network design with new text data representations” J Big Data (2017) 4:7DOI 10.1186/s40537-017-0065-8.
[16] Karl Weiss* , Taghi M. Khoshgoftaar and Ding Ding Wang Weiss,” A survey of transfer learning” et al. J Big Data (2016) 3:9 DOI 10.1186/s40537-016-0043-6.
[17] Priya nagargoje, vandna jagtap, “time-efficient fracture detection using multi-core parallel processing”, international journal of current engineering and scientific research (ijcesr) issn (print): 2393-8374, (online): 2394-0697, volume-3, issue-9, 2016.

Priya Nagargoje, Monali Baviskar, "Uncertainty Handling In Big Data Analytics: Survey, Opportunities and Challenges," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.59-63, 2021.

The purpose of this review is to find how today`s generation impacted with chronic inflammation and its effects on health and long-term diseases using Machine Learning Algorithms. These diseases are found to be appeared after long time suffering with chronic inflammation. By detecting it early and taking lifestyle changes and healthy diet could potentially avoid the diseases like diabetes, cardiovascular diseases, cancer, arthritis, and bowel diseases. Autoimmune disease symptoms can be minimized by identifying inflammation levels and by taking precautionary measures at any stage of the patient. There are various inflammation markers to detect inflammation in the body by simple blood tests like CRP, ESR which are inexpensive and provide early disease detective mechanisms. These reports can be input to Machine Learning algorithms and train the system to help the patients to identify inflammation levels and alert them to take appropriate actions to prevent further damage from diseases on human organs.

Machine Learning, algorithm, Inflammation, Autoimmune

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Abeda Begum, Rajeev Kumar, "Review of Chronic Inflammation and long term effects on health using Machine Learning Algorithms," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.64-71, 2021.

The process of reimposing the missing pixels from the flawed image and remove unwanted object is referred as Image inpainting. The most prominent purpose of the inpainting algorithm is to put back distorted and unpleasant regions and fill holes using natural method. Some common application of this technique includes remove unwanted object, restoring photos, photo retouching, or remove unwanted text, logo, stamps, copyright from the images. Based on the background information, image inpainting restore the damage part of the image by filling missing or corrupted data in the image. The restored image, which is produced as the result of applying inpainting technique will provide more realistic and more pleasant than compared to the original image. The attempt of recovering scene details blocked by visible parts is called disocclusion, which is viewed as an important part in image and depth inpainting. Holes are the occluded and impaired parts which is to be restored in an image. Hierarchical super-resolution-based, diffusion based, hybrid inpainting, texture synthesis based and exemplar-based method are used for inpainting. This paper gives brief review of the existing image inpainting approaches. This paper presents a brief survey of different image inpainting techniques and provides a relative comparison between these techniques for inpainting.

