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In present scenario cloud computing is not a new term for scientist, engineers and researchers. It is used by people from varied walks of life ranging from organizations to mobile users. This technology allows many organization and individual users to take services, hardware, storage spaces and software on rent rather than setting up new infrastructure. With advancement in technology, cloud computing faces several challenges like power consumption, reliability, performance, bandwidth cost, security and privacy which needs to be addressed by researchers. This paper presents an overview of cloud computing technology and comparative analysis of virtual machine (VM) scheduling algorithms. VM scheduling algorithms are compared on basis on several parameters such as reliability, scalability, QoS, and environment. The comparison is further fine-tuned with quantified data.

Cloud, Virtual Machine, Scheduling, Quality of Service, Energy, Cost.

[1] Puneet Himthani, Amit Saxena, and Manish Manoria, “Comparative Analysis of VM Scheduling Algorithms in Cloud Environment.” International Journal of Computer Applications, Vol. 120, Isuue.6, pp. 1-6, 2015.
[2] Anuradha, Suman Sangwan (2016) “Implementing Multiple Security in the Cloud Environment” International Journal of Advance research , Ideas and Innovations in Technology, Vol. 2, Issue. 5, pp. 1-7, 2016.
[3] Shikha Gupta, Suman Sangwan, “Load Balancing in Cloud Computing: A Review”, International Journal of Science, Engineering and Technology Research, Vol. 4, Issue. 6, pp. 1912-1916, 2015.
[4] Amanpreet Kaur, and Dr.Sawtantar Singh Khurmi “A Study of Cloud Computing Based on Virtualization and Security Threats”, International Journal of Computer Sciences and Engineering, Vol. 5, Issue. 9, pp. 108-112, 2017.
[5] Rajkumar Buyya. James Broberg, and Andrzej Goscinki, “Cloud Computing: Principles and Paradigms”, John Wiley and Sons, Canada, 2011.
[6] P. Mell and T. Grance, “The NIST Definition of Cloud Computing”, National Institute of Standards and Technology, Information Technology Laboratory, Technical Report, Vol. 15, 2009.
[7] Nandimandalam Mohan, Krishna Varma, and Eunmi Choi (2016) “Study and Comparison of Virtual Machine Scheduling Algorithms in Open Source Clouds.” Advanced Multimedia and Ubiquitous
[8] Engineering, Lecture Notes in Electrical Engineering, pp. 349-355, 2016.
[9] Karan D. Prajapati, Pushpak Raval, Miren Karamta, and M. B. Potdar, “Comparison of Virtual Machine Scheduling Algorithms in Cloud Computing.” International Journal of Computer Applications, Vol. 83, Issue.15, pp. 23-30, 2015.
[10] Choudhary and Peddoju , “A Dynamic Optimization Algorithm for Task Scheduling in Cloud Environment.” International Journal of Engineering Research & Applications, Vol. 2 Issue. 3, pp.2564-2568, 2012.
[11] Amit Kumar Das, Tamal Adhikary, Md. Abdur Razzaque, Choong Seon Hong, “An Intelligent Approach for Virtual Machine and QoS Provisioning in Cloud Computing” in IEEE International Conference on Information Networking (ICOIN), pp. 462-467, 2012.
[12] Liu, Chen, Qiu, Cai and Huang, “Scheduling Parallel Jobs using Migration & Consolidation in the Cloud”, Hindwai Publications of Mathematical Problems in Engineering, Vol. 2012, pp.1-18, 2012.
[13] S. Sadhasivam, N.Nagaveni, R. Jayarani, and R. Vasanth Ram, “Design and Implementation of an efficient Two level Scheduler for Cloud Computing Environment” in International Conference on Advances in Recent Technologies in Communication and Computing, Inida, pp. 884-886, 2009.
[14] Shinichi and Kuribayash,“Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments” International Journal of Research and Reviews in Computer Science, Vol. 2 , Issue. 1, pp. 1-8, 2011.
