Volume-8 , Issue-4 , Apr - 2020 Go Back

In past years, the human population living in urban areas has been increasing. This has created large quantities of waste. Since civic authorities tend to overlook this major concern, the untreated waste causes pollution in our environment and creates unhygienic conditions for the citizens. If this waste is treated properly then it becomes useful for us. Proper handling of this situation by our method, can lead to cleaner surroundings for the citizens, efficient treatment of the waste and it can also be financially profitable for the authorities. Our method proposes segregation of waste into various categories based on its property. We accomplish this by using a system made of multiple sensors embedded with a micro-controller which can classify various types of materials. The system segregates waste into dry, wet, metallic and non metallic categories. This leads to better processing of each category separately, which is a more efficient method. The segregated waste can be reused, recycled or used in landfills depending on its category. Also our system monitors the fill level of waste in the dustbins so that it can be collected on time by the concerned authorities. This method leads to better utilisation of waste.

Sensor technology, waste segregation system, metal waste segregation, automated waste segregation, residual waste, recyclable waste, wet waste, dry waste, moisture sensor, ultrasonic sensor, inductive proximity sensor etc.

[1]. Nurul Nazihah Ahamad, Sarah Yasmin Mohamad, Nur Shahida Midi, Siti Hajar Yusoff, Faridah Abd Rahman “Discrimination of Residual and Recyclable Household Waste for Automatic Waste Separation System” 2018 7th International Conference on Computer and Communication Engineering (ICCCE)
[2]. Rajkamal R, Anitha V, Gomathi Nayaki P, Ramya K, Kayalvizhi E, “A Novel Approach For Waste Segregation At Source Level For Effective Generation Of Electricity – GREENBIN,” International Conference on Science, Engineering and Management 2014
[3]. Shamin N, Raghavendra R-“Smart Garbage Segregation & Management System Using Internet of Things(IoT) & Machine Learning(ML) 1st International Conference on Innovations in Information and Communication Technology (ICIICT),2019
[4]. Marloun Sejera, Joesph Bryan Ibarra, Anrol Sarah Canare, Lyra Escano, Dianne Claudinne , Mapanoo, John Phillip Suaviso, “Standalone Frequency Based Automated Trash Bin and Segregator of Plastic Bottles and Tin Cans,” 2016 IEEE Region 10 Conference(TENCON).
[5]. Balaji Masanamuthu, Chinnathurai, Ramakrishna Sivakumar, Sushuruth Sadagopan, James M. Conrad, “Design and Implementation of a Semi-Autonomous Waste Segregation Robert,” 2016 IEEE.
[6]. Shinjini Ray, Sayan Tapadar, Suhrid Krishna Chatterjee, Robin Karlose, Sudipta Saha, Himadri Nath Saha. "Optimizing routine collection efficiency in IoT based garbage collection monitoring systems", 2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC), 2018
[7]. 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, 2018.

A.V. Balpande, H.S. Sathawane, S.S. More, S.S. Bisht, "Robust and Automatic Waste Segregator," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.149-152, 2020.

Giving students a chance to select a subject of their choice is becoming popular day by day. Elective subjects provide this chance and are increasingly a key part of the progress of a student in their academics. Various universities offer different subjects which belong to various areas of studies. Opting for the best field of study definitely plays a driving role in every student’s career. The proposed system titled “Elective Subject Recommendation System” is a web application for suggesting the best elective subject, among all their academic elective subjects, in which that particular student could have a scope of scoring more. It mainly focuses on the tests that will be taken to analyze the student’s basic knowledge in the respective field. Then the elective subject is recommended using the random forest algorithm. The objective of the project is to let every student opt the elective subjects based on their capability and knowledge but not by the choice of their fellow students.

