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Cloud Computing is a technology that has seen a spurious growth recently and is being deployed for personal as well as business purposes. Cloud computing and analytics across the Internet provide greater pace to innovation and accelerate resources with increased flexibility. With this feature becoming increasingly integral to various services provided across the inter-connected digital world, it is imperative that its susceptibility be assessed and security made impenetrable to protect the sensitive information Cloud servers store. Cloud security has been vulnerable to threats and in several cases has led to Data Loss, Information Hacking and Denial of Services. These incidents have given rise to widespread concern regarding the data security that these Cloud Services employ. However, security models and security tools are being continually enhanced. This work aims to implement a Security Enhancement mechanism that gives the client-side greater control over data security and access.

Cloud security, Diffie-Hellman Key Exchange, AES Encryption Standard, Multiple Blocks Cloud Storage

[1].V. Fusenig, A. Sharma 2012. Security architecture for cloud networking, in: 2012 International Conference on Computing, Networking and Communications (ICNC). Presented at the 2012 ICNC Conference.
[2].B.R. Kandukuri, V.R. Paturi, A. Rakshit, 2009. Cloud Security Issues, in: IEEE International Conference on Services Computing, SCC 2009.
[3].S. Ramgovind, M.M. Eloff, E. Smith, 2010. The management of security in Cloud computing, in: Information Security for South Africa (ISSA), 2010.
[4].Karun Handa, Uma Singh, 2015. Data Security in Cloud Computing using Encryption and Steganography. International Journal of Computer Science and Mobile Computing. IJCSMC, 2015.
[5].H. Tianfield, 2011. Cloud computing architectures, in: 2011 IEEE International Conference on Systems, Man, and Cybernetics (SMC). Presented at the 2011 IEEE SMC Conference.
[6]. Op – ed : Encryption, not restriction, is the key to safe cloud computing. https: / / www. nextgov.com/cloud- computing/2012/10/oped-encryp-tion-not-restriction-key-
safe-cloudcomputing/58608
[7].“ Cloud Security and Privacy ” , Tim Mather, ShahedLatif and Subra Kumaraswamy. – O’Reilly Book.
[8].David Talbot. “ How Secure Is Cloud Computing?” Technology Review [Online].Available: http://www.technologyreview.com/computing/23951/ 2009
[9].Tania Gaur, Divya Sharma, 2016. A Secure and Efficient Client-Side Encryption Scheme in Cloud Computing. I.J. Wireless and Microwave Technologies. IJWMT 2016.

[10].Elminaam, DiaaSalama Abdul et al. "Performance Evaluation of Symmetric Encryption Algorithms." IJCSNS International Journal of Computer Science and Network Security 8.12. 2008
[11]. “Heuristics in Physical Design Partitioning “, Bhargab Sinha, Naushad Manzoor Laskar, Rahul Sen, ICIIECS 2015
[12]. “Security Aware Partitioning for efficient file system search”, Aleatha Parker Wood, Christina Strong, Ethan L. Miller.

Kretika Tiwari, Priyanka Sharma, Rahul Tiwari, "Cloud Security Intensification by Client-side Data segregated and Encryption," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1672-1676, 2019.

Wireless Mesh Network is a growing technology which is being targeted highly by researchers and has become part of our daily life. Routing has been one of the major issues in the Ad hoc networks other than interference. Various routing protocols used in wireless networks are broadly classified into proactive, reactive and hybrid protocols. The Zone Routing Protocol (ZRP) is a hybrid protocol that puts together the advantages of the proactive and reactive protocols by maintaining an up-to-date topological map of a zone centered on each node. In this paper, the proposed approach targets on improving ZRP using greedy heuristic algorithm in order to reduce end to end delay, packet loss and enhance the throughput of the network. The simulation has been performed using MATLAB R2016a and then the results are compared with existing Dynamic Source Routing (DSR). Tabu Search has been used to improve the overall performance of the network. Simulation has been carried out for 300 seconds over 10 nodes in a mesh topology and the results were then calculated and compared using different metrics.

