Volume-7 , Issue-10 , Oct - 2019 Go Back

In this paper we present a new model of low noise amplifier and propose solutions to improve performance. Both single-head and two-head amplifiers are designed and simulated with common mode feedback circuit.

noise, amplifier, power, CMOS Technology

[1] Hitoshi I, Tomoyuki O, Masanori T, et al. "An auto-gain control trans-impedance amplifier with low noise and wide input dynamic range for 10-Gb/s optical communication systems", IEEE J Solid-State Circuits, Vol. 36(5), pp. 1303, 2001.
[2] Han Peng, Wang Zhigong, Sun Ling, et al. "155Mb/s automatic gain control CMOS trans-impedance preamplifier for optical communication", ACTA Electronica Sinica, Vol. 35(11), pp. 2189, 2007.
[3] Yu Changliang, Mao Luhong, Xiao Xingdong, "Standard CMOS Implementation of a novel, fully differential optoelectronic integrated receiver", Chinese Journal of Optoelectronics Laser, Vol. 20(4), pp. 432, 2009.
[4] Huang Beiju, Zhang Xu, Chen Hongda. "1-Gb/s zero-pole cancellation CMOS trans-impedance amplifier for Gigabit Ethernet applications", Journal of Semiconductors, Vol. 30(10), 1, 2009.
[5] Chen W Z, Cheng Y L, Lin D S. "A 1.8-V 10-Gb/s fully integrated CMOS optical receiver analog front-end", IEEE J Solid-State Circuits, Vol. 40(6), pp. 1388, 2005.
[6] Jin J D, Hsu S H. "A 75-dB 10-Gbps trans-impedance amplifier in 0.18-_m CMOS technology", IEEE Photonics Technol. Lett, Vol. 20(24), 2177, 2008.
[7] Chen W Z, Huang S H. "A 2.5 Gbps CMOS fully integrated optical receiver with lateral PIN
detector", Proc IEEE Custom Integrated Circuits Conference, 293, 2007.
[8] B. Razavi, "Design of integrated circuits for optical communications", Wiley series in lasers and applications, 2nd edition, 2003.
[9] M. Rakideh, M. Seifouri, P. Amiri, “A folded cascode-based broadband transimpedance amplifier for optical communication”, Microelectronics Journals, Vol. 54, pp. 1–8, 2016.
[10] D. Chen, S. Yeh, X. Shi, M.A. Do, C.C. Boon, W.M. Lim, “Cross-coupled current conveyor based CMOS transimpedance amplifier for broadband data transmission”, IEEE Trans. Very Large Scale Integer. (VLSI) System, Vol. 21, pp. 1516–1525, 2013.
[11] M.H. Taghavi, L. Belostotski, J.W. Haslett, P. Ahmadi, “10-Gb/s 0.13-μm CMOS inductor less modified-RGC transimpedance amplifier”, IEEE Transactions on Circuits and Systems, Vol. 62, pp. 1971–1980, 2015.
[12] P. Andre, S. Jacobus, “Design of a high gain and power efficient optical receiver front-end in 0.13μm RF CMOS technology for 10Gbps applications”, Microw. Opt. Technol. Lett., Vol. 58, pp. 1499–1504, 2016.
[13] K. Honda, H. Katsurai, M. Nada, “A 56-Gb/s transimpedance amplifier in 0.13-μm SiGe BiCMOS for an optical receiver with −18.8dBm input sensitivity”, in: Proceeding of the IEEE Compound Semiconductor Integrated Circuit Symposium (CSICS), 2016.
[14] M. Seifouri, P. Amiri, I. Dadras, “A transimpedance Amplifier for optical communication network based on active voltage-current feedback”, Microelectronics Journal, Vol. 67, pp. 25-31, 2017.
[15] Y. Chen, J. Li, Z. Zhang, H. Wang, Y. Zhang, “12-Channel, 480 Gbit/s optical receiver analogue front-end in 0.13μm BiCMOS technology”, Electron. Lett., Vol. 53, pp. 492-494, 2017.
[16] Y. Akbey and O. Palatmutcuogullari, “A Broadband Differential Transimpedance Amplifier in 0.35μm SI Ge BICMOS Technology for 10Gb/S Fiber Optical Front Ends”, Analog integrated Circuits and Signal Processing, Vol. 74, Issue. 1, pp.155-162, 2013.
[17] R.Y. Chen, Z.Y. Yang, “CMOS transimpedance amplifier for gigabit-per-second optical wireless communications”, IEEE Trans. Circuits Syst. II, Vol. 63, pp. 418–422, 2016.

