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With the growth of the Internet community, textual data has proven to be the main tool of communication in human-human interaction. This communication is constantly evolving towards the goal of making it as human and real as possible. One way of humanizing such interaction is to provide a framework that can recognize the emotions present in the communication or the emotions of the involved users in order to enrich user experience. The use of social networking sites is one of the approaches for putting views of user. Proposed emotion detector system takes a text document or audio and the emotion word ontology as inputs and produces the scores of six emotion classes (i.e. happy, sad, fear, surprise, anger and disgust) as the output; for twitter data as input the extracted tweets are categorized in to positive, negative and neutral tweets.

Human-Computer Interaction; Textual Emotion Recognition; speech analysis; twitter analysis; Emotion Word Ontology

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P. Rani, M.V. Jagannatha Reddy, K.S.M.V. Kumar, Sreedhar S.B., "Detecting Human Emovere through Data Mining," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.64-69, 2020.

Voice disorders are abnormal characteristic of sound produced by larynx involving pitch, intensity, loudness. Nowadays Voice disorders are one among rapidly spreading diseases. Disordered quality of voice could also be a symptom for laryngeal diseases. The goal of this work is to build a model to identify the types of voice disorders that includes Normal, Dysphonia, Stammering and Vocal palsy. To deal with this classification problem, Machine learning classifier Support Vector Machine (SVM) is used. The results are evaluated in terms of accuracy, sensitivity, specificity and ROC based on the features extracted using Mel Frequency Cepstral Coefficients (MFCCs), they are the cepstral representation of audio clip.

Voice disorders, Machine Learning, Classification, SVM, MFCC

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[15] Gourish Malage, Kiran Kumari Patil, “A Voice based Farmer Information System”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.7, Issue.6, pp.220-224,2019.
[16] Sujitha Perumal, Mohammed Saqib Javid, “Voice Enabled Smart Home Assistant for Elderly”, International Journal of Scientific Research in Computer Sciences and Engineering, Vol.7, Issue.11, pp. 30-37, 2019.
[17] M. Mat Baki, G. Wood, M. Alston, P. Ratcliffe, G. Sandhu, J. Rubin, and M. Birchall, “Reliability of operavox against multidimensional voice program (mdvp)”, Clinical Otolaryngology, vol. 40, no. 1, pp. 22–28, 2015.

Arpitha M.S., Nagarathna, "A Framework for Classification of Vocal Disorders without Clinical Intervention," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.70-73, 2020.

Each person’s fingerprint structure is unique and is developed for biometric authentication systems than others because fingerprints have advantages such as: feasible, differ from each other (distinct), permanent, accurate, reliable and acceptable all over the world for security and person identity. Fingerprints are considered legitimate proofs of evidence in courts of law all over the world. Fingerprint based gender classification can be studied using spatial domain and frequency domain approach. Spatial domain approach uses ridge related parameters like ridge count, ridge density, ridge width, ridge thickness to valley thickness ratio. Frequency domain approach do not work on physical parameters related to ridge, but work on measuring parameters like frequency and region parameters of an image. This paper compares one method of spatial domain approach and one method of frequency domain approach in terms of processing time, accuracy, simplicity in calculations and compatibility with other methods.

Fingerprint, gender classification, spatial domain, frequency domain, ridge parameters, measuring parameters

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A.V. Anjikar, K. Ramteke, S. Chauvan, "Study of Spatial Domain and Frequency Domain Approach for Fingerprint Based Gender Classification," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.74-78, 2020.