Wireless Mesh Networks, ZRP, IARP, IERP, BRP, Greedy heuristic Algorithm

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[3] M. Eslami, O. Karimi, T. Khodadadi, “A Survey on Wireless Mesh Networks: Architecture, Specifications and Challenges”, IEEE 5th Control and System Graduate Research Colloquium. pp. 219-222, 2014.
[4] S Y. Shahdad, A. Sabahath and R. Parveez, “Architecture, Issues, and Challenges of Wireless Mesh Network”, International Conference on Communication and Signal Processing. pp. 0557-0560, 2016.
[5] A. Clementi, F. Pasquale, and R. Silvestri, “Opportunistic MANET: Mobility can make up for low transmission power” IEEE Transactions on Networking, vol. 21, no. 2, pp. 610-620, 2013.
[6] H. Mehta and Sukhbir , “Novel Approach to Zone Routing Protocol,” vol. 6, no. 4, pp. 881–886, 2016.
[7] A. Khatkar and Y. Singh, “Performance Evaluation of Hybrid Routing Protocols in Mobile Ad hoc Networks” in Proc. of the 2nd IEEE International Conference on Advanced Computing & Communication Technologies, pp. 542-545, 2012.
[8] B. A. S. Roopa, J. V. R. Murthy, and G., “Secure Zone Based Routing Protocol for ad hoc networks,” in Proc. of the IEEE International Conference on iMac4s, pp. 839-846, 2013.
[9] R. Raju, B. Dahill, K. Runkana, and J. Mungara, “ZRP versus AODV and DSR: A Comprehensive Study on ZRP Performance on MANETs” in Proc. of the 10th IEEE International Conference on Computational Intelligence and Communication Network, pp. 194-199, 2013.
[10] S. S. Rajput And M. C. Trivedi, “Securing ZRP routing protocol in MANET using Authentication Technique,” IEEE International Conference on CICN, pp. 872-877, 2014.
[11] S. Kaur, S. Kaur, “Analysis Of Zone Routing Protocol In Manet,” Int. J. Res. Eng. Technol., vol. 02, no. 09, pp. 520–524, 2015.
[12] S.Kalwar, “Introduction to the reactive protocol,” IEEE Potentials, vol. 29, no. 2, pp. 34-35, 2010.
[13] J. H. Song, V. W. Wong, and V. C. Leung, “Efficient on-demand routing for mobile ad hoc wireless access networks,” IEEE Transactions on Selected Areas in Communications, vol. 22, no. 7, pp. 1374-1383, 2004.
[14] Marc R. Hass, J. Zygmunt, Pearlman, “Determining the Optimal Configuration for the Zone Routing Protocol” Sel. Areas Commun., vol. 17, no. 8, pp. 21–29,1999.
[15] N. Beijar, “Zone Routing Protocol (ZRP),” Netw. Lab.Helsinki Univ.Technol. pp. 1–12, 2002.

G. Singh, J.S. Saini, "Routing In Multi-Channel Allocation Using ZRP for Wireless Mesh Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1677-1682, 2019.

humans make use of facial expression to communicate in their day to day interactions with each other, which comes naturally without much effort. Facial expression is essentially a communication and interaction between humans and where other information like speech is not available; it becomes what one can depend on to transmit emotion or reactions of an individual. Hence, human expression recognition with high recognition is still an interesting task. This study is aimed at implementing face detection and expression recognition using fuzzy rule interpolation (FRI) technique. This follows through a development of specifications for fuzzy rule interpolation in emotion recognition using the viola jones algorithm as the detection algorithm and local binary pattern (LBP) algorithm for the feature extraction. The extended Cohn Kanade (CK+) face database was used for the experimentation of the system. The classification of the various expressions was achieved by the image category classifier of Matlab.

Fuzzy Rule Interpolation, Viola Jones Algorithm, Local Binary Pattern, Human Computer Interaction, Region of Interest, emotions, Compositional Rule of Inference, Sparse Rule