Majid. Haghi, Mohammad. Emadi, "Low Noise Amplifier with Low Power Consumption in 0.18 Micrometer CMOS Technology," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.1-8, 2019.

Cloud computing is a rapid rising technology and of a great degree acceptable computing prototype round the globe resulting from its merits on prompt deployment, monetary value (on staging up and environment), big storage capacity, likewise worry free privilege to system anytime, anywhere. This work is aimed at defining various attack patterns that affect the accessibility, confidentiality and integrity of resources and services in cloud computing environment. In addition, the research ushers in a network based intrusion prevention system (NIPS) to discover and stop suspecting actions by monitoring configuration of the system, logs files, network traffic changes, and activities of end-users in the cloud computing network using predefined signatures (rules). This rules classified IP address of users to white list for real user and blacklist for attacker. Results shows that block IP addresses found in blacklist were redirected to attackers (intruders) log, detailing the IP addresses, username, date/time and action. The system security is strong; users whose IP addresses, username and password were found in white list could use the system.

Cloud Computing, Hybrid Cloud, Network Security, Honey Pot, Network Based, Intrusion, Prevention, Detection

[1]. G. Robert. "Privacy in the clouds: risks to privacy and confidentiality from cloud computing." In Proceedings of the World privacy forum, 2012.
[2]. Badger, L., Tim G., Robert P., and Jeff V., "Cloud computing synopsis and recommendations." National Institute of Standards and Technology (NIST), special publication 800 pp.146 2012.
[3]. Stolfo, S. J., S. M. Bellovin, S. Hershkop, A. D. Keromytis, S. Sinclair, S. W. Smith, eds. Insider attack and cyber security: beyond the hacker., Springer Science & Business Media, Vol.39, 2008.
[4]. S. Richard, S. Bahargam, A. Bestavros. "Software-defined ids for securing embedded mobile devices." In 2013 IEEE High Performance Extreme Computing Conference (HPEC), pp. 1-7, 2013.
[5]. N. H. Anh, D. Choi. "Application of data mining to network intrusion detection: classifier selection model." In Asia-Pacific Network Operations and Management Symposium, Springer, Berlin, Heidelberg, pp.399-408., 2008.
[6]. L. Wei., "A genetic algorithm approach to network intrusion detection." SANS Institute, USA Vol.15, pp.209-216, 2004.
[7]. G. R. Hui, M. Zulkernine, P. Abolmaesumi. "A software implementation of a genetic algorithm-based approach to network intrusion detection." In Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/ Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network, IEEE, pp.246-253., 2005.
[8]. Xiang, C., and S. M. Lim. "Design of multiple-level hybrid classifier for intrusion detection system." In 2005 IEEE Workshop on Machine Learning for Signal Processing, pp.117-122., 2005.
[9]. Shacham H. and Waters B. (2008). “Compact proofs of irretrievability,” in Proceedings of Asiacrypt’08 of LNCS, vol.5350, pp.90–107.
[10]. Scarfone, Karen A., and Peter M. Mell. Guide to Intrusion Detection and Prevention Systems (IDPS)| National Institute of Standards and Technology (NIST). No. Special Publication (NIST SP), pp.800-94. 2007.
[11]. Reda M. (2013). "A hybrid network intrusion detection framework based on random forests and weighted k-means." Ain Shams Engineering Journal 4.4, 753-762.
[12]. H. Jin, M. Dong, K. Ota, Minyu F., G. Wang. "NetSecCC: A scalable and fault-tolerant architecture for cloud computing security." Peer-to-Peer Networking and Applications, Vol.9, Issue.1 pp.67-81, 2016.
[13]. Alharkan, T., P. Martin. "Idsaas: Intrusion detection system as a service in public clouds." In Proceedings of the 2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012), IEEE Computer Society, pp.686-687. 2012.
[14]. Mohamed, K. Kifayat, Qi S., W. Hurst. "A system for intrusion prediction in cloud computing." In Proceedings of the International Conference on Internet of things and Cloud Computing, ACM, pp.35, 2016.
[15]. R. Sumant, Mariki M. E., E. Smith. "The management of security in cloud computing." In 2010 Information Security for South Africa, IEEE, pp.1-7, 2010.
[16]. K. Eero, K. Lukka, Arto S., "The constructive approach in management accounting research.", Journal of management accounting research, Vol.5, Issue.1 pp.243-264, 1993.
[17]. L. Liisa, J. Junnonen, S. Kärnä, L. Pekuri. "The constructive research approach: problem solving for complex projects." Designs, Methods and Practices for Research of Project Management. Gower, 2016.
[18]. G. D. Crnkovic, "Constructive research and info-computational knowledge generation." In Model-Based Reasoning in Science and Technology, Springer, Berlin, Heidelberg, pp.359-380, 2010.