[15] Lu Huang, Hai-shan Chen and Ting-ting Hu, "Survey on Resource Allocation Policy and Job Scheduling Algorithms of Cloud Computing" Journal of Software, Vol. 8 Issue. 2, pp. 480-487, 2013.
[16] Sung Ho Jang, Tae Young Kim, Jae Kwon Kim and Jong Sik Lee, “The Study of Genetic Algorithm-based Task Scheduling for Cloud Computing”, International Journal of Control and Automation, Vol.5 Issue.4, pp.157-162, 2012.
[17] Shaminder Kaur and AmandeepVerma,”An Efficient Approach to Genetic Algorithm for Task Scheduling in Cloud Computing Environment”, International Journal of Information Technology and Computer Science, Vol.4, Issue. 10, pp. 74-79, 2012.
[18] J. Yu and R. Buyya, “Scheduling Scientific Workflow Applications with Deadline and Budget Constraints using Genetic Algorithms” Scientific Programming Journal, Vol.14, Issue. 3, pp. 217-230, 2006.
[19] Khan, Kapgate and Prasad, “A Review on Virtual Machine Management Techniques & Scheduling in Cloud Computing”, International Journal of Advanced Research in Computer Science & Software Engineering, Vol. 3, Issue. 12, pp. 838-845, 2013.
[20] Ahn, and Jeongseob, "Dynamic Virtual Machine Scheduling in Clouds for Architectural Shared Resources" in Proceedings of the 4th USENIX conference on Hot Topics in Cloud Computing, pp. 1-6, 2012.
[21] Chen He Ying, and Lu David Swanson, “Matchmaking: A New Map Reduce Scheduling Technique” in IEEE Third International Conference on Cloud Computing Technology and Science (Cloud Com), pp.40-47, 2011.
[22] Kehe Wu, Ruo Du, Long Chen, and Su Yan, “An Energy Saving Virtual-Machine Scheduling Algorithm of Cloud Computing System” in IEEE International Conference on Information Science and Cloud Computing Companion, pp. 219-224, 2013.
[23] Xing, Liu and Ling, “Neural Network PID Control Based Scheduling Mechanism for Cloud Computing” International Journal of Applied Mathematics & Information Sciences, Vol.9, Issue.2), pp.789 – 796, 2015.
[24] Chun-Wei Tsai, Wei-Cheng Huang, Meng Hsiu Chiang, Ming Chao Chiang, Chu-Sing Yang, “A Hyper-Heuristic Scheduling Algorithm for Cloud” IEEE Transactions on Cloud Computing, Vol.2, Issue.2, pp.236-250, 2016.
[25] Shridhar Domanal, Student Member, Ram Mohana Reddy Guddeti, and Rajkumar Buyya,”A Hybrid Bio-Inspired Algorithm for Scheduling and Resource Management in Cloud Environment” IEEE Trans. on Services Computing, Vol. 99, pp. 1-14, 2017.
[26] Chongjin Li, Jidong Ge, Haiyang Hu, Wei Song, Hao Hu, and Bin Luo, “Cost and Energy Aware Scheduling Algorithm for Scientific Workflows with Deadline Constraint in Clouds” IEEE Transactions on Services Computing , Vol. 99, pp. 1-15, 2015.
[27] Heba Kurdi, Ebtehal T. Alotaibi, “A Hybrid Approach for Scheduling Virtual Machines in Private Clouds”, Procedia Computer Science Vol.34, pp.249 – 256, 2014.
[28] D.I. George Amalarethinama, T. Lucia Agnes Beenab, “Customer Facilitated Cost-based Scheduling (CFCSC) in Cloud”, Procedia Computer Science, Vol. 46, pp.660 – 667, 2014.
[29] J. Yu and R. Buyya, “Scheduling Scientific Workflow Applications with Deadline and Budget Constraints using Genetic Algorithms” Scientific Programming Journal Vo. 14, No. Issue. 3, pp. 217-230, 2006.
[30] Choudhary and Peddoju, “A Dynamic Optimization Algorithm for Task Scheduling in Cloud Environment”, International Journal of Engineering Research & Applications, Vol. 2, Issue.3, pp. 2564 – 2568, 2012.
[31] J. Rouzaud-Cornabas, “A trust aware distributed and collaborative scheduler for virtual machines in cloud” US Patent, 2011.
[32] James & Verma, “Efficient VM Load Balancing Algorithm for a Cloud Computing Environment”, International Journal on Computer Science & Engineering, Vol.4 Issue.9, pp. 1658-1663, 2012.