[1]. Vyas, R., Garg, G., “Face recognition using feature extraction and neuro-fuzzy techniques”. International Journal of Electronics and Computer Science Engineering, Vol.1, Issue.4, pp.2048-2056, 2012.
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[3]. Nisha, S. D., “Face Detection and Expression Recognition using Neural Network Approaches”. Global Journal of Computer Science and Technology: F Graphics & Vision, Vol.15 Issue.3, pp.1-7, 2015.
[4]. G. Gîlcă, N.G. Bîzdoacă, “A Fuzzy Approach For Facial Emotion Recognition”. ACTA Universitatis Cibiniensis Vol.67, Issue.1, pp.195-200, 2015.
[5]. G. Gîlcă, N.G. Bîzdoacă, Detecting Human Emotions with an Adaptive Neuro-Fuzzy Inference System. 6th International Conference Computational Mechanics and Virtual Engineering: pp.285-290. 2015.
[6]. Mishra, S. and A. Dhole, Design And Implementation of Facial Expression Recognition Using Adaptive Neuro Fuzzy Classifier. International Journal Of Engineering And Computer Science Vol.5, Issue.8, pp.1-5, 2016.
[7]. Rasoulzadeh, M., “Facial expression recognition using fuzzy inference system”. International Journal of Engineering and Innovative Technology Vol.1, Issue.4, pp.1-5, 2012.
[8]. Khandait, S., et al., Automatic facial feature extraction and expression recognition based on neural network. arXiv preprint arXiv:1204.2073 Vol.2, Issue.1, pp.113-118, 2012.
[9]. Mishra, S. and A. Dhole, “An Effectual Approach for Facial Expression Recognition Using Adaptive Neuro Fuzzy Classifier”. International Journal of Advanced Research in Computer and Communication Engineering Vol.4, Issue.5, pp.3, 2015.
[10]. Johanyák, Z. C., Tikk, D., Kovács, S., Wong, K. W., “Fuzzy rule interpolation Matlab toolbox-FRI toolbox”. In 2006 IEEE International Conference on Fuzzy Systems pp. 351-357, 2006.
[11]. Tikk, D., Csaba Johanyák, Z., Kovács, S., & Wong, K. W., “Fuzzy rule interpolation and extrapolation techniques: Criteria and evaluation guidelines”. Journal of Advanced Computational Intelligence and Intelligent Informatics, Vol.15, Issue.3, pp.254-263, 2011.
[12]. Ioannou, S. V., et al. “Emotion recognition through facial expression analysis based on a neurofuzzy network”. Neural Networks Vol.18, Issue.4, pp.423-435. 2005.
[13]. V. Gomathi, K. Ramar, A. S. Jeevakumar, “Human facial expression recognition using MANFIS model”. World Academy of Science, Engineering and Technology, 50, 2009.
[14]. A. Chaturvedi, A. Tripathi, “Emotion Recognition using Fuzzy Rule-based System”. International Journal of Computer Applications Vol.93, Issue.11, pp.1-4, 2014.
[15]. Rázuri, J. G., et al., Automatic emotion recognition through facial expression analysis in merged images based on an artificial neural network. Artificial Intelligence (MICAI), 2013 12th Mexican International Conference on, IEEE, 2013.
[16]. Y. Guo, et al, “Dynamic facial expression recognition with atlas construction and sparse representation”. IEEE Transactions on Image Processing Vol.25, Issue.5, pp.1977-1992, 2016.
[17]. Jinkal Patel, Tejas Kadiya, “Facial Expression Recognition Using Fuzzy Art”, International Journal of Engineering Development and Research (IJEDR), Vol.3, Issue.4, pp.827-829, 2015
[18]. Suma S L, Sarika Raga, “Real Time Face Recognition of Human Faces by using LBPH and Viola Jones Algorithm”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.5, pp.6-10, 2018.
[19]. G.Sowmiya, V. Kumutha, "Facial Expression Recognition Using Static Facial Images", International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.2, pp.72-75, 2018
[20]. R. R. Damanik, D. Sitanggang, H. Pasaribu, H. Siagian, F. Gulo, “An application of Viola Jones method for face recognition for absence process efficiency”. In Journal of Physics: Conference Series Vol.1007, Issue.1, pp.012013, 2018.
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Williams. D. Ofor, Nuka. D. Nwiabu, Daniel Matthias, "Face Detection and Expression Recognition Using Fuzzy Rule Interpolation," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1683-1689, 2019.

IoT is composed of tiny objects implanted with sensors, actuators and Radio Frequency Identification (RFID) tags which make the best use of the network to offer an extensive choice of applications. The major cause for the rapid expansion of IoT is the economic range of intelligent objects and easy availability of internet services. The sensitive information is accessible over network and thus creating threat to data and information. The information privacy is a critical factor in the growth of IoT. In this paper, we review the basics of privacy in IoT, various issues related to privacy and the technologies which can aid in maintain the security of personal data.