Randomforest, collaborative filtering

[1]. Rishi Kumar Dubey, Umesh Kumar Pandey, “Elective Subject Selection Recommender System”, International Journal on Recent and Innovation Trends in Computing and Communication, Vol.5, Issue.7,pp.306 – 310, 2017.
[2]. Narimel Bendakir and Esma A ̈ımeur, “Using Association Rules for Course Recommendation”, American Association for artificial intelligence, USA, 2006.
[3]. Pawan S. Wasnik, S.D.Khamitkar, Parag Bhalchandra, S. N. Lokhande, Ajit S. Adte, “An Observation of Different Algorithmic Technique of Association Rule and Clustering”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Special Issue.1, pp.28-30, 2018.
[4]. Michael P. O’Mahony,Barry Smyth, “A Recommender System for On-line Course Enrolment:An Initial Study”, RecSys `07: Proceedings of the 2007 ACM conference on Recommender systems, USA, pp.133-136, 2007.
[5]. Khairil Imran Ghauth, Nor Aniza Abdullah, “Learning materials recommendation using good learners’ ratings and content-based filtering”, Education Tech Research Dev, pp.58:711–727, 2010.
[6]. Michael J. Pazzani and Daniel Billsus, “Content-based Recommendation Systems”, The Adaptive Web, Berlin, pp. 325-341, 2007.
[7]. Dou Gui-Qin, Zhu Yan-Song, Han Yu-Min, “Research On Selection System Based on Bayesian Recommendation Model”, Proceedings of the 2011 International Conference on Advanced Mechatronic Systems, Zhengzhou, China, pp. 35 – 38, 2011.
[8]. SongJie Gong, “A Collaborative Filtering Recommendation Algorithm Based on User Clustering and Item Clustering”, Journal of software vol. 5, Issue. 7, pp. 745 – 752, 2010.
[9]. Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms”, Proceedings of the 10th international conference on World Wide Web, Hong Kong, pp. 285 – 295, 2001.
[10]. Grewal DS and Kaur K, “Developing an Intelligent Recommendation System for Course Selection by Students for Graduate Courses”, Business and economics journal, Vol. 7, Issue. 2, pp. 1 – 9, 2016.
[11]. Oyelade, O. J, Oladipupo, O. O, Obagbuwa, I. C, “Application of k-Means Clustering algorithm for prediction of Students’ Academic Performance”,(IJCSIS) International Journal of Computer Science and Information Security,Vol.7, Issue.1, pp. 292 – 295, 2010.
[12]. Francesco Ricci, Lior Rokach, Bracha Shapira, Paul B.Kantor, “Recommender Systems Handbook”, Springer New York Dordrecht Heidelberg London, pp. 64-66, 2011.
[13]. Sanjog Ray and Anuj Sharma,”A Collaborative Filtering Based Approach for Recommending Elective Courses” ,ICISTM 2011 © Springer-Verlag Berlin Heidelberg, pp. 330–339, 2011.
[14]. Kumar R., “Candidate Job Recommendation System”, International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.6, pp.12-15, 2018.
[15]. S.N. Patil, S.M. Deshpande, Amol D. Potgantwar, “Product Recommendation using Multiple Filtering Mechanisms on Apache Spark”, Vol.5, Issue.3, 2017.

Savita Sangam, Riya Uchagaonkar, Sridhari Yayavaram, Minal Chavan, "Elective Subject Recommendation System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.153-155, 2020.

Generating a description of an image is called image captioning. Image captioning requires recognizing the important objects, their attributes, and their relationships in an image. This process has many potential applications in real life. A noteworthy one would be to save the captions of an image so that it can be retrieved easily at a later stage just on the basis of this description. In this survey article, we aim to present a comprehensive review of existing deep-learning-based image captioning techniques. We discuss the foundation of the techniques to analyze their performances, strengths, and limitations. We also discuss the datasets and the evaluation metrics popularly used in deep-learning-based automatic image captioning.