P.J. Ebiriene, N.D. Nwiabu, "An Improved Hybrid Cloud Computing Security Architecture Using Network Based Intrusion Prevention System," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.9-14, 2019.

ATM is an essential part of every human life as it enables the customer of a bank to perform financial transactions at any time and without the direct interaction with the bank employees. But, with the increase in the use of ATM, the machines are being prone to hacker attacks, fraud, robberies and security breaches. False keypad, hidden cameras are being used to get the account information and PIN of a user which lead to card cloning or skimming. In this paper, the authors introduce a fast and robust method for the advancement of ATM security which applies a combination of face recognition and fingerprint recognition system as biometric customer authentication and thereby obsoletes the use of PIN. In this method, after the user inserts the card in the card reader, his/her face undergoes the detection and matching process using PCA face detection and recognition algorithm with Eigenfaces method. If a match occurs, the user’s fingerprint is scanned and matched using Minutiae based matching algorithm. If the result comes positive, the user is allowed to process the usual transaction. The experimental results carried out with a laptop webcam and a commercial fingerprint scanner, illustrates the robustness and efficiency of the approach even in low light environments. The challenges and limitations faced during the research and some suggestions for future research directions are also discussed in this paper.

ATM Security, face recognition, fingerprint recognition, biometric, PCA, Minutiae

[1] Viola, P.; Jones, M.,” Rapid Object Detection Using a Boosted Cascade of Simple Features”, TR2004-043 May 2004.
[2] M. Turk and A. Pentland, “Eigenfaces for Recognition,” J. Cognitive Neuroscience, vol. 3, no. 1, 1991.
[3] Shashi kumar D.R, R.K.Chhotaray, Raja K B & Pattanaik Sabyasachi, “Fingerprint Verification based on fusion of Minutiae and Ridges using Strength factors”, International Journal of Computer Applications, July ,2010.

Jufishan Boksha, Romita Mondal, Soumi Mitra, Asoke Nath, "A Proposed Model of Advanced Security System in ATM: Implementation of Face Recognition and Finger Print Recognition," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.15-20, 2019.

An autoencoder is an unsupervised machine learning algorithm [12] that applies back propagation, setting the target values to be equal to the inputs. Deep autoencoders are used to reduce the size of our inputs into a minor representation. If anyone needs the original data, they can reconstruct it from the compressed data.The input seen by the autoencoder is not the raw input but a stochastically corrupted version. A denoising autoencoder is thus trained to reconstruct the original document from the noisy version.In the implementation of Deep autoencoders we have trained the algorithm with noisy and cleaned document images; we generated a model which helps us in removing noise or unnecessary interruption from the documents. Document denoising can be achieved with the deep learning model which automatically learns the discriminative features necessary for classification of input images.

document denoising,deep autoencoder,supervised learning, deep learning ,classification,cleaned and noisy images