Sea clutter in marine surveillance radar makes the task of detecting small targets a very challenging problem In this paper, a better model is been proposed for slow moving smaller target detection. This model initially starts with signal preprocessing to remove the basic noise then a technique called Hanning-Weighted Window Function (HWWF) assisted Time series analysis model with Spatio-Temporal Fourier Transform (STFT) for Time-Frequency analysis is applied, then a space-Time Adaptive Processing (STAP) technique with adaptive weight and filter will be applied to perform small moving target detection under sea clutter, then at last an enhanced Antenna-Pulse-Pair Selection (APS) with Space Spectrum Correlation Coefficient (SSCC) estimation, which has been further processed for the optimal Antenna-Impulse Pair Selection that approximates clutter covariance matrix to achieve enhanced Signal-to-Clutter plus Noise Ratio (SCNR) to achieve computationally efficient STAP for moving target detection This model is proposed for multiple (moving) sea-target detection in sea clutter and jamming probable environment. The overall proposed model will be developed based on impulse radar setup using MATLAB tool Thus, it is well suited for slow moving small target detection under sea clutter for efficient coastal surveillance purposes.

Moving Target Detection, Sea-Clutter Environment, STFT, Hanning Weighted Window Analysis; Hanning-Weighted Overlapped Time-Series Analysis, Impulse Radar, Space Time Adaptive Process, Clutter-Suppression, Antenna, Pulse Pair Selection, Coastal Surveillance

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[2] S. Panagopoulos and J. J. Soraghan, “Small-target detection in sea clutter,” IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 7, pp. 1355–1361, 2004.
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Rajesh B., Udayarani V., Jayaramaiah G.V., "An Advanced Target Detection Model for Slow Moving Smaller Target in the Coastal Area," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.79-83, 2020.

A Vehicular Ad-Hoc Network uses moving cars as nodes to create a mobile network. VANET turns every participating car into a wireless router and allow cars to connect and create a wide range network. VANETs are developed for increasing the driving safety and comfort of automotive end users. It can provide wide variety of services such as Intelligent Transportation System (ITS) e.g. safety applications. Many of safety applications built in VANETs are required real-time communication with high reliability. The main issues are to avoid deterioration of communication channels in dense traffic network. VANET protocols have to face high challenges due to dynamically changing topologies. Many of studies suggested that suitable congestion control algorithms are required to provide efficient operation of a network. However, all congestion control algorithms are not applicable to event-driven safety messages. This paper discusses various existing congestion control algorithms in different congested scenarios. The effectiveness of congestion control algorithm is evaluated through the simulations using Network Simulator-2 (NS2).

Ad-Hoc Network, VANET, Congestion Control, Routing Protocol, AODV, Network Simulator-2

[1] Jing Zuo et.al, “Performance Evaluation of Routing Protocol in VANET with Vehicle-node Density”, IEEE Sixth International Conference on Wireless Communications Networking and Mobile Computing (WiCOM), September 2010, pp 1-4.
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[3] Shwetha. A and Sankar. P, “Queue Management Scheme to Control Congestion in A Vehicular Based Sensor Network”, IEEE,June 2018.
[4] Bhushan Vidhale and S.S. Dorle, “Performance Analysis of Routing Protocols in Realistic Environment for Vehicular Ad Hoc Networks”, IEEE Twenty first International Conference on Systems Engineering, August 2011, pp 267-272.
[5] Jordan T. Willis, Arunita Jaekel and Ikjot Saini, “Decentralized Congestion Control Algorithm for Vehicular Networks Using Oscillating Transmission Power”, IEEE, April 2017.
[6] Hesham El-Sayed, Gokulnath Thandavarayan, Sharmi Sankar and Ishtiaque Mahmood, “An Infrastructure Based Congestion Detection and Avoidance Scheme for VANETs”, IEEE, December 2017.
[7] Forough Goudarzi And Hamid Asgari, “Non-Cooperative Beacon Rate and Awareness Control for VANETs”, IEEE, August 2017.
[8] N. Balon, and J. Guo, “Increasing Broadcast Reliability in Vehicular Ad Hoc Networks”, Third ACM International Workshop on Vehicular Ad Hoc Networks VANET, pp. 104-105, 2006.
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[12]Mustapha Younes Taleb, Salah Merniz and Saad Harous, “Congestion Control Techniques in VANETs: A Survey” IEEE, 20 July 2017.
[13]Swati Sharma, Manisha Chahal and Sandeep Harit, “Transmission Rate-based Congestion Control in Vehicular Ad Hoc Networks”, IEEE, February 2019.
[14]Ashish Patil, Deeksha M., N. Shekar V. Shet and Muralidhar Kulkarni, “Transmit Data Rate Control based Decentralized Congestion Control Mechanism for VANETs”, IEEE, March 2019.
[15]P Sailaja, Banoth Ravi and Jaisingh T, “Performance analysis of AODV and EDAODV routing protocol under congestion control in VANETs”, IEEE, September 2018
[16]Zhanxu Cao, Kai Shi, Qingfeng Song and Jinsong Wang, “Analysis of Correlation Between Vehicle Density and Network Congestion in VANETs”, IEEE, July 2017.
[17]Mohammed Erritali and Bouabid El Ouahidi, “Performance evaluation of ad hoc routing protocols in VANET”, Third international journal on advanced computer science and application, pp 33-40, July 2013