Privacy , Internet of Things, Identification, Privacy-Preserving Mechanism

[1]A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, and M. Ayyash, “Internet of things: A survey on enabling technologies, protocols, and applications”. IEEE Communications Surveys Tutorials, 17(4):2347–2376, Fourth quarter 2015.
[2] I. Andrea, C. Chrysostomou, and G. Hadjichristofi, “Internet of things: Security vulnerabilities and challenges”. In Proc. of 2015 IEEE Symposium on Computers and Communication (ISCC), July 2015.
[3]L. Atzori, A. Iera, and G. Morabito, “The internet of things: A survey”In Computer Networks, 54(15):2787–2805, October 2010.
[4]M.V. Bharathi, R. C. Tanguturi, C. Jayakumar, and K. Selvamani., “Node capture attack in wireless sensor network: A survey”. In Proc. of 2012 IEEE International Conference on Computational Intelligence
Computing Research (ICCIC), December 2012.
[5]F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the internet of things”, In Proc. of the First Edition of the MCC Workshop on Mobile Cloud Computing, August 2012.
[6] C. Bormann, A. P. Castellani, and Z. Shelby, “Coap: An application protocol for billions of tiny internet nodes”. IEEE Internet Computing, 16(2):62–67, March 2012.
[7] G. Gan, Z. Lu, and J. Jiang, “Internet of things security analysis”, In Proc. of 2011 International Conference on Internet Technology and Applications (iTAP), August 2011.
[8] M. Leo, F. Battisti, M. Carli, and A. Neri, “A federated architecture approach for internet of things security”, In Proc. of 2014 Euro Med Telco Conference (EMTC), November 2014.
[9] Y. Liu and G. Zhou, “Key technologies and applications of internet of things”, In Proc. of 2012 Fifth International Conference on Intelligent Computation Technology and Automation (ICICTA), January 2012.
[10] P. Lpez, D. Fernndez, A. J. Jara, and A. F. Skarmeta, “Survey of internet of things technologies for clinical environments”, In Proc. of 2013 27th International Conference on Advanced Information Networking and Applications Workshops (WAINA), March 2013.
[11] R. Mahmoud, T. Yousuf, F. Aloul, and I. Zualkernan, “Internet of things (iot) security: Current status, challenges and prospective measures”, In Proc. of 2015 10th International Conference for Internet Technology and Secured Transactions (ICITST), December 2015.
[12] D. Miorandi, S. Sicari, F. De Pellegrini, and I. Chlamtac, “Internet of things”, in Ad Hoc Networks, 10(7):1497–1516, September 2012.
[13] M. H. Miraz, M. Ali, P. S. Excell, and R. Picking, “A review on internet of things (iot), internet of everything (ioe) and internet of nano things (iont)” In Proc. of 2015 Internet Technologies and Applications (ITA),September 2015.
[14] K. P. N. Puttaswamy, R. Bhagwan, and V. N. Padmanabhan, “Anonygator: Privacy and integrity preserving data aggregation”, In Proc. of the ACM/IFIP/USENIX 11th International Conference on Middleware, Middleware ’10, Berlin, Heidelberg, December 2010, Springer-Verlag.
[15] F. Qiu, F. Wu, and G. Chen, “Privacy and quality preserving multimedia data aggregation for participatory sensing systems” in IEEE Transactions on Mobile Computing, 14(6):1287–1300, June 2015.
[16] X. Ren, X. Yang, J. Lin, Q. Yang, and W. Yu, “On scaling perturbation based privacy-preserving schemes in smart metering systems”, In Proc. of 2013 22nd International Conference on Computer Communication and Networks (ICCCN), July 2013.
[17] J. A. Stankovic, “Research directions for the internet of things”. IEEE Internet of Things Journal, 1(1):3–9, February 2014
[18] K. Zhao and L. Ge., “A survey on the internet of things security”, In Proc. of 2013 9th International Conference on Computational Intelligence and Security (CIS), December 2013.
[19] G.D’Acquisto, J.Domingo-Ferrer, P. Kikiras, V. Torra, Y.-A. De Montjoye, and A. Bourka, “Privacy by design in big data: An overview of privacy enhancing technologies in the era of big data analytics,” arXiv preprint arXiv: 1512.06000, 2015
[20] M. Langheinrich, “Privacy by design principles of privacy-aware ubiquitous systems,” in Ubicomp 2001: Ubiquitous Computing., pp. 273–291. Springer, 2001
[21] S. Spiekermann and L. F. Cranor, “Engineering privacy,” IEEE Transactions on software engineering, vol. 35, no. 1, pp. 67–82, 2009.
[22] S. Lahlou, M. Langheinrich, and C. R¨ocker,“Privacy and trust issues with invisible computers,” Communications of the ACM, vol. 48, no. 3, pp. 59–60, 2005.
[23] V. Tiwari, P. Adkar, “Implementation of IoT in Home Automation using Andriod Application”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 7, Issue 2, pp. 11-16, April 2019.
[24] P. Bhatt, B. Thaker, N. Shah, “A Survey on Developing Secure IoT Products”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue 5, pp. 41-44, October 2018.
[25] G. Kaur, M.Sohal, “IOT Survey: The Phase Changer in Healthcare Industry”, International Journal of Scientific Research in Network Security and Communication, Volume-6, Issue-2, pp. April 2018.
[26] R. Maruthaveni, V. Kathiresan,“A Critical Study on RFID” International Journal of Scientific Research in Network Security and Communication, Volume-6, Issue-2, pp.62-65, April 2018.