Image Captioning, Deep Learning, Encoder, Decoder

[1]. MD. ZAKIR HOSSAIN, FERDOUS SOHEL, MOHD FAIRUZ SHIRATUDDIN, and HAMID LAGA, “A Comprehensive Survey of Deep Learning for Image Captioning”, ACM Computing Surveys, Vol. 51, No. 6, Article 118, February 2019.
[2]. Zhihong Zeng, Xiaowen Li, “Application of human computing in image captioning under deep learning”, Springer Nature 2019, May 2019.
[3]. Xianhua Zeng, Li Wen, Banggui Liu, Xiaojun Qi, “Deep Learning for Ultrasound Image Caption Generation based on Object Detection”, Neurocomputing (2019), doi: https://doi.org/10.1016/j.neucom.2018.11.114, Nov 2018.
[4]. Christian Otto, Matthias Springstein, Avishek Anand, Ralph Ewerth, “Understanding, Categorizing and Predicting Semantic Image-Text Relations”, ICMR ’19, Ottawa, ON, Canada , June 10–13, 2019.
[5]. Xinyu Xiao, Lingfeng Wang, Kun Ding, Shiming Xiang, and Chunhong Pan, “Deep Hierarchical Encoder-Decoder Network for Image Captioning”, DOI 10.1109/TMM.2019.2915033, IEEE Transactions on Multimedia, 2019.
[6]. Yuting Su, Yuqian Li, Ning Xu, An-An Liu, “Hierarchical Deep Neural Network for Image Captioning”, Springer Science+Business Media, LLC, Springer Nature , 2019.
[7]. CHENG WANG, HAOJIN YANG, and CHRISTOPH MEINEL,” 40 Image Captioning with Deep Bidirectional LSTMs and Multi-Task Learning”, ACM Trans, Multimedia Comput. Commun., Appl. 14, 2s, Article 40, April 2018.
[8]. Xiaoxiao Liu, Qingyang Xu, Ning Wang, “A survey on deep neural network-based image captioning”, https://doi.org/10.1007/s00371-018-1566-y, Springer Nature, 2018.
[9]. Vasiliki Kougia, John Pavlopoulos, Ion Androutsopoulos, “A Survey on Biomedical Image Captioning”, arxiv:1905.13302v1, May 2019.
[10]. Peter Anderson, Xiaodong He, Chris Buehler, Damien Teney, Mark Johnson, Stephen Gould, Lei Zhang, “Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering”, IEEE Explore, 2019.
[11]. Justin Johnson, Agrim Gupta, Li Fei-Fei, “Image Generation from Scene Graphs”, IEEE Explore, 2019.
[12]. Yang Feng, Lin Ma, Wei Liu, Jiebo Luo, “Unsupervised Image Captioning”, IEEE Explore, 2019.
[13]. Songtao Ding , Shiru Qu , Yuling Xi , Arun Kumar Sangaiah , Shaohua Wan, “ Image caption generation with high-level image features”, Pattern Recognition Letters 123 (2019) 89–95, Mar 2019.
[14]. Lin Ma, Wenhao Jiang, Zequn Jie, Yu-Gang Jiang, and Wei Liu, “Matching Image and Sentence with Multi-faceted Representations”, DOI 10.1109/TCSVT.2019.2916167, IEEE Transactions on Circuits and Systems for Video Technology, 2019.
[15]. Lun Huang, Wenmin Wang, Gang Wang, “IMAGE CAPTIONING WITH TWO CASCADED AGENTS”, ICASSP 2019, IEEE, 978-1-5386-4658-8/18, 2019.
[16]. Alexander G Schwing Jyoti Aneja, Aditya Deshpande. 2018. Convolutional image captioning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5561–5570.
[17]. Peter Anderson, Basura Fernando, Mark Johnson, and Stephen Gould. 2016. Spice: Semantic propositional image caption evaluation. In European Conference on Computer Vision. Springer, 382–398.
[18]. Xinlei Chen and C Lawrence Zitnick. 2015. Mind’s eye: A recurrent visual representation for image caption generation. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2422–2431.
[19]. Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bahdanau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder approaches. In Association for Computational Linguistics. 103–111.
[20]. Junyoung Chung, Caglar Gulcehre, KyungHyun Cho, and Yoshua Bengio. 2014. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555.

Anurag, Naresh Kumar, "A Comparative Study of Various Deep Learning Techniques Based on Automatic Image Captioning," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.156-160, 2020.

Chatbot is another new way for people to interact with a computer system. Traditionally, to get a reply by a software program involved using a search engine. Humans simply ask questions to the chatbot to get answered by the chatbot in a most efficient way. This Paper Builds a general purpose that makes conversations between user and computer. Initially the Chatbot has Given all instructions Related to your Computer. If User doesn`t Know How to Work with a computer he can ask any related thing about it. The Bot we are trying to build is related to computers i.e. Windows OS version 10 and Above.

Chatbot , Generative-based model, Retrieval-based model

[1] Alvaro Nuez Ezquerra “Implementing ChatBot using Neural Machine Translation techniques” 2018.
[2] Alexander Bartl and Gerasimos Spanakis “A retrieval-based dialogue system utilizing utterance and context embeddings” 2017.
[3] K.Jwala, G.N.V.G Sirisha, G.V. Padma Raju “Developing a chatbot using Machine Learning” 2019.
[4] Merva Chkroun, Amos Azaria ‘Did I Say Something Wrong?’ : Towards a Safe Collaborative Chatbot” 2018.