M. Kumar, Suman, S. Singh, "A Survey on Virtual Machine Scheduling Algorithms in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.485-490, 2018.

Many traffic violations occur every day yet there is no proper tracking system to keep a check on such occurrences and the consequences that occur thereto. Presently there is a manual system wherein a traffic police goes to drivers and vehicles which are suspicious and then checks the vehicle and license documents. There are many instances where all documents though accurate and to date, the driver would have missed to carry all the said documents. On a given business day to conduct this process is a tedious one and not an adaptable solution both for the traffic personnel and the public considering the inflow of the vehicles to and fro in to the city especially in metro cities. The proposed system helps control all the tedious tasks and aims to automate the monitoring and tracking of vehicles.

Automation, Monitoring, RFID[Radio-frequency identification]

[1] http://www1.huawei.com/enapp/28/hw-110836.html
[2] Drazen Pasalic, Branimir Cvijic, Dusanka Bundalo, Zlatko Bundalo, Radovan Stojanovic. “Vehicle Toll Payment System Based on Internet of Things Concept”. 5th Mediterranean conference on Embedded Computing, pp. 485-488, 2016
[3] Hu Lingling, Li Haifeng, Xu Xu, li Jain.” An Intelligent Vehicle Monitoring System Based on Internet of Things” , IEEE International Conference on Computational Intelligence and Security, pp 231-233, 2011.
[4] Chi-Man Vong, Pak-Kin Wong, Zi-Qian Ma. “Application of RFID Technology and the Maximum spanning Tree Algorithm for solving vehicle emissions in cities on Internet of Things” , IEEE World Forum on Internet of Things, pp. 347-352, 2014.
[5] Serge Popov, Mikhail Kurochkin, Leonid Kurochkin, Vadim Glazunov. “The implementing of Internet of Things concepts for the Continuous provision of Informational services for vehicle Drivers and passengers”. International Conference on Telematics and Future Generation Networks, pp. 1-5.
[6] Avik Ghose, Provat Biswas, Chirabrata Bhaumik, Monika Sharma, Arpan Pal, Abhinav Jha. “Road Condition Monitoring and Alert Application”. IEEE International Conference on Pervasive Computing and Communications Workshops, pp. 489-491, 2012.
[7] Artis Mednis, Girts Strazdins, Reinholds Zviedris, Georgijs Kanonirs, Leo Selav. " Real Time Pothole Detection using Android Smartphones with Accelerometers", International Conference on Distributed Computing in Sensor Systems and Workshops
[8] Yu-chin Tai, Cheng-wei Chan, Jane Yung-jen Hsu. "Automatic road anomaly detection using smart mobile device," in Proceedings of the Conference on Technologies and Applications of Artificial Intelligence (TAAI2010), November 2010.
[9] Minghe Yu, Dapeng Zhang, Yurong Cheng, and Mingshun Wang. “An RFID Electronic Tag based Automatic Vehicle Identification System for Traffic IOT Applications” , Chinese Control and Decision Conference,pp. 4244-8738, 2012.
[10] M. Abuelela, S. Olariu, "Taking VANET to the clouds", Proceedings of the 8th International Conference on Advances in Mobile Computing and Multimedia series MoMM `10. New York, pp. 6-13, 2010.
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[12] J J. Anda, J. LeBrun, D. Ghosal, C.-N. Chuah, and M. Zhang, VGrid. “Vehicular Adhoc Networking and Computing Grid for Intelligent Traffic Control”, IEEE 61st Vehicular Technology Conference, pp. 2905--2909, May 2005.
[13] Stephen Olariu, Michele C.Weigle. “Vehicular Networks: From Theory to Practice”, Taylor and Francis, Boca Raton, pp.1.1--1.28 2009.
[14] Newlin Rajkumar M, Ranjani M, Venkatesa kumar V. "Toll Gate Vehicle Monitoring System-A Preview", International Journal of Computer Sciences and Engineering, Volume-5, Issue-2,2017.