Priyanka Mangal, Amit Kumar Sariya, "A Survey on Congestion Control Techniques for Ad Hoc Routing Protocol in VANET," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.84-89, 2020.

QR (Quick Response) Codes and the way they will be wont to attack each human interaction and automatic systems. Because the encoded data is meant to be code solely, some individuals cannot distinguish between a legitimate and a maliciously manipulated QR code. Whereas humans may fall for phishing attacks, automatic readers square measure possibly susceptible to SQL injections and command injections. Our contribution consists of Associate in Nursing Associate in Nursing lysis of the QR Code as an attack vector, showing totally different attack methods from the attackers purpose of read and exploring their potential consequences.

Security, QR Code, DES Algorithm, Protocols

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K. Ravikumar, R. Geetha, "Security of Quick Response Code," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.90-92, 2020.

Social networking sites like twitter have millions of people share their thoughts day by day as tweets. As tweet is characteristic short and basic way of expression.There are a number of social networking sites and interrelated mobile applications, and some more are still rising. An enormousquantity of data is generated by these sites daily and this data can be used as a source for differentexamination purposes. People interrelate with each other; share their ideas, opinions, interests and personal information. These user tweet are used for finding the sentiments and also add financial, commercial and social values. though, due to the enormous quantity of user-generated information, analyzing the information manually is an expensive method. Increasing sentiment analysis activity, challenges are being added every day. Automated analytical methods are needed to extract views transmitted in user remarks. Opinion mining is the computational analysis of views transmitted in natural language for decision-making purposes. Preprocessing data play a vital role in getting accurate sentiment analysis results. Extracting opinion target words provide fine-grained analysis on the customer twwets. The labeled data required for training a classifier is expensive and hence to overcome, This paper shows opinion mining analysis types and techniques used to perform extraction of opinions from tweets. A Comparative study on the different techniques and approaches of opinion mining twitter data are dealt with in this survey paper.

Sentiment Analysis, Opinion Mining, Social Media, Twitter Data

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B. Nagajothi, R. Jemima Priyadarsini, "A Survey on Twitter Dataset Using Sentiment Analysis," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.93-97, 2020.

Wireless sensor Network is a self-organizing Network. WSN can be deployed in various environments for monitoring purpose. WSN has major applications on health monitoring, military surveillance, weather monitoring, agriculture, etc. In recent decade wireless sensor network implemented in agriculture field are encouraged in many researches. WSN in precision agriculture help to measure different environmental factors such as humidity, temperature, soil moisture, PH value of soil,etc. This precision agriculture helps to enhance the crop quality.Utilizing wireless sensor technologies and management tools can help to identify various diseases in crops, excess usage of pesticides, etc. This paper proposes the efficient use of sensor network in precision agriculture to help the farmers. This paper presents agricultural field where the sensors are used and the different types of sensors used in precision agriculture, also it suggests the environmental factors influencing agriculture.