[1]. Xie, J., Xu, L., Chen, E.: Image denoising and in painting with deep neural networks. In: NIPS. (2012)
[2]. J. Portilla, V. Strela, M.J. Wainwright, and E.P. Simoncelli. Image denoising using scale mixtures of Gaussians in the wavelet domain. Image Processing, IEEE Transactions on, 12(11):13381351, 2003.
[3]. F. Luisier, T. Blu, and M. Unser. A new SURE approach to image denoising: Interscale orthonormal wavelet thresholding. IEEE Transactions on Image Processing, 16(3):593606, 2007.
[4]. K. Matsumoto et al.,”Learning classifier system with deep autoencoder,” 2016 IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, 2016,pp. 4739- 4746.
[5] A. Krizhevsky, I. Sutskever and G. Hinton,”ImageNet classification with deep convolutional neural networks”, Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.
[6] Semeion Research Center of Sciences of Communication, via Sersale 117, 00128 Rome, Italy Tattile Via Gaetano Donizetti, 1-3-5, 25030 Mairano (Brescia), Italy.
[7] L. Deng, ”The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web],” in IEEE Signal Processing Magazine, vol. 29, no.6, pp.141-142, Nov.2012.
[8] J. Schmidhuber, ”Deep learning in neural networks: An overview”, Neural Networks, vol. 61, pp. 85-117, 2015.
[9] “All About Autoencoders”, Pythonmachinelearning.pro, 2018.
[10] “Image recovery Theory and application”, Automatica, vol. 24, no. 5, pp. 726-727, 1988.
[11] “Building Autoencoders in Keras”, Blog.keras.io, 2018.
[12] M. Celebi and K. Aydin, Unsupervised learning algorithms.
[13] A. Krizhevsky, I. Sutskever and G. Hinton, ”ImageNet classification with deep convolutional neural networks”, Communications of the ACM, vol. 60, no. 6, pp. 84-90, 2017.
[14] V. Nair and G. E. Hinton. Rectified linear units improve restricted Boltzmann machines. In ICML, 2010
[15] ”PyTorch”, Pytorch.org, 2018.
[16] K. He, X. Zhang, S. Ren and J. Sun, ”Deep Residual Learning for Image Recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016, pp. 770-778. DOI: 10.1109/CVPR.2016.90
[17] T. D. Gedeon and D. Harris, ”Progressive image compression,” [Proceedings 1992] IJCNN International Joint Conference on Neural Networks, Baltimore, MD,1992, pp. 403-407 vol.4.
[18] L. Bottou. Large-scale machine learning with stochastic gradient descent. COMPSTAT, 2010.
[19] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. ICLR, 2015.
[20] A. V. Lugt, ”Signal detection by complex spatial filtering,” in IEEE Transactions on Information Theory, vol. 10, no. 2, pp. 139-145, Apr 1964.
[21] E. Kaur and N. Singh, ”Image Denoising Techniques: A Review”, Rroij.com, 2018.

Mohammad Imran, T. Sita Mahalakshmi, M.D. Venkata Prasad, V. Kumar Kopparty, "Denoising Dirty Document using Autoencoder," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.21-26, 2019.

Cyber Security introduces a group of methods, used to shield networks, data and programs from intrusion, deterioration and illegal access. Cyber intrusion is the act of breaking the security of one’s computer with the means of a network. To cut down the threat of various illegal accessing in order to enhance the cyber security, Machine Learning approach is used widely. Machine learning in itself is the study of various ways to train the machine with real datasets and make them act like humans in similar circumstances. In this paper, most of the Machine Learning and Deep Learning algorithms that are used for enhancing cyber security have been summed up.