N. Lokeshwari, G. Harsha Vardhan, G. Rahul, A.V.V. Manasa, N. Mounica, "System Control ChatBot," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.161-163, 2020.

In today`s world, everything is dependent on technology. From small to large work, we use technology. But still in some school and colleges, the attendance is taken on paper or sheets. Also other information related to students or teachers are stored taken on papers. This is very tedious job. Also lots of time get wasted due to these activities. So there can be risk of information loss. Hence this is also need to be updated with the technology. So it will be helpful for schools and colleges to maintain the records. So "Android Based Student Information System" can store student information and also used for marking students attendance. It has three modules namely Admin, Teacher and Student. So this can be very effective for teachers. The overhead can be reduced from the teachers as they need not have to waste their time by taking the attendance on paper or storing the information related to student on paper. Also it will give the students, the information about themselves. So this will be useful application for both teachers and students. Keywords— Android, Firebase, Java, Student details, Attendance, Marks etc

Android, Firebase, Java, Student details, Attendance, Marks etc

[1] Chandralekha G.C. , Geeta Kalshety, Suma Paddki, Anuradha T. , “Android Based Student Information System”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 6, Issue. 8, pp. 379-382, August 2017.
[2] Freya Vora, Pooja Yadav, Rhea Rai, Nikita Yadav, “Android Based Mobile Attendance System”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 6, Issue. 2, pp. 369-371, February 2016.
[3] Mohammed A. Jabbar Hameed, “Android Based Smart Student Attendance System”, International Research Journal of Engineering and Technology, Vol. 4, Issue. 12, pp. 1-5, December 2017.
[4] Deepak Rathore, A. Julwania, D.K. Dixit, “AMS: Attendance Management System using Android Environment”, International Journal of Scientific Research in Computer Sciences and Engineering (IJSRCSE), Vol. 4, Issue.2, pp. 20-25, Apr-2016

H.S. Chavan, A.A. Chavan, S.V. Karangutkar, S.M. Melasagare, "Android Based Student Information System," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.164-166, 2020.

In the world of information we live in, cyber security is of paramount importance. Despite communicating through secure channels, there might be breaches that might be targeted by attackers to extract valuable data. Designing and betterment of these symmetric and asymmetric algorithms remain an open challenge to all. The Bitwise Positional Encryption Algorithm is a completely new idea in this field. This simple encryption technique provides strong protection against any kind of attack, be it brute force or statistical attacks. This multilevel encryption algorithm employs several bit-level encryption procedures and matrix transposition operations. It takes into consideration positional parameters of bits, which is further shuffled randomly and hence is impossible for the intruder to decrypt without knowing the key as well as the exact method. The key here should be a shared parameter, for this is an instance of symmetric encryption. The algorithm works on bit-level so it is not possible to break easily. By this algorithm, one can encrypt any kind of file with extensions like .txt, .doc, .jpg, .png and the like. The present method can be used for encryption of any confidential messages like OTP (One Time Password), ATM transactions etc. It is not possible to get back the original plain text file if there is one change in bits in encrypted text. The testing is done on almost all types of files and it was found that the method is working satisfactorily.

Plain text, ciphertext, encryption, decryption, key, transposition, positional extraction.

[1] William Stallings, “Cryptography and Network Security: Principles and Practice”, Tata Mc-Graw Hill Publishing LTD.
[2] Behrouz A. Forouzan, “Cryptography and Network Security”, Special Indian edition 2007, Tata Mc-Graw Hill Publishing LTD.
[3] Ronald A. Gove, “Introduction to Encryption Technology.”
[4] Dan Boneh, Victor Shoup, “A Graduate Course in Applied Cryptography.” from Stanford University.
[5] Sachin Sharma, Jeevan Singh Bisht, “Performance Analysis of Data Encryption Algorithms”, Volume-3, Issue-1, pp. 1-5, 2015.
[6] M. Arora, S. Sharma, “Synthesis of Cryptography and Security Attacks”, Volume-5, Issue-5, Oct 2017.
[7] Sreeparna Chakrabarti, Dr. G.N.K. Suresh Babu, “A Literature Survey on the Cryptographic Encryption Algorithms for Secured Data Communication.” in International Journal on Future Revolution in Computer Science & Communication Engineering Volume: 4 Issue: 10.
[8] Asoke Nath, Soumyadip Ray, Salil Anthony Dhara, Sourav Hazra, “3-Dimensional Bit Level Encryption Algorithm Version-3 (3DBLEA-3)”, International Journal of Latest Trends in Engineering and Technology Vol.(10)Issue(2), pp.347-353
[9] Bit Level Encryption Standard(BLES) : Version-I, Neeraj Khanna, Dripto Chatterjee, Joyshree Nath and Asoke Nath, International Journal of Computer Applications(IJCA)(0975-8887) USA Volume 52-No.2.,Aug, Page.41-46(2012).
[10] Multi Way Feedback Encryption Standard Ver1, Purnendu Mukherjee, Prabal Banerjee, Asoke Nath, IJACR, published in September 2013 issue.