ImageInpainting,RegionFilling,ObjectFilling,Holes

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[2].Komal s Mahajan, M.B. Vaidya, “Image in Painting Techniques: A survey”, IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661, ISBN: 2278-8727 Vol. 5, No. 4 (Sep-Oct. 2012), Pp 45-49.
[3].K. Sangeetha, Dr. P. Sengottuvelan, E. Balamurugan,”Combined Structure and Texture Image Inpainting Algorithm forNatural Scene Image Completion.” - Journal of Information Engineering and Applications. ISSN 2224-5758 (print) ISSN 2224-896X Vol 1, No.1, 2011.
[4].M. Daisy, P. Buyssens, D. Tschumperl, and O. Lzoray, “A smarter exemplar-based inpainting algorithm using local and global heuristics for more geometric coherence,” in Proc. IEEE ICIP, Oct. 2014,pp. 4622-4626.
[5].A. Criminisi, P. Prez, and K. Toyama, “Region filling and object removal by exemplar-based image inpainting,” IEEE Trans. Image Process., vol. 13, no. 9, pp. 12001212, Sep. 2004
[6].O. Le Meur, J. Gautier, and C. Guillemot, “Examplar-based inpainting based on local geometry,” in Proc. 18th IEEE ICIP, Brussels, Belgium, Sep. 2011, pp. 34013404
[7].A. Wong and J. Orchard, “A nonlocal-means approach to exemplarbased inpainting,” in Proc. 15th IEEE Int. Conf. Image Process. (ICIP), Oct. 2008, pp. 26002603.
[8].O. Le Meur, M. Ebdelli, and C. Guillemot, “Hierarchical superresolutionbased inpainting,” IEEE Trans. Image Process., vol. 22, no. 10, pp. 37793790, Oct. 2013
[9]M. Bertalmio, L. Vese, G. Sapiro and S. Osher, "Simultaneous Structure and Texture Image Inpainting," IEEE Transaction on Image Processing, vol. 12, no. 8, August 2003, pp. 882-889.
[10].H. Li, W. Luo, J. Huang,” Localization of diffusion-based inpainting in digital images”, IEEE Transactionson Information Forensics and Security 12 (12) (2017) 3050-3064.
[11].Gunamani Jena, "Restoration of Still Images using Inpainting techniques”, International Journal of Computer Science & Communication, Vol. 1, No. 2, Pp. 71 -74, July - December 2010.
[12].Komal s Mahajan , Prof. M. B. Vaidya, "Image in Painting Techniques: A survey", IOSR Journal of Computer Engineering (IOSRJCE) Volume 5, Issue 4 (Sep-Oct. 2012), PP 45-49
[13]S. Rane, G. Sapiro, and M. Bertalmio, "Structure and Texture Filling-In of Missing Image Blocks in Wireless Transmission and Compression Applications," IEEE Transaction on Image Processing, vol. 12, no. 3, March 2003, pp. 296-303.
[14].T. Ruzic, A. Pizurica, "Context-Aware Patch-Based Image Inpainting Using Markov Random Field Modeling," IEEE Transactions on Image Processing, vol. 24, 2015, pp. 444-456
[15]. Qureshi, Muhammad & Deriche, Mohamed & Beghdadi, Azeddine & Amin, Asjad. (2017). A critical survey of state-of-the-art image inpainting quality assessment metrics. Journal of Visual Communication and Image Representation.
[16].R. T. Pushpalwar and S. H. Bhandari, "Image Inpainting Approaches - A Review," 2016 IEEE 6th International Conference on Advanced Computing (IACC), Bhimavaram, , pp. 340-345, 2016
[17].Zhang, N., Ji, H., Liu, L. “. Exemplar-based image inpainting using angle-aware patch matching”. J Image Video Proc. 2019, EURASIP Journal on Image and Video Processing 70 (2019).

Anushree Santosh, Vinod P.R., Jyothirmayi Devi C., "Review Paper On Image Inpainting Techniques," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.72-76, 2021.

Last few years the area of social media , e- commerce, social field has seen a large increase in the web world. The product view became the basic need of today’s world . The product reviews channel the customers and help them in making decisions regarding various available products which otherwise would bemuse them. This circumstances opened a new area of research called Opinion Mining and Sentiment Analysis. sentiment analysis is the process of determining the emotion ,feeling, and views of the people towards the piece of text, that comes under the area of blog view, article review , product review, social media buzzing etc. This research paper presents machine learning methods for detecting the sentiment expressed by movie reviews. The semantic point of reference of a review can be positive or negative.

Sentiment analysis, sentiment analysis techniques, Experimental result, comparative analysis, conclusion

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[6] Rui Xia, Chengqing Zong, Shoushan Li, “Ensemble of feature sets and classification algorithms for sentiment classification”, Information Sciences 181 (2011) 1138–1152
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[11] A. Mardin, T. Anwar, B. Anwer, “Image Compression: Combination of Discrete Transformation and Matrix Reduction”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.1, pp.1-6, 2017.
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Sakshi koli , "Sentiment Analysis with Machine Learning Techniques and Improved J48 Decision Tree Technique," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.77-82, 2021.