Machine Learning, Deep Learning, Cyber security, Intrusion

[1] M. Esmalifalak, Nam Tuan Nguyen, Rong Zheng, Han. Zhu, “Detecting stealthy false data injection using machine learning in smart grid”, 2013 IEEE Global Communications Conference (GLOBECOM), pp.1-9, 2013.
[2] M. Chora, R. Kozik, “Machine learning techniques applied to detect cyber attacks on web applications”, Logic Journal of IGPL, Vol. 23, Issue.1, pp. 45–56, 2015.
[3] C. Amrutkar, Y. S. Kim, P. Traynor. “Detecting Mobile Malicious Webpages in Real Time”, IEEE Transactions on Mobile Computing, Vol. 16, Issue.8, 2017.
[4] J. Siryani, B. Tanju, T. J. Eveleigh. “A Machine Learning Decision-Support System Improves the Internet of Things’ Smart Meter Operations. IEEE Internet of Things Journal, Vol. 4, Issue.4, 2017.
[5] Nir Nissim, Aviad Cohen, and Yuval Elovici, “ALDOCX: Detection of Unknown Malicious Microsoft Office Documents Using Designated Active Learning Methods Based on New Structural Feature Extraction Methodology”. IEEE Transactions on Information Forensics and Security, Vol. 12, Issue.3, 2017.
[6] K. Fukuda, J. Heidemann, A. Qadeer, “Detecting Malicious Activity With DNS Backscatter Over Time”, IEEE/ACM Transactions on Networking, Vol. 25, Issue.5, 2017.
[7] S. Deng, A. H. Zhou, D. Yue, B. Hu, L. P. Zhu, “Distributed intrusion detection based on hybrid gene expression programming and cloud computing in a cyber physical power system”, IET Control Theory & Applications, Vol. 11, Issue.11, 2017.
[8] G. Loukas, T. Vuong, R. Heartfield, G. Sakellari, Y. Yoon, D. Gan, “Cloud-Based Cyber-Physical Intrusion Detection for Vehicles Using Deep Learning”,IEEE Access, Vol. 6, 2018.
[9] C. C. San, M. M. S. Thwin, N. L. Htun, “Malicious Software Family Classification using Machine Learning Multi-class Classifiers”, Computational Science and Technology, pp. 423–433, 2018.
[10] J. B., Fraley, J. Cannady, “The promise of machine learning in cybersecurity”, SoutheastCon 2017, 2017.
[11] F. Bu, “A High-Order Clustering Algorithm Based on Dropout Deep Learning for Heterogeneous Data in Cyber-Physical-Social Systems”, IEEE Access, Vol. 6, 2018.
[12] S. Ahmed, Y. Lee, S. H. Hyun, I. Koo, “Feature Selection–Based Detection of Covert Cyber Deception Assaults in Smart Grid Communications Networks Using Machine Learning”. IEEE Access, Vol. 6, 2018.
[13] S. Naseer, Y. Saleem, S. Khalid, M. K. Bashir, J. Han, M. M. Iqbal, K. Han, “Enhanced Network Anomaly Detection Based on Deep Neural Networks”, IEEE Access, Vol. 14, Issue.8, pp. 1–15, 2018.
[14] N. R. Sabar, X. Yi, A. Song, “A Bi-objective Hyper-Heuristic Support Vector Machines for Big Data Cyber-Security”,. IEEE Access, Vol. 6, 2018.
[15] T. Alves, R. Das, T. Morris, “Embedding Encryption and Machine Learning Intrusion Prevention Systems on Programmable Logic Controllers” IEEE Embedded Systems Letters, Vol. 1, 2018.
[16] L. Fernandez Maimo, A. L. Perales Gomez, F. J. Garcia Clemente, M. Gil Perez, G. Martinez Perez, “A Self-Adaptive Deep Learning-Based System for Anomaly Detection in 5G Networks”, IEEE Access, Vol. 6, 2018.
[17] M. Yousefi-Azar, L. Hamey, V. Varadharajan, S. Chen, “Malytics: A Malware Detection Scheme”, IEEE Access, Vol. 4, pp. 1–14, 2018.
[18] P. Chattopadhyay, L. Wang, Y. P. Tan, “Scenario-Based Insider Threat Detection From Cyber Activities”, IEEE Transactions on Computational Social Systems, pp. 1–16, 2018.
[19] M. N. Napiah, M. Y. I. Bin Idris, R. Ramli, I. Ahmedy, “Compression Header Analyzer Intrusion Detection System (CHA - IDS) for 6LoWPAN Communication Protoco”. IEEE Access, Vol. 6, 2018.
[20] J. Li, Y. Ye, Y. Zhou, J. Ma, “CodeTracker: A Lightweight Approach to Track and Protect Authorization Codes in SMS Messages”, IEEE Access, Vol. 6, 2018.
[21] A. Abeshu, N. Chilamkurti, “Deep Learning: The Frontier for Distributed Attack Detection in Fog-to-Things Computing”, IEEE Communications Magazine, Vol. 56, Issue.2, pp. 169–175, 2018.
[22] J. Liu, C. Zhang, Y. Fang, “EPIC: A Differential Privacy Framework to Defend Smart Homes Against Internet Traffic Analysis”, IEEE Internet of Things Journal, Vol. 5, Issue.2, 2018.
[23] M. D. Smith, M. E.Pate-Cornell, “Cyber Risk Analysis for a Smart Grid: How Smart is Smart Enough? A Multiarmed Bandit Approach to Cyber Security Investment”, IEEE Transactions on Engineering Management, Vol. 65, Issue.3, 2018.
[24] D. Mallampati, “An Efficient Spam Filtering using Supervised Machine Learning Techniques”, International Journal of Scientific Research in Computer Science and Engineering, Vol. 6, Issue.2, pp.33-37, 2018.
[25] B. Wahyudi, K. Ramli, H. Murfi, “Implementation and Analysis of Combined Machine Learning Method for Intrusion Detection System”, International Journal of Communication Networks and Information Security, Vol. 10, No. 2, 2018.

