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Waste management has become an immense concern in the context of today’s modern cities. Improper waste management leads to unclean, unhygienic conditions in the city hence spreading lots of diseases and leads to improper management of logistic and human resources. However, Internet of Things (IoT) has brought about a revolution in the traditional system to develop a smart city project in various fields. Our proposed idea is for proper waste management and optimization of waste collection and disposal system to avoid scenarios of waste overflow in the context of technology enabled smart cities. In this research work waste bins are divided into three categories namely (i) biodegradable, (ii) non-biodegradable and (iii) metallic. A real-time monitoring of the garbage level inside the waste bin is periodically sent from each location to a centralized cloud platform. Whenever the garbage level inside waste bin reaches the threshold, the waste collection vehicles are routed according to the decreasing order of percentage of waste filled in the dustbins of different areas. The main objective of the project is to save resources and strict constraint of the overflowing of waste bins. In this project, HCSR04 ultrasonic sensors are used with Arduino UNO for developing the prototypes. ESP8266 is used to send real-time sensor data to cloud.

Internet of Things (IoT), Waste Management System, ThingSpeak, Route Optimization, ESP8266, Smart City

[1] Sneha Patil, Snehal Mohite, Aishwarya Patil, Dr. S.D.Joshi, “IoT Based Smart Waste Management System for Smart City,” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 7, Issue 4, pp- 1-6, April 2017.
[2] Nagajothi.S, Durga.J, Jayasurya.K, Vishnuvarshini.M.D, Nadhiya.M, “Smart Waste Management using IOT,” International Journal of Advanced Research in Computer and Communication Engineering( IJARCCE), Vol. 7, Issue 3, pp-160-163, March 2018.
[3] Abhishek Apte, Dhiraj Ghadi,Devang Deasai, Hiral Raveshiya, Nidhi Sanghavi, “Waste Management System using IOT,” International Journal of Recent Trends in Engineering & Research, Vol. 4, Issue 4, pp- 220-229, April 2018.
[4] D.Anuradha , A.Vanitha ,S.Padma Priya ,S.Maheshwari, “Waste Management System Using IoT,” International Journal of Computer Science Trends and Technology (IJCST) – Volume 5 Issue 2, pp- 152-155, Mar – Apr 2017.
[5] Najaf Ali ,M. Muzammul and Ayesha Zafar, “Intelligent System for Garbage collection: IoT technology with Ultrasonic sensor and Arduino Mega,” IJCSNS International Journal of Computer Science and Network Security, Vol. 18 No.9, pp- 102-107, September 2018.
[6] G.Jenilasree, Dr.N.Shenbagavadivu , Dr.M.Bhuvaneswari, “A Study on Automatic Solid Waste Management System for Smart City,” IJCRT1704217 International Journal of Creative Research Thoughts (IJCRT), Volume 5, Issue 4, pp- 1682-1689, November 2017.
[7] Tushar Kaple ,Raghunath Kundgir, Punam Kadam, Mrs. Chaitali Raje, Dr.D.Y. Patil, “Review on Smart Garbage Management for Smart Cities Using IoT,” International Journal of Innovative Research in Computer and Communication Engineering Vol. 5, Issue 4, pp-9019-9023, April 2017.
[8] Akhil Nair .R,Dr. P. Valarmathie, “IOT Based Waste Management System for Smart Cities,” International Journal of Advance Research Ideas And Innovations In Technology, Volume 3, Issue 3, pp-1276-1280, June 2017.
[9] Kasliwal Manasi H, Suryawanshi Smitkumar B, “A Novel Approach to Garbage Management Using Internet of Things for Smart Cities,” International Journal of Current Trends in Engineering & Research (IJCTER), Volume 2, Issue 5, pp-765-769, May 2016.
[10] Krishna Nirde , Prashant S. Mulay , Uttam M.Chaskar, “IoT based solid waste management system for smart city,” International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India, pp- 666-669, 2017.
[11] Parkash , Prabu V, “IoT Based Waste Management for Smart City,” International Journal of Innovative Research in Computer and Communication Engineering, Vol. 4 , Issue 2, pp- 2923-2927, February 2016.
[12] S.A. Mahajan, Akshay Kokane, Apoorva Shewale, Mrunaya Shinde , Shivani Ingale, “Smart Waste Management System using IoT,” International Journal of Advanced Engineering Research and Science (IJAERS),Vol-4, Issue-4, pp-93-95, Apr- 2017.
[13] Andrea Zanella, Nicola But, Angelo Castellani,Lorenzo Vangelista,Michele Zorzi, “Internet of Things for Smart Cities”, IEEE Internet of Things Journal, Volume 1, Issue 1, pp- 22-32, Feb. 2014.
[14] Kanchan Mahajan, Prof J.S. Chitode, ”Waste bin monitoring system using integrated technology”, International Journal of Innovative Research in Science, Engineering and Technology, Vol.20, Issue 1, pp-64-76, Feb 2018.
[15] S.S.Navghane, M.S.Killedar, Dr.V.M.Rohokale, ”IoT based smart garbage and waste collection bin”, international journal of advance research in electronics and communication engineering, volume 5, Issue 5, May 2016.
[16] P. Masek, K. Zeman, Z. Kuder, J. Hosek, S. Andreev, R. Fujdiak F. Kropfl, “Wireless M-BUS: An Attractive M2M Technology for 5G- Grade Home Automation”, In EAI International Conference CYber physiCaL systems, iOt and sensors Networks (CYCLONE 2015). Rome, 2015.
[17] J. Hosek, P. Masek, D. Kovac, M. Ries, F. Kropfl, “Universal Smart Energy Communication Platform,” International Conference on Intelligent Green Building and Smart Grid (IGBSG)-2014, Taipei, Taiwan( IEEE), pp- 1-4, 2014.
[18] M.N.K. Boulos and N.M. Al-Shorbaji, “On the Internet of Things,smart cities and the WHO Healthy Cities”, International Journal of Health Geographics. 2014.
[19] ReTHINK, “Low power networks hold the key to IoT”, Rethink Technology Research Ltd. 2015.
[20] Palaghat Yaswanth Sai, “IOT Smart Garbage Monitoring System in Cities-An Effective Way to Promote Smart City”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 7, Issue 2, pp- 99-102, Feb-2017.
[21] D. V. Rojatkar and P. A. Hande, “IoT Based Garbage Mangement System” International Journal of Trend in Scientific Research and Development (IJTSRD), Vol. 1 Issue 06, pp- 865-867, Oct-2017.
[22] Raghumani Singh, C. Dey, M., “Solid waste management of Thoubal Municipality, Manipur- a case study”, International Conference Green Technology and Environmental Conservation (GTEC 2011), Chennai, pp-21 – 24, 2011.
[23] Ayaskanta Mishra, "Performance Analysis of TCP Tahoe, Reno and New Reno for Scalable IoT Network Clusters in QualNet® Network Simulator", International Journal of Computer Sciences and Engineering, Vol.6, Issue.8, pp.347-355, Aug 2018.
[24] R. Zade, N. Khadgi, M. Kasbe, T. Mujawar, "Online Garbage Monitoring System Using Arduino and LabVIEW", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.6, pp.5-9, Dec-2018.
[25] Bhanudas Suresh Panchabhai, Anand Jayantilal Maheshwari, Sunil DhonduMone, "The Road Map of Cloud Computing to Internet of Things", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.37-42, Jan-2018.