Asoke Nath, Sankar Das, Oishi Mazumder, Adrija Saha, Monimoy Ghosh, "A New Bit Level Positional Encryption Algorithm (NBLPEA ver-1)," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.167-172, 2020.

Wireless communications is the fastest growing segment of the communication industry. The most common used wireless communication is mobile communication. But, there are many technical challenges that must be overcome. In A signal transmitted on a wireless channel is subject to Interference, Fading, Propagation path loss, Shadowing etc. There is always a greater order for capacity with the high quality service. In this situation, OFDM is well defined technique, which is a very much suitable option for high band width data transmission, by converting the wideband signal into narrow band signals for transmission. The transmission of these individual narrow band signals are executed with orthogonal carrier. In this dissertation, the performance of transmission mode are evaluated by Bit Error Rate versus the Signal to Noise Ratio under frequently used Rayleigh channel modes,. In order to investigate, first we derive the mathematical modeling for bit error rate and signal to noise ratio of OFDM over Rayleigh then, OFDM is design. now we have assumed Rayleigh fading channel as noise channel and also built BPSK, QPSK and QAM modulation technique. OFDM transmitters and receivers are implemented here using IFFT and FFT of size 64 with 52 sub carriers to convert the spectra to time domain & vice versa and also measure peak to average power ratio in different modulation scheme.

PAPR, Digital Audio Broadcasting (DAB) ,(OFDM) orthogonal frequency division multiplexing ,MIMO ,Low Density Parity check(LDPC) and Complementary cumulative distribution function(CCDF) ,Digital amplitude modulation (DAM), ACLR,OOB

[1] Vipin Kumar, Dr.Praveen, Anupama“Performance Analysis of MIMO OFDM system using BPSK &QPSK modulation techniques under Rayleigh Fading Channel” IJARCET 2016.
[2] ArunAgarwal, KabitaAgarwal “Performance evaluation of OFDM based WiMAX (IEEE 802.16d) system under diverse channel conditions” IEEE Jan 2015
[3] Tareq Y. Al-Naffouri ,Ahmed A. Quadeer “Impulse Noise Estimation an Removal for OFDM Systems” IEEE Transactions on communication (Volume:62 , Issue: 3 ) February 2014
[4] Parul Wadhwa , Gaurav Gupta, “BER Analysis & Comparison of Different Equalization Techniques for MIMO-OFDM System”, International Journal of Advanced Research in Computer Science and Software Engineering, (IJARCSSE), Volume 3, Issue 6, June 2013.
[5] Vishal Anand, Sharmeele Thanagjam, Sukhjeet Kaur, “Analysis of OFDM BER and PAPR using TURBO CODE”, IJCSNS International Journal of Computer Science and Network Security, VOL.12 No.3, March 2012.
[6] Ligata, Haris Gacanin, Fumiyuki Adachi, Miha Smolnikar, and Mihael Mohorcic, “Bit Error Rate Analysis for an OFDM System with Channel Estimation in a Nonlinear and Frequency Selective Fading Channel”, EURASIP Journal on Wireless Communications and Networking, 2011.
[7] Vivek K. Dwivedi and G. Singh, “An Efficient BER Analysis of OFDM Systems with ICI Conjugate Cancellation Method”, Progress In Electromagnetics Research Symposium, Cambridge, USA, July 2-6, 2008.
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Milind Sharma, Shiv Kumar, Ankit Navlakha, "Enhanced Performance Analysis of OFDM, Measuring Bit Error Rate and Peak to Average Power Ratio using Different Modulation," International Journal of Computer Sciences and Engineering, Vol.8, Issue.4, pp.173-179, 2020.