Sagar Bansal, Anshika Singh, "Machine learning in the prediction, determination and further study of different cyber-attacks," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.27-36, 2019.

Automatic detection of linguistic negation in free text is a demanding need for many text processing applications including sentiment analysis. Our system uses online news archives from two different resources namely NDTV and The Hindu to predict the scope of negation in the text. In this paper, our main focus was on identifying the scope of negation in news articles for two political parties namely YSR Congress Party (YSRCP) and Alliance (which includes Jana Sena Party, Communist Party of India , Bahujan Samaj Party , Telugu Desam Party (TDP)) by using two existing namely Fixed Window Length (FWL), Dependency Analysis (DA) and one proposed methodology is Negation Sentiment Analyzer (NSA). The average F measures for each one of them were 0.61, 0.66 and 0.72 respectively. It was observed that NSA outperforms the other two. We further evaluated the results of NSA against the standard BioScope negation corpus as a benchmark, achieving 0.75 as a F1 scores

Negation Identification, Sentiment Analysis, Natural Language Processing, Artificial Intelligence

[1] Insaac G. Councill, Ryan McDonald, 2010, What‟s Great and What‟s Not: Learning to Classify the Scope of Negation for Improved Sentiment Analysis, Negation and Speculation in Natural Language Processing (NeSp-NLP 2010), Proceedings of the Workshop, UppNSAla, Sweden, 10 July 2010.
[2] Kevin Lerman, Ari Gilder, Mark Dredze, Reading the Markets: Forecasting Public Opinion of Political Candidates by News Analysis, 2008.
[3] Veronika Vincze, György Szarvas, Richárd Farkas, György Móra, and János Csirik, The BioScope corpus: annotation for negation, uncertainty and their scope in biomedical texts, BMC Bioinformatics, 2008.
[4] Talmy Giv´on, English Grammer: A Function-Based Introduction. Benjamins, Amsterdam, NL, 1993.
[5] Gunnel Tottie. Negation in English Speech and Writing: A Study in Variation Academic, San Diego, CA, 1991.
[6] Theresa Wilson, Janyce Wiebe, and Paul Hoffmann, Recognizing contextual polarity in phrase level sentiment analysis. Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing Pages 347-354, 2005.
[7] Tetsuji Nakagawa, Kentaro Inui, and Sadao Kurohashi. Dependency Tree-based Sentiment Classification using CRFs with Hidden Variables. Proceedingsof The 11th Annual conference of the North American Chapter of the Association for Computational Linguistics ACL, Los Angeles, CA, 2010.
[8] Cristian Danescu-Niculescu-Mizil, Lillian Lee, And Richard Ducott, Without a “doubt‟? Unsupervised discovery of downward-entailing operators. Proceedings of The 10th Annual Conference of the North American Chapter of the Association for Computational Linguistics. ACL, Boulder, CO, 2008.
[9] Theresa Wilson, Janyce Wiebe, and Paul Hoffmann, Recognizing contextual polarity in phraselevel sentiment analysis.Proceedings of the Conference on Human Language Technology and Empirical Methods in Natural Language Processing Vancouver, Canada, 2005.
[10] Edward S. Klima. Negation in English. Readings in the Philosophy of Language. Ed. J. A. Fodor and J. J. Katz. Prentice Hall, B. Englewood Cliffs, NJ: 246-323, 1964.
[11] Miller, G. A., WordNet: a lexical database for English. Communications of the ACM, Vol. 38, 11, pp. 39-41, 1995.
[12] Penn_Treebank, The Penn Treebank Project [WWW]. Available from: http://www.cis.upenn.edu/~treebank/ [Accessed April 21, 2012], 1992,
[13] Heerschop, B., Hogenboom, A. and Frasincar, F., 2011, Sentiment Lexicon Creation from Lexical Resources. In: 14th International Conference on Business Information Systems (BIS 2011). Springer, 185-196.