Ayaskanta Mishra, Nisha Ghosh, Pujarini Jena, "Internet of Things based Waste Management System for Smart Cities: A real time route optimization for waste collection vehicles," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.496-503, 2019.

This paper discusses the different classification techniques. It also compares the efficiency of Tree Based Classifiers Random Forest, REP Tree and J48 Classifiers for the detection of masses in mammogram images and compares their robustness through various measures. The mammogram images used in this research have been taken from MIAS database and the classification is performed with the help of open source machine learning tool. Finding the best classifier is a tough task and this paper gives opportunity to researchers to drill down efficient research works for evaluating different classifiers

Mammogram, Classification, Random Forest (RF), REP tree, J48 classifiers

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M. Vasantha, "Comparison of Tree Based Supervised Classification Methods with Mammogram Data Set," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.504-506, 2019.

Maintaining a consistent moisture environment for a product is one of the key objectives in a manufacturing process. Mostly the product moisture is maintained by a temperature sensor which is manually controlled by a human who knows knowledge about the system and also supervising the temperature by human does not assure complete moisture control of the system. To address this problem, a time series model trained with a custom product moisture dataset which can predict the temperature to be maintained with 97% accuracy in the storage system has been implemented in the temperature system. In this time series model, Longest Short Memory Network is used as neural network architecture with some defined hyper parameters to achieve target accuracy.