Green House Management System (GHMS), Arduino Uno,WMSN, PA,GIS

[1] Ravi Kishore Kodali, NisheethRawat and Lakshmi Boppana,, ” WSN Sensors for Precision Agriculture”, 2014 IEEE Region 10 Symposium, India, pp. 651-655, 2014.
[2] Rajinder Kumar, Nagaraj V Dharwadkar,” A Wireless Sensor Network Based Low Cost and Energy Efficient Frame Work for Precision Agriculture”,2017 International Conference on Nascent Technologies in the Engineering Field (ICNTE-2017), India, ISBN No: 978-1-5090-2795-8, 2017.
[3] Mohamed Rawidean Mohd Kassim, Ibrahim Mat, Ahmad NizarHarun,” Wireless Sensor Network in Precision Agriculture Application”, 2014 IEEE, Malaysia, pp.1-5. 2014.
[4] K.Indumathi, R. Hemalatha,S.Aasha Nandhini and S. Radha,” Intelligent Plant Disease Detection System Using Wireless Multimedia Sensor Networks”, 2017 IEEE, pp.1607-1611, 2017.
[5] Ibrahim Mat, Mohamed Rawidean, AhamadNizarHarun, Ismail Mat Yusoff, “IOT in Precision Agriculture Applications Using Wireless Moisture Sensor Networks”, IEEE Conference on Open Systems (ICOS), Malaysia, pp.24-29, 2016.
[6] Rahim Khan, IhsanAli, Muhammad Zakarya , Mushtaq Ahmad ,”Technology-Assisted Decision Support System for Efficient Water Utilization: A Real-Time Testbed for Irrigation Using Wireless Sensor Networks”, IEEE Access, pp.25686-25697, 2018.
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P. Jasmine Lizy, N. Chenthalir Indra, "Wireless Sensor Network in Precision Agriculture," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.98-101, 2020.

Project management involves with effective plans and systematically organization of work. It includes defining the project objectives, making schedules and assigning tasks, in order to accomplish a successful goal. There are many tools that make project management more effective and efficient. The development of software for an improved business process, the construction of a building or bridge, the relief effort after a natural disaster, the expansion of sales into a new geographic market — all are projects. Project management, is the application of knowledge, skills, tools, and techniques to project activities to meet the project requirements.

Project Management, WBS, HOQ

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Bindia Tarika, "Review on Project Management Tools," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.102-106, 2020.

The series of experiments documented in this paper was aimed at determining, analyzing and comparing different solar panel cleaning systems. Output in all experiments was measured in terms of Potential Difference, Current and Power. Readings were taken under three circumstances: (i) when the solar panel was clean, (ii) when a measured amount of material was deposited on it, and (iii) after cleaning action was performed. It was observed and concluded through performed experiments that (i) Of materials used (talcum, dust, sawdust, and leaves), dust was most difficult to remove and led to a maximum drop in the overall output of solar panel after cleaning action was performed, (ii)Dust particles are charged in nature and stick to solar panel screens, rendering current mechanical cleaning methods inefficient, and (iii) Electrodynamic sheet comprising of Indium Tin Oxide improved efficiency to up to 98% whereas other methods witnessed a power drop to a maximum of 25% of original power output, making it better than mechanical cleaning methods by a significant margin.

Photovoltaic Cells, Dust Removal Efficiency, Mechanical Cleaning of Solar Panels, Electrodynamic Sheet, Indium Tin Oxide

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[2] Calle CI, McFall JL, Buhler CR, et al. “Dust particle removal by electrostatic and dielectrophoretic forces with applications to NASA exploration missions” Proceedings of the ESA annual meeting on electrostatics, Paper O1; 2008.
[3] Jones, TB., Electromechanics of Particles, Cambridge University Press, Cambridge, 1995

Sancia Sehdev, Manya Gureja, Mandeep Sukhija, "Improving Efficiency of Dust Removal from Surfaces to Maximize Output of Photovoltaic Cells," International Journal of Computer Sciences and Engineering, Vol.8, Issue.1, pp.107-114, 2020.