Nalina V., P. Jayarekha, "Real Time Automated Vehicle Monitoring and Control System Using Internet of Vehicles," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.491-494, 2018.

Congestion is always a critical area to be tackled to prevent hindrance in successful communication between networks. Sharing of critical information is the foremost responsibility of assorted components of network. Due to sudden occurrence of congestion, all nodes in the Wireless sensor network have to work hard and more in retransmitting data packets to base station through intermediate sensor nodes due to their huge loss. Extreme energy consumption is also there which degrades all over performance and sometimes showing concomitant effects like packet loss in network. So, this congestion needs to be avoided to prevent network failure by improved design of Wireless sensor Network and making use of different congestion protocols as per the application in which it is required. In this paper, we will compare different congestion detection and control algorithms on the basis of protocols and parameters incorporate by them in their Wireless Sensor Network’s topology to handle congestions.

WSNs, Congestion Removal Techniques, Traffic Manager and Resource Manager

[1] Wang C., Li B., Sohraby K., Daneshmand M. & Hu Y. (2007), “Upstream Congestion Control in Wireless Sensor Networks Through Cross-Layer Optimization”, IEEE J Sel Areas Commun, vol. 25, no. 4, s. 786 – 95.
[2] Yick J., Mukherjee B. & Ghosal D. (2008), “ Wireless Sensor Network survey”, Computer Networks, vol. 52, no. 12, s. 2292 – 2330. 

[3] Monowar M., Rahman O., Pathan A. & Hong C. (2012), “ Prioritized Heterogeneous Traffic-Oriented Congestion Control Protocol for WSNs”, International Arab Journal of information technology. Vol. 9, no. 1, s. 39 – 48.
[4] Zhao J., Wang L., Li S., Liu X., Yuan Z. & Gao Z. (2010), “ A survey of Congestion Control Mechanisms in Wireless Sensor Networks”, IEEE sixth international conference on intelligent information hiding and multimedia signal processing, October 2010, Darmstadt, s. 719 – 722, Germany. 

[5] Wan C., Eisenman S. & Campbell A. (2003), “ CODA: Congestion Detection and Avoidance in Sensor Networks”, In: Proceedings of Association for Computing Machinery Sensor Systems, Los Angeles, vol. 6, s. 266 – 279, USA. 

[6] Wang C., Li B., Sohraby K., Daneshmand M. & Hu Y. (2006) Priority-based Congestion Control in Wireless Sensor Networks. In: IEEE international conference on sensor networks, s. 22 – 31, Taiwan.
[7] Heikalabad S.R., Ghaffari A., Hadian M.A. & Rasouli H. (2011), “ DPCC: dynamic predictive congestion control in wireless sensor networks”, IJCSI Int J Comput Sci Issues 2011, vol. 8, no. 1, s. 472 – 7. 

[8] Ee C-T. & Bajcsy R. (2004), “ Congestion control and evenhandedness for many-to-one routing in sensor networks”, In: Proceedings of ACM Sensys’04, s. 461 – 473. 

[9] Ghaffari A. (2015), “ Congestion Control mechanisms in wireless sensor networks: A survey”, Journal of network and computer applications, vol. 52, s. 101 – 115. 

[10] Kafi M.A., Djenouri D., Othman J.B., Ouadjaout A. & Badache N. (2014),” Congestion Detection Strategies in Wireless Sensor Networks: A comparative study with test bed experiments”, The 5th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN-2014), vol. 16, no. 3, s. 1369 – 1390.