Scheduling has always been deemed as a perfunctory task for most organizations. It could nevertheless become an extremely arduous task if involves the management of events and of the dynamic variables that control it. Herein, we present an idea/solution to the Travelling Tournament Problem (TTP), which is a unique combinatorial problem tackling both feasibility and optimality of the solution. As the TTP belongs to the category of NP-Hard problems, generating solutions usually tend to be extremely costly, with most of the solutions having a time complexity of as high as O(n!). However, two of the many burgeoning fields, artificial intelligence and quantum computing are making headway and we believe that quantum computers possess enough potential to be competent enough to solve such scheduling problems. The aforementioned technologies have provided us with an excellent framework, which we have consequently adopted in our implementation. In the following paper, we portray a hybrid solution that utilizes the immense computational power of a quantum computer while also tweaking the classical algorithm to dynamically reduce the number of iterations and subsequently the cost. We draw parallels between the Travelling Salesman Problem (TSP) and the TTP by utilizing the insights obtained from a detailed analysis of the existing Simulated Annealing based approach, and thus propose certain unique modifications to the best known classical only solutions.

Quantum Computing, NP-Hard Problem, Travelling Tournament Problem, Simulated annealing

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Desmond Lobo, Kalpita Wagaskar, Sarang Gawane, Clifford Fernandes, "Hybrid Quantum - Classical AI based approach to solve the Traveling Tournament Problem," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.83-90, 2021.

Technology is the key to innovation in all aspects of this modern age. In any technology, data becomes the most important asset to protect. Many encryption algorithms are widely available and used in information security. Encryption can provide secure information across the platform. Encrypting the message using natural languages reduces encryption time and improves performance. Telugu is the oldest Dravidian language spoken in South India. The message to be encoded is translated into Telugu, after which this translated text is converted into a randomly generated combination of 2-bit English alphabets. This is a hybrid algorithm because the intermediate node is encrypted using the standard advanced encryption algorithm to improve the privacy of the text. Because Telugu encryption method uses three phases, namely translation, mapping, and encryption, this makes the data much more secure than existing algorithms like Blowfish and Data Encryption Standard (DES), and the person trying to decrypt must have knowledge of the Telugu language, as well as mapping details to see the original data and This algorithm shows a stronger avalanche effect of 96.5%, which is greater than Blowfish and DES. Evaluation of the proposed algorithm shows that it runs faster and has relatively less encryption time, less memory requirements.

Cryptography, Security, Telugu language, Encryption, AES

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T. Madhavi Kumari, A. Vinaya Babu, "A Novel Hybrid Symmetric Key Encryption Algorithm for Telegu Script," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.91-96, 2021.

Crime is the illegal task perform by human system for perform unauthorise3d access of various services without legal permission. in computer system there are many categories of crime related with cybercrime. the crime is basically relating to offence which is happen by illegal task and permission. N this computer system the many crime is describing various cybercrime. Like malware, hacking, cyber-attacks, cyber illegal access, IOT hacking, phishing scams, ransom ware, etc. These are described various types of cybercrime in computer technology. for determine the any one we have used various algorithm and simulation tool performance. Thyis is destroy our human society transaction system. It is also created by some human; many companies also do this illegal work for our business growth. This cyber various crime is also impact of our country growth and development also decreased. In this research work we have determinate cybercrime attacks detection and prediction model of determine the cyber-attacks using data mining and machine learning techniques. For implementation of this proposed work we are using WEKA simulation tool for execute data mining algorithms and Jupiter anaconda navigator simulate tool for machine learning algorithms for determinate accuracy of data mining and design production model using machine learning algorithms. The attacks categories by cybercrime are ddos attacks, U2r, R2L, probe, ICMP attacks, UDP attacks, TCP attacks, FAR high false alarm rate, Dos attacks, these are cyber-attacks detection by data mining and machine learning algorithms based determinate accuracy and efficiency of data mining algorithms and prediction model by machine learning algorithms

cybercrime, cyber-attacks, data mining, KDD cup data set, machine learning, weak tool, Jupiter anaconda navigator simulate tool

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Neetu Singh, Tripti Ajariya, Shailesh Raghuvanshi, Neha Singh, "Crime Analysis and Prediction Model Using Data Mining and Machine Learning Techniques: Comparative Analysis," International Journal of Computer Sciences and Engineering, Vol.9, Issue.6, pp.97-104, 2021.