J. Kaur, J. Sengupta, "Architectural Layers of Internet of Things and Issues at Different Layers," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1690-1694, 2019.

Wireless Body Area Networks (WBANs) represent one of the most promising approaches for improving the quality of life, allowing remote patient monitoring and other healthcare applications. The deployment of a wireless body area network is a critical issue that impacts both the network lifetime and the total energy consumed by the network. This work deploys the reduction of energy consumption in the Attempt model reduces the path loss ratio to enhance the multiple sensors in WBAN. It provides a good tradeoff between the energy consumption and the number of relays, wireless body area networks in short computing time, thus representing an interesting framework for the dynamic network. Low power devices attached to the body have limited battery life. It is desirable to have energy efficient routing protocols that maintain the required reliability value for sending the data from a given node to the sink. In addition to the processing energy, sensor sensing, transient energy and transmission/reception on/off energy have also been taken into account. The results show improved performance of the routing protocols in terms of energy efficiency.

Wireless body area networks, Energy consumption, Network lifetime, Energy parameters

[1] Crosby, G.V (2012). Wireless body area networks for Healthcare. International Journal of Ad hoc, sensor & ubiquitous computing.
[2] Effatparvar, M (2017). Lifetime maximization in wireless body area sensor networks. Journal of mobile communications.
[3] Hyder Ali.H. (2018). Improving QoS parameters in a wireless sensor network. Journal of engineering and applied sciences.
[4] Javaid. (2014). Energy efficient MAC protocols in wireless body area networks. Journal on wireless engineering and technology.
[5] Khalid, Abu AI –Saud. (2012).Wireless body area networks signal processing. Information processing in Sensor Networks.
[6] Le Yan, L.Z. (2014). Energy comparison and optimization of wireless body area network. Journal of mobile communication networks.
[7] Mobeen Khan. (2017). A security framework for wireless body area network. Wireless communication networks.
[8] Omar Samai, I.A (2014). Energy-aware and stable routing protocol for WBAN networks. Journal of network and computer applications.
[9] Pejman, Niksaz. (2015). Wireless body area networks, attacks and countermeasures. International Journal of scientific & engineering research.
[10] Swamy, R.N (2017). Analysis of various protocols in wireless body area networks (WBAN). International Journal of Electronics and communication engineering and technology.

S. Selvaraj, R. Rathipriya, "Energy Efficiency in Wireless Body Area Networks Using Path Loss Model," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1695-1700, 2019.

With great advancements in technology, topics like machine learning, artificial intelligence, and big data are trending right now. These technologies are touching the lives of millions of people around the world. Data is being produced at exponential rates and computer engineers want to put this data to good use. In this project, we discuss the scope of machine learning and image processing to record the attendance of students in a classroom. This method is extremely efficient compared to the traditional attendance registration wherein the teacher has to manually mark the attendance of each and every student. With this system, the attendance is marked every hour and reports are generated automatically for easy consolidation. A combination of the HOG and Neural Networks are used for detection and recognition in our system. It eliminates the need for paper-based records and helps in quick consolidation.

Facial Recognition; Image Processing; HOG; Neural Networks; Face API; Attendance System

[1] E. Varadharajan, R. Dharani, S. Jeevitha, B. Kavinmathi, S. Hemalatha, “Automatic attendance management system using face detection”, Green Engineering and Technologies (IC-GET), Online International Conference, India, pp.1-3, 2016
[2] T. Ephraim, T. Himmelman, K. Siddiqi, “Real-Time Viola-Jones Face Detection in a Web Browser”, CRV `09 Proceedings of the 2009 Canadian Conference on Computer and Robot Vision, Canada, pp.321-328, 2009
[3] M. Da`san, A. Alqudah, and O. Debeir, “Face Detection using Viola and Jones Method and Neural Networks”, IEEE International Conference on Information and Communication Technology Research, UAE, pp.40-43,2015.
[4] N. K. Jayant, S. Borra, “Attendance management system using hybrid face recognition techniques”
Conference on Advances in Signal Processing (CASP), India, pp.412-417, 2016
[5] H. Rathod, Y. Ware, S. Sane, SurS.esh Raulo, V. Pakhare, I. A. Rizvi, “Automated attendance system using machine learning approach”, 2017 International Conference on Nascent Technologies in Engineering (ICNTE), India, pp.1-5, 2017
[6] T. Li, W. Hou, F. Lyu, Y. Lei, C. Xiao, “Face Detection Based on Depth Information Using HOG-LBP”,
2016 Sixth International Conference on Instrumentation & Measurement, Computer, Communication and Control (IMCCC), China, pp.779-784, 2016
[7] B. Patel, T. Mokadam, S. Kernse, S. Gundety, "Collective Face Recognition System", Vol 2, Issue 2, pp. 1063-1066, 2017
[8] R. Kaur, Himanshi, “Face recognition using Principal Component Analysis”, 2015 IEEE International Advance Computing Conference, India, pp.585-589, 2015
[9] H. Kim, T. Kim, P. Kim, “Interest Recommendation System Based on Dwell Time Calculation Utilizing Azure Face API”, France, pp.1-5, 2018
[10] P. L. Agarwal, D. D. Patil, “Wearable Face Recognition System to Aid Visually Impaired People”, Vol 2, Issue 2, pp.372-376, 2017