Shivali Dhaka, "Analysis and Comparison between Congestion Control Techniques," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.495-498, 2018.

In Healthcare and Biomedical sectors, the data is growing more and more, analysing of such medical data accurately will benefits disease detection and early diagnosis. Mammography is the process toward utilizing low-energy X-rays to look at the human cancer for diagnosis and screening. The objective of mammography is the early detection of breast cancer , ordinarily through recognition of trademark masses or macrocalcifications. Low positive predictive model of mammogram will lead to more no unnecessary biopsies with benign outcomes. The accuracy and reliability of prediction mechanisms is important to reduce the number of biopsies. In this paper, we look at different machine learning algorithms with a specific end goal to predict the performance accuracy. By comparing different algorithms, it has been concluded that deep learning algorithm and Revisiting SVM have highest prediction accuracy among other algorithms studied. Experimental results show this prediction approach is more effective.

Deep learning, Machine Learning, Revisiting SVM, SVM

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[12]. Y. Wang, and I. H. Witten, “Inducing Model Trees for Continuous Classes”, the European Conf. on Machine Learning Poster Papers.

R. Swathi, R. Seshadri, "Evolution of Machine Learning Methods for Memography Classification," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.499-502, 2018.

Big data consists of large volumes of data which are used to discover the hidden knowledge. Class imbalance nature is a conventional issue which is present in all real world datasets. The class imbalance nature in the big data reduces the performance of the existing classification algorithms. The data source of diverse nature available from varied sources also degrades the performance of the existing algorithms. To address these issues of class imbalance problem the present work proposed various novel and effective class imbalance learning (CIL) algorithms. In this work, we proposed Uniform Strategic Sampling (USS) Technique novel algorithms approaches for class imbalance data sources.

Class Imbalance Learning(CIL),Big Data,Sampling,Uniform Sampling Strategy Technique,Classification

[1]. Rukshan Batuwita and Vasile Palade, “CLASS IMBALANCE LEARNING METHODS FOR SUPPORT VECTOR MACHINES”, Imbalanced Learning: Foundations, Algorithms, and Applications, By Haibo He and Yunqian
Ma, Copyright c 2012 John Wiley & Sons, Inc.
[2]. Rushi Longadge, Snehlata S. Dongre, Latesh Malik,” Class Imbalance Problem in Data Mining: Review”, International Journal of Computer Science and Network (IJCSN) Volume 2, Issue 1, February 2013. www.ijcsn.org ISSN 2277-5420.
[3]. Kun Jiang, Jing Lu, Kuiliang Xia,” A Novel Algorithm for Imbalance Data Classification Based on Genetic Algorithm Improved SMOTE”, Arab J Sci. Eng, DOI 10.1007/s13369-016-2179-2.
[4]. Shaza M. Abd Elrahman and Ajith Abraham, “A Review of Class Imbalance Problem” Journal of Network and Innovative Computing ISSN 2160-2174, Volume 1, pp. 332-340, 2013. ©MIR Labs, www.mirlabs.net/jnic/index.html
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Mohammad Imran, "A Novel Algorithm for Class Imbalance Learning on Big Data using Uniform Sampling Strategy (USS) Technique," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.503-506, 2018.

One of the most dangerous diseases in the world today is cancer. The brain, lung, skin, liver, and other organs have various cancers. Finding cancer has proven to be difficult. Cancer has a good possibility of being cured if it is discovered early. The dangerous disease can only be cured in large part by detection. Feature extraction, segmentation, pre-processing, and classification are all steps in the detection process. Pre-processing is the stage where noise removal is possible. Segmentation can assist in dividing the image into different fields, feature extraction can assist in extracting features, and classification can assist in classifying and detecting the final cells. This paper provides a comprehensive overview of skin cancer and the role that digital image processing plays in its early identification. All research papers are taken from reputable journals that cover this topic.