Time Series Model, LSTM, Moisture Control

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[15] P. Anitha, D. Rajesh, K. Venkata Ratnam, “Machine Learning in Intrusion Detection – A Survey” vol 7, pp 112-119, 2019

V.P. Gladis Pushparathi, D. Robin Reni, M. Selva Kumar, V. Prasanna Kumar, M. Suriyakiran, "Consistent Product Moisture Prediction in Manufacturing using Time Series Analysis," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.507-510, 2019.

A real time communication network having the open access features also suffers from various intruders. These intruders steal the communication information, disrupt the data or slow down the transmission. In this paper, a dynamic signature and parameter map based model is presented for intrusion detection. At earlier stage of this model, the log-event table map is performed to verify the authenticity of node. At this stage, the prevention from any external node is done under activity monitoring. In final stage, the communication observation under multiple parameters is done to identify the intrusion. The query pattern observation is applied in this stage to identify the attacked node and pattern. The proposed work model is simulated in NS2 environment with multiple query patterns. The observations show that the model has identified various attack patterns significantly.

Signature, Query Pattern, Intrusion Detection, Activity Monitoring

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[2 Ying Li," Component-Based Track Inspection Using Machine-Vision Technology", ICMR’11, April 17-20, 2011, Trento, Italy ACM 978-1-4503-0336-1/11/04
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[4] Johann Schlamp," How to Prevent AS Hijacking Attacks", CoNEXT Student’12, December 10, 2012, Nice, France. ACM 978-1-4503-1779-5/12/12
[5] Joshua Goodman," Stopping Outgoing Spam", EC’04, May 17–20, 2004, New York, New York, USA. ACM 1-58113-711-0/04/0005
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[10] Mauro Conti," A Randomized, Efficient, and Distributed Protocol for the Detection of Node Replication Attacks in Wireless Sensor Networks", MobiHoc’07, September 9-14, 2007, Montréal, Québec, Canada. ACM 978-1-59593-684-4/07/0009
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Lavlish Goyal, Nitika, "A Dynamic Analysis Model for Intrusion Detection in Mobile Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.511-515, 2019.

Agriculture is an important component of every individual`s livelihood. During farming infestation of insect pests in crops are inevitable. Manual detection and identification of type is the most challenging process. The proposed architecture will pave a way to develop an automatic detection system to do the identification of pest present in videos. Through this system, the pest infestation can easily be identified and suitable management techniques can be applied early to improve the quality of crops. The proposed architecture incorporates the following techniques for effective detection insects in crops: key frame extraction by calculating a threshold from histogram difference of consecutive frames, best frame selection by finding PSNR and MSE value of key frames, filtering, color image segmentation through K – Means clustering segmentation, feature extraction through Neural Network techniques with a pre trained network VGG19 and classification by using multi class SVM classifier. By using this proposed algorithm, this system identifies five types of pests namely, tuta absoluta, fall armyworm, leaf hopper, epilachna beetle and corn borer with accuracy of 98.46%.

Key frame extraction, K-Means clustering Segmentation, Convolutional Neural Networks (CNN), VGG19, Support Vector Machine (SVM)

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[2] Gondal, Muhammad Danish, Khan, Yasir Niaz , “Early Pest Detection from Crop using Image Processing and Computational Intelligence”, FAST-NU Research Journal (FRJ), Vol.1, Issue. 1, pg. 59-68, 2015
[3] Premalatha C., “Automatic Smart Irrigation System Using IOT”, International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.1, pp.1-5, 2019
[4] Denan Xia, Peng Chen, Bing Wang, Jun Zhang and Chengjun Xie (2018), “Insect Detection and Classification Based on an Improved Convolutional Neural Network”, Sensors MDPI.
[5] Uma Rani, Amsini,“Pest Identification in Leaf Images using SVM Classifier”, International Journal of Computational Intelligence and Informatics, Vol. 6: Issue.. 1, pg. 30-41,2016
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[8] Ghatak, Sanjoy,“Key Frame Extraction using Threshold Technique”, International Journal of Engineering Applied Sciences and Technology, Vol. 1, Issue. 8, pg. 51-56, 2016.
[9] Sladojevic, Srdjan, Arsenovic, Marko, Anderla, Andras, Culibrk, Dubravko and Stefanovic, Darko, “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification”, Hindawi Publishing Corporation Computational Intelligence and Neuroscience, Vol. 2016, pg. 1-11, 2016
[10] Sreelakshmi M, Padmanayana ,“Early Detection and Classification of Pests Using Image Processing”, International Journal of Innovative Research in Electrical, Electronics, Instrumentation and Control Engineering, Vol. 3, Issue. 1, pg. 239-242, 2015

S. Iswarya, S. Pitchumani Angayarkanni, "Detection, Classification and Identification of Pest in Crops Using Video Segmentation Techniques," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.516-520, 2019.