Aswin Rajeev, Jaiks Rebbi, Joseph Gavin Correya, Varun V Prabhu, Divya James, "HOG – Neural Network Based Student Attendance System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1701-1705, 2019.

The major issues in water bodies is contamination which results in the reduction of dissolved oxygen level. This leads to various environmental hazards and also affects the aquatic animals. Cleaning water bodies of these waste materials requires manpower and long hours. Instead of wasting manpower at once we can build an autonomous device which can decrease the human effort and interference by periodic cleaning of the water body. This device which is built using the Raspberry Pi is to help people by frequent maintenance of the water bodies with minimalist human interference. An object detection algorithm is used for the detection of trash on water bodies. Computer Vision Technology is used for image processing, object detection and object tracking. The device navigates towards the detected trash with the control signals generated by custom made algorithm. After the device navigates towards the trash, the trash is collected inside the mesh structure built into the structure of the device.

Raspberry Pi, Object Detection, Computer Vision

[1] Samruddhi There, Chetan Shinde, Ashish Kumar Nath, Shubhangi A Joshi, “Garbage detection and collection of garbage using computer vision”, International Journal of Innovations in Engineering Research and Technology,Vol..,Issue., 2017.
[2] Yong Wang, Dianhong Wang, Qian Lu, Dapeng Luo, and Wu Fang, "Aquatic Debris Detection using embedded camera sensors", https://www.mdpi.com/journal/sensors, Vol.15, Issue.15, 2015.
[3] Ms. Prajakta K. Ghorpade, “Aquatic Debris Monitoring & Detection using Raspberry Pi based AQUABOT”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 5, Issue 5, 2016.
[4] Jakkrit Sumroengrit and Niramon Ruangpayoongsak, “Economic Floating Waste Detection for Surface Cleaning Robots”, In the Proceedings of the 2016 the 3rd International Conference on Mechatronics and Mechanical Engineering, 2017.
[5] Chen Zhihong, Zou Hebin, Wang Yanbo, Liang Binyan, Liao Yu, “A Vision-based Robotic Grasping System Using Deep Learning for Garbage Sorting”, In the Proceedings of the 2017 36th Chinese Control Conference, 2017.

Vikas P Damle, Nupur Gudigar, Shreyas P, Sridhar M, Prakash B Metre, "Auto Trash Collection in Water Bodies Using A Smart Device," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1706-1709, 2019.

In this paper, a design is proposed for the automation of lights, temperature etc. using IR, PIR sensor etc. and Arduino with Internet of Things for smart rooms. The proposed work uses IR sensors to detect persons entering the room and switch the lights and other appliances on if there are any occupants in the room. The temperature sensor would be used to read the temperature of the room. The ESP8266 NodeMCU microcontroller Wi-Fi module will be the ‘heart’ of the system to connect all the materials for developing the system. The power consumed by the devices and the appliances will also be calculated and stored using the PLX-DAQ software which extracts the data stored in the memory of the module and stores it in an Excel sheet.