Skin cancer, Segmentation, Classification, Image Processing, ANN

[1] S. H. Hassanpour, M. Dehghani, "Review of cancer from perspective of molecular", Journal of Cancer Research and Practice, Vol. 4, Issue. 4, pp. 127-129, 2017.
[2] S. Jain, V. jagtap, N. Pise," Computer Aided Melanoma Skin Cancer Detection Using Image Processing", Procedia Computer Science, Vol. 48, pp. 735-740,2015.
[3] S. R. Silpa, V. Chidvila, “A Review On Skin Cancer”, International Research Journal of Pharmacy, Vol. 4, pp. 83-88, 2013.
[4] R. Maurya, A. Singh, V. Srivastava, R. Yadav, “A Comparative Review of Various Approaches for Skin Cancer Detection”, International Journal of Computer Sciences and Engineering, Vol. 5, Issue 10, pp. 146-152, 2017.
[5] E. Jana, R. Subban, S. Saraswathi, "Research on Skin Cancer Cell Detection Using Image Processing, ”, In the Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pp. 1-8, 2017
[6] A. Kwasigroch, A. Miko?ajczyk, M. Grochowski, “Deep neural networks approach to skin lesions classification—A comparative analysis”, In the Proceedings of the 2017 22nd International Conference on Methods and Models in Automation and Robotics, pp. 1069-1074, 2017.
[7] R. B. Aswin, J. A. Jaleel, S. Salim, "Hybrid genetic algorithm Artificial neural network classifier for skin cancer detection", 2014 International Conference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), pp. 1304-1309, 2014.
[8] A. Esteva, B. Kuprel, R. Novoa, et al.,” Dermatologist-level classification of skin cancer with deep neural networks”, Nature 542, pp. 115–118, 2017.
[9] H. Alquran, "The melanoma skin cancer detection and classification using support vector machine", In the Proceedings of the 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Aqaba, pp. 1-5, 2017.
[10] S. Mustafa, A. Kimura,” An SVM-based diagnosis of melanoma using only useful image features”, In the Proceedings of the 2018 IEEE International Workshop on Advanced Image Technology (IWAIT), pp. 1-4, 2018.
[11] D.Didona, G. Paolino, U. Bottoni, C. Cantisani, “Nonmelanoma skin cancer pathogenesis overview. Biomedicines”, Vol 6, Issue 6, pp. 1-15 2018.
[12] F.Islami, A. S. Goding, K. D. Miller, R. L. Siegel, S. A. Fedewa, E. J. Jacobs, M. L. McCullough, A. V. Patel, J. Ma, I. Soerjomataram, W. D. Flanders, O. W. Brawley, S. M. Gapstur, A. Jemal, “Proportion and number of cancer cases and deaths attributable to potentially modifiable risk factors in the United States” CA: a cancer journal for clinicians, Vol 68, Issue 1,pp. 31-54. 2018.
[13] L. Tang, S. E. Park, “Sun Exposure, Tanning Beds, and Herbs That Cure: An Examination of Skin Cancer on Pinterest”, Health Communication, Vol. 32, Issue. 10, pp. 1192–1200, 2017.
[14] A. Khatami, S. Mirghasemi, A. C. P. Khosravi, Lim, H. S. Asadi, Nahavandi, “A Swarm Optimization-Based Kmedoids Clustering Technique for Extracting Melanoma Cancer Features”, ”, In the Proceedings of the International Conference on Neural Information Processing, pp. 307-316. Springer, Cham, 2017.
[15] J. C. Kavitha, A. Suruliandi, D. Nagarajan, T. Nadu, “Melanoma detection in dermoscopic images using global and local feature extraction”, International Journal of Multimedia and Ubiquitous Engineering, Vol.12, Issue. 5, pp. 19-28. 2017.
[16] P. Kharazmi, M. I. AlJasser, H. Lui, Z. J.Wang, T. K. Lee, “Automated detection and segmentation of vascular structures of skin lesions seen in Dermoscopy, with an application to basal cell carcinoma classification”, IEEE journal of biomedical and health informatics, Vol. 21, Issue. 6, pp.1675-1684, 2017.
[17] M. A. Taufiq, N. Hameed, A. Anjum, F. Hameed, “m-Skin Doctor: a mobile-enabled system for early melanoma skin cancer detection using a support vector machine”, In eHealth 360°, Springer, Cham, pp. 468-475, 2017.
[18] S.S. Mane, S.V. Shinde, “Different Techniques for Skin Cancer Detection Using Dermoscopy Images”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.12, pp.159-163, 2017.
[19] A. Pennisi, D. D. Bloisi, D. Nardi, A. R. Giampetruzzi, C. Mondino, A. Facchiano, “Skin lesion image segmentation using Delaunay Triangulation for melanoma detection”, Computerized Medical Imaging and Graphics, Vol. 52, pp. 89-103, 2016.
[20] E. Nasr-Esfahani, S. Samavi, N. Karimi, S. M. R. Soroushmehr, M. H. Jafari, K. Ward, K. Najarian,” Melanoma detection by analysis of clinical images using a convolutional neural network”, In the Proceedings of 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 1373-1376, 2016.
[21] C. Sagar, L. M. Saini,” Color channel-based segmentation of skin lesion from clinical images for the detection of melanoma”, In the Proceedings of IEEE 1st International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), pp. 1-5, 2016.
[22] R. S. Soumya, S. Neethu, T. S. Niju, A. Renjini, R. P. Aneesh, “Advanced earlier melanoma detection algorithm using color correlogram”, In the Proceedings of 2016 International Conference on Communication Systems and Networks (ComNet), pp. 190- 194, 2016.
[23] N. Ghavami, G. Tiberi, M. Ghavami, S. Dudley, M. Lane, “Huygens principle-based UWB microwave imaging method for skin cancer detection”, In the Proceedings of IEEE 10th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), pp. 1-4, 2016.
[24] P. Mehta, B. Shah, “Review on techniques and steps of a computer-aided skin cancer diagnosis”, In the Proceedings of Procedia Computer Science85, pp. 309-316. 2016.