Deep learning has been playing a crucial role in making applications much smarter than before and more reliable. The reliability of a model can be marked out using parameters like accuracy. Recurrent Neural Networks, is a complicated deep learning model, which can be hard to develop but can be more reliable if properly trained. A good collection of data alone cannot give good accuracies. Fuzzy Logic is a statistical approach that can be used to mold the data based on the degree of truth. Gini index based fuzzification is a technique that builds the data by finding relations within the data and then fuzzifying it. In this paper, the gini index based fuzzification is applied on the data set and this fuzzified data is used in training and testing the RNN model. Here, better Accuracy is observed for RNN model with fuzzy data compared to the actual data.

Deep Learning, Recurrent Neural Networks, RNN, Fuzzy logic, Gini Index

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S.V.G. Reddy, K. Thammi Reddy, V. Valli Kumari, P. Sanjay Varma, S.V.S. Nitish Kumar Gupta, "A Recurrent Gini Index based Fuzzy Neural Network," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.521-525, 2019.

Network Intrusion Detection System (NIDS) is one of the best solutions against network attacks. Attackers also dynamically change tools and technologies. Powerful network security analytics is not a function of making use of simply one approach. To detect emerging threats, a network intrusion detection system should be able to use a mixture of techniques. In this research we start off evolved via collecting the proper information for complete visibility and the usage of analytical strategies along with behavioral modeling and deep learning. All that is supplemented by means of global threat intelligence that is aware about the malicious campaigns and maps the suspicious conduct to a recognized threat for extended fidelity of detection. In this research we prepare multichannel deep learning approach and incremental learning approach to enhance detection rate.

Network Intrusion Detection System, Network Security, Deep Learning, Threat Intelligence, Performance Evolution.

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[2] Choudhury, Sumouli, and AnirbanBhowal. "Comparative analysis of machine learning algorithms along with classifiers for network intrusion detection." Smart technologies and management for computing, communication, controls, energy and materials (ICSTM), 2015 International conference on. IEEE, 2015.
[3] Hamid, Yasir, M. Sugumaran, and LudovicJournaux. "Machine learning techniques for intrusion detection: a comparative analysis." Proceedings of the International Conference on Informatics and Analytics. ACM, 2016.
[4] Haq, Nutan Farah, et al. "Application of machine learning approaches in intrusion detection system: a survey." IJARAI-International Journal of Advanced Research in Artificial Intelligence 4.3 (2015): 9-18.
[5] Dhanabal, L., and S. P. Shantharajah. "A study on NSL-KDD dataset for intrusion detection system based on classification algorithms." International Journal of Advanced Research in Computer and Communication Engineering 4.6 (2015): 446-452.
[6] Buczak, Anna L., and ErhanGuven. "A survey of data mining and machine learning methods for cyber security intrusion detection." IEEE Communications Surveys & Tutorials 18.2 (2016): 1153-1176.
[7] Belavagi, Manjula C., and BalachandraMuniyal. "Performance evaluation of supervised machine learning algorithms for intrusion detection." Procedia Computer Science 89 (2016): 117-123.
[8] Shanmugavadivu, R., and N. Nagarajan. "Network intrusion detection system using fuzzy logic." Indian Journal of Computer Science and Engineering (IJCSE) 2.1 (2011): 101-111.
[9] Shone, Nathan, et al. "A deep learning approach to network intrusion detection." IEEE Transactions on Emerging Topics in Computational Intelligence 2.1 (2018): 41-50.
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[26] Suman Sharma, Yogesh Verma, Amit Nadda, "Information Security: Cyber Security Challenges", International Journal of Scientific Research in Computer Science and Engineering, Vol.7, Issue.1, pp.10-15, 2019
[27] Shailja Sharma, Sheeba Khan, "Analysis of Cloud Security, Performance, Scalability and Availability (SPSA)", International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.1, pp.13-15, 2019

Kushal Jani, Punit Lalwani, Deepak Upadhyay, M. B. Potdar, "Network Intrusion Detection System using Threat Intelligence and Deep Learning Approach," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.526-531, 2019.