IoT, smart room, power consumption monitoring, Android

[1] G.Mahalakshmi, M.Vigneshwaran, “IOT Based Home Automation Using Arduino”, International Journal of Engineering and Advanced Research Technology (IJEART), Vol.3, pp.7-11, Issue.8, August 2017.
[2] Alamri, A., Ansari, W.S., Hassan, M.M., Hossain, M.S., Alelaiwi, A., Hossain, M.A., “A survey on sensor-cloud: architecture, applications, and approaches”, International Journal of Distributed Sensor Networks 2013.
[3] Das, S.R., Chita, S., Peterson, N., Shirazi, B.A., Bhadkamkar, M.,“Home automation and security for mobile devices,” IEEE PERCOMWorkshops, pp. 141-146, 2011.
[4] Piyare R., “Internet of things: ubiquitous home control and monitoring system using android based smart phone”, International journal of Internet of Things. Vol.2, Issue.1, pp.5-11, Sep-2013.
[5] G. Kortuem, F. Kawsar, D. Fitton, and V. Sundramoorthy, "Smart objects as building blocks for the internet of things," Internet Computing, IEEE, vol. 14, pp. 44-51, 2010.
[6] A. ElShafee and K. A. Hamed, "Design and Implementation of a WiFi Based Home Automation System," World Academy of Science, Engineering and Technology, pp. 2177 2180, 2012.
[7] M.B.Salunke , Darshan Sonar, Nilesh Dengle ,Sachin Kangude, and D. Gawade, "Home Automation Using Cloud Computing and Mobile Devices," IOSR Journal of Engineering, vol. 3, pp. 35-37, 2013.
[8] A. Kamilaris, V. Trifa, and A. Pitsillides, "HomeWeb: An application framework for Web-based smart homes," in Telecommunications (ICT), 2011 18th International Conference on, 2011, pp. 134-139.
[9] R. Shahriyar, E. Hoque, S. Sohan, I. Naim, M. M. Akbar, and M. K. Khan, "Remote controlling of home appliances using mobile telephony," International Journal of Smart Home, vol. 2, pp. 37-54, 2008.

R.N. Diengdoh, S.G. Lamare, N. Choudhury, R. Mandal, L. Lyngdoh, "IoT Based Smart Room For Power Consumption Monitoring And Control," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1710-1713, 2019.

With increasing use of wireless networks there is increase in the number of intruder in networks. This could be explained by taking the example of the Mobile ad-hoc Network, as it is a network with no fixed infrastructure, security is the main issue in this network. During the transmission of data from source to destination, any node in network can act as an intruder .The intruder will not allow the successful transmission of the data. So, Intrusion detection system using cuckoo search and decision tree has been designed to detect the intruder node and exclude it from the network. The outcome has been depicted using MATLAB simulator using 45 to 60 nodes. For these nodes there are number rounds for which nodes are plotted. From each round the affected nodes are calculated on the basis of energy consumed by the nodes. The node which consumes maximum energy is the intruder in the network. Optimization of various QOS parameters is performed using Cuckoo search algorithm. The detected intruder is presented using decision tree.

MANET,Cuckoo search algorithm, Intrusion Detection system,Decision Tree

[1] R. Kumari , P. Nand, “Performance Analysis for MANETs using certain realistic mobility models: NS-2” , International Journal of Scientific Research in Computer Science and Engineering , Vol. 6, No. 1,pp. 70-77, 2018.
[2] R. Kumari , P. Nand, “Performance Analysis of Existing Routing Protocols” , International Journal of Scientific Research in Computer Science and Engineering , Vol. 5, No. 5,pp. 47-50, 2017.
[3] P. Rutravigneshwaran, “A Study of Intrusion Detection System using Efficient Data Mining Techniques”, Int. J. Sc. Res. in Network Security and Communication, Vol. 5, No. 6, pp.5-8, 2017.
[4] C.Zefan, Y.Xiaodong, “Cuckoo Search Algorithm with Deep Search”, In the Proceedings of 2017 3rd IEEE International Conference on Computer and Communications (ICCC 2017),Chengdu, China,pp.2241-2246,2017
[5] M.A.Jabbar, S.Samreen, “Intelligent network intrusion detection using alternating decision trees”, In the Proceedings of 2016 International Conference on Circuits, Controls, Communications and Computing (I4C), Bangalore, India, pp.1-6,2016.
[6] A.Hinds, M.Ngulube, S.Zhu, “A Review of Routing Protocols for Mobile Ad-Hoc Networks (MANET)”, International Journal of Information and Education Technology, Vol. 3, No. 1,pp.1-5, 2013.
[7] Z.Yuan, C.Wang, “An improved network traffic classification algorithm based in Hadoop Decision tree”, In the Proceedings of 2016 IEEE International Conference of Online Analysis and Computing Science (ICOACS), Chongqing, China, pp.53-56,2016.
[8] A.Biradar, R.C.Thool, “Effectiveness of Genetic Algorithm In Reactive Protocols For MANET”, International Journal of Engineering Research & Technology (IJERT), ISSN: 2278-0181, Vol. 2, Issue 7,pp.1757-1761, 2013.
[9] C.Khetmal1, S.Kelkar, N.Bhosale, “MANET: Black Hole Node Detection in AODV”, International Journal of Computational Engineering Research, Vol. 03, Issue 6, pp.79-85,2013.
[10] F.L.Fessant, A.Papadimitriou, A.C.Viana, C.Sengul, E.Palomar, “A sinkhole resilient protocol for wireless sensor networks: Performance and security analysis”, Computer Communications ,Vol.35, Issue 2, pp. 234–248,2012.