Balwinder Kaur, "Review of Skin Cancer Detection Techniques Using Image Processing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.507-513, 2018.

The potential of quantum computing to revolutionize various industries and markets has been widely recognized. By utilizing the principles of quantum mechanics such as superposition and entanglement, quantum computers are capable of representing data and performing operations on them at an unprecedented speed. These unique features allow for solving complex problems that are practically impossible for classical computers to solve efficiently. This article provides an overview of the three layers of a quantum computer, which include the hardware layer, system software layer, and application layer. Moreover, it discusses the potential applications of quantum computing and explores possible research directions in the field of information systems.

Quantum Computing, Qubits, Quantum mechanics, gates

[1] Ciliberto, C., Herbster, M., Ialongo, A. D., Pontil, M., Rocchetto, A., Severini, S., & Wossnig, L. (2018). Quantum machine learning: A classical perspective. Proceedings Mathematical, Physical, and Engineering Sciences, 474 (2209), 20170551, 2018.
[2] Almudever, C. G., Lao, L., Fu, X., Khammassi., N., Ashraf, I., Iorga, D.,Varsamopoulos, S., Eichler, C., Wallraff, A., Geck, L., Kruth,A., Knoch, J., Bluhm, H., & Bertels, K. (2017). The engineering challenges in quantum computing, Design, Automation & Test in Europe Conference & Exhibition (DATE), 836–845, 2017.
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[9]Ménard, A., Ostojic, I., Patel, M., & Volz, D. (2017). A game plan for quantum computing, 2017.

Md. Shibli Rahmani, "Quantum Computing: Unleashing the Potential of Qubits and Quantum Gates," International Journal of Computer Sciences and Engineering, Vol.6, Issue.3, pp.514-517, 2018.