The primary aim of this paper is to efficiently act as a substitution of voice and ear by translating gestures performed by hand to the corresponding text which can be understood by any person who can read. The purpose of this research is to analyze and evaluate how the device can reduce the difficulty in Communication among people having listening and speech disability and find out the limitations of the device in comparison to the other technologies and devices working towards the similar objective. Their communications with others only involve the use of motion by their hands and expressions. We have designed a technique called artificial speaking mouth for dumb people. It will be very helpful for them to convey their thoughts to others. For mute people, there is a meaning behind every motion this message is kept in a database. So when the information generated by the flex sensors as per the gesture is being fed to the microcontroller. Microcontroller Atmega 328 matches the gesture data with the data in the database and the relevant gesture name is determined from the output.

artificial speaking mouth, Atmega 328, Microcontroller

[1] “Overview: human-computer interaction An globally uses technique in society” Nidhi manchanda, sarita, saurabh mukherjee
[2]“Flex sensor based robotic arm controller using micro controller” Abidhusain syed, zamrrud taj h agasbal, thimmannagouday melligeri, bheemesh gudur
[3] “Embedded control for manipulating video game objects in real time Using hand gesture recognition” Pranav pawar, kiran tupe, sanket somvanshi, vishal shinde, sudam pawar
[4] “Giving voice to mute people “Ijariie” issn(o)-2395-4396, A.h. ansari, jori ashwini sanjay, dolas ashwini shesherao , gaikwad prajakta chandrakant
[5] “Communication interface for mute and hearing impaired people”Issn 2278-0181, garima rao , laksh narang ,abhishek solanki, kapil singh , karamjit kaur , neeraj gupta
[6] “Speaking system to mute people using hand gesture “P-issn: 2395-0072 e-issn: 2395-0056, S .k imam basha , s ramasubba reddy
[7]”Design and development of hand gesture recognition system “Kavitha sood, kathakali majumder, aishwarya muralidharan, ayesha sultana, aliya farheen
[8] “Hand gesture recognition and voice conversion system for dumb people “ v.padmanabhan m.sornalatha

Pallavi Nagarkar, Pravar Chaturvedi, Bhumika Neole, "Hand Gesture Speaking Unit for Mute People," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.532-535, 2019.

Programming building and advancement is outstanding to experience the ill effects of impromptu extra minutes, which causes pressure and sickness in designers and can prompt low quality programming with higher imperfections. As of late, we presented a multi-target choice help way to deal with assistance balance venture dangers and term against extra time, so programming designers can more readily design additional time. This methodology was observationally assessed on six genuine programming ventures and looked at against best in class developmental methodologies and right now utilized extra time techniques. The outcomes demonstrated that our proposition serenely beated every one of the benchmarks considered. This paper broadens our past work by exploring versatile multi-target ways to deal with meta-heuristic administrator choice, in this manner expanding and enhancing algorithmic execution. We additionally stretched out our experimental examination to incorporate two new true programming ventures, along these lines improving the logical proof for the specialized execution claims made in the paper. Our new outcomes, over each of the eight tasks contemplated, demonstrated that our versatile calculation beats the considered cutting edge multi-target approaches in 93 percent of the analyses. The outcomes likewise affirm that our methodology essentially beats current additional time arranging rehearses in 100 percent of the trials.

Software building, the executives, arranging, look based programming designing, venture booking, additional time, hyper heuristic, multi-objective transformative calculations

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Rahat Parween, "Overtime Planning Using Evolutionary Algorithms in Software Development," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.536-539, 2019.

Summary Spam SMS is unwanted messages to users who are worried and harmful from time to time. Currently, group survey papers are available on SMS detection techniques. Study and review their used techniques, approaches and algorithms, their advantages and disadvantages, evaluation measures, discussion of data sets, as well as the end result of the studies. Although the SMS spam detection techniques are additionally demanding as sms spam detection techniques, as the local content, the use of abbreviated words, unfortunately does not meet any of the existing research on these challenges. There is an enormous amount of emerging research in this region and this survey can serve as a point of reference for the upcoming direction of research.

Mobile SMS spam detection

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Ashok Koujalagi, "Mobile SMS Spam Recognition Using Machine Learning Techniques with the help of Biasian and Spam Filters," International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.540-542, 2019.