N. Kaur, K. Sharma, "An Intrusion Detection System for MANET based on Cuckoo Search Algorithm and Decision Tree," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1714-1719, 2019.

With the increase in the number of the patients of heart diseases, it is important to analyse the heart activity so that we can easily classify and diagnose the disease. Since, the Electrocardiogram (ECG) signals are used for detecting the cardiac diseases so, in this study analyses and classification of ECG signal are done using Support Vector Machine (SVM) and Linear Discriminant Analysis (LDA) data mining techniques. A cleaned ECG signal provides vital information about the heart diseases and ischemic changes that may occur. It provides necessary information about the functional characteristics of the heart. In this paper, R-peaks of the ECG signal is analysed and its optimization is done using the Genetic Algorithm (GA) as the optimization algorithm. The optimized features are selected using this algorithm. Classification of heart disease is done using SVM and LDA data mining techniques. The two cardiac disorder named bradycardia and tachycardia is classified using SVM and LDA techniques. The comparison of these two techniques is performed on the basis of precision value. In this study, SVM showed better results.

ECG Signal, Genetic Algorithm, SVM, LDA

[1] V.K. Gujare , P. Malviya, “Big Data Clustering Using Data Mining Technique”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 5, Issue.2. pp.9-13, 2017.
[2] A. Diker, E. Aci, Z. Comert, D. Avci, E. Kacar, I. Serhatlioglu, “Classification of ECG Signal by using Machine Learning Methods”, In the Proceedings of the 2018 26th Signal Processing and Communications Applications Conference (SIU), Izmir, Turkey, pp. 1-4, 2018.
[3] A. Gaikwad1 , M.S. Panse, “Extraction of FECG from Non-Invasive AECG signal for Fetal Heart Rate Calculation”, Int. J. Sc. Res. in Network Security and Communication, , Vol. 5, Issue.3. pp.109-112, 2017.
[4] P. Guo, X. Wang, Y. Han, “The Enhanced Genetic Algorithms for the Optimization Design”, In the Proceedings of the 2010 3rd International Conference on Biomedical Engineering and Informatics, Yantai, China, pp. 2990-2994, 2010.
[5] R. Varatharajan, G. Manogaran, M.K. Priyan, “A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing”, Multimedia Tools and Applications, Vol.77, Issue.8, pp.10195-10215, 2018.
[6] K. Kaur, L.S. Mann.,“Heart Disease Prediction System Using ANOVA, PCA and SVM Classification”, International Journal of Advanced Research, Ideas and Innovations in Technology, Vol. 2, Issue.3, pp.1-6, 2016.
[7] R. Ceylan, “The Effect of Feature Extraction Based on Dictionary Learning on ECG Signal Classification”, International Journal of Intelligent Systems and Applications in Engineering, Vol.6, Issue.1, pp.40-46, 2018.
[8] C. Venkatesan, P. Karthigaikumar, R. Varatharajan, “A novel LMS algorithm for ECG signal preprocessing and KNN classifier based abnormality detection”, Multimedia Tools and Applications, Vol.77, Issue.8, pp.10365-10374, 2018.
[9] S. Raj, K.C. Ray, “ECG signal analysis using DCT-based DOST and PSO optimized SVM”, IEEE Transactions on Instrumentation and Measurement, Vol.66, Issue.3 pp.470-478, 2017.
[10] T. Debnath, M. Hasan, T. Biswas, “Analysis of ECG signal and classification of heart abnormalities using Artificial Neural Network”, In the Proceedings of the International Conference on Electrical and Computer Engineering, Dhaka, Bangladesh, pp.353-356, 2018.
[11] S. Chattopadhyay, S. Das, A. Nag , J.K. Ray, S. Bhattacharjee, B. Neogi, “Design and Simulation Approach Introduced to ECG Peak Detection with Study on Different Cardiovascular Diseases”, International Journal of Scientific and Research Publications, Vol.2, Issue.12, pp.348-353, 2012.

S. Grover, Shailja, "ECG Signal Classification using Support Vector Machine and Linear Discriminant Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.5, pp.1720-1725, 2019.