S. Padmaja, Sasidhar Bandu, Deepa Ganu, S. Sameen Fatima, "Analyzing Sentiment and Determining Negation Scope in Political News," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.37-42, 2019.

Emotion Recognition is a recent research area which can be applied in various applications. Speech Emotion is useful in E-learning, medical and in entertainment. The proposed work focuses on emotion recognition using Fast Fourier Transform and Marathi speech database. In this work six emotions are considered and Fast Fourier Transform is used for feature extraction and as a major feature for finding specific emotion. The Marathi words which represents the emotion like surprise and sad like Are Bapre (अरे बापरे ! ), Kiti Wilakshan (किती विलक्षण ! ), Are Deva (अरे देवा !) etc are used as a speech samples for analysis purpose. This paper highlights the overview of existing speech database as well as the newly developed Marathi emotional speech database. In this proposed work total 52 Marathi utterances are used in the experimental purpose. Fourier Transform is used to convert time domain signal into Frequency domain signal. The proposed experimental work gives 100% recognition rate for surprise and disgust emotion. Similarly 90% accuracy for sad emotion and 87.5% accuracy got for fear and angry emotion. The overall average recognition rate for six emotions is 93%. Very less accuracy rate i.e 62.5% got for happy emotion.

Emotion Recognition, SER, AER

[1] Surabhi Vaishnav and Saurabh Mitra, “Speech Emotion Recognition: A Review”, International Journal of Engineering and Technology (IRJET), Vol. 03, Issue:04, Apr-2016.
[2] Leila Kerkeni, Youssef Serrestou, Mohamed Mbarki, Kosai Roof and Mohamed Ali Mahjoub, “Speech Emotion Recognition: Methods and Cases Study”, in Proceedings of the 10th International Conference on Agents and Artificial Intelligence (ICAART 2018) – Vol.2,Pages 175-182.
[3] Dhruvi Desai, “Emotion Recognition using Speech Signal: A Review”, International Research Journal of Engineering and Technology (IRJET), Vol. 05, Issue:04, Apr-2016.
[4] Shweta Sinha, “Analysis and Recognition of Dialects of Hindi Speech”, International Journal of Scientific Research in Computer Science and Engineering (IJSRCSE), Volume-3, Issue-5, Oct 2015.
[5] V.K. Jain and N. Tripathi, “Speech Features Analysis and Biometric Person Identification in Multilingual Environment”, International Journal of Scientific Research in Network Security and Cmmunications-6, Issue-1, February 2018.
[6] Agnihotri P.P., Shinde A.R. and Khanale P.B. “Development of Noise free Marathi Speech database using various Filtering Techniques”, Indian Journal of Research, Volume-5, Issue-2, February 2016.
[7] Oh-Wook Kwon, Kwokleung Chan, Jiucang Hao, Te-Won Lee, “Emotion Recognition by Speech Signals” EUROSPEECH 2003 - INTERSPEECH 2003 8th European Conference on Speech Communication and Technology Geneva, Switzerland, September 1-4, 2003.
[8] S.Lalitha, Sbhishek Madhavan, Bharath Bhushan, Srinivas Saketh,”Speech Emotion Recognition”, International Conference on Advances in Electronics, Computers and Communications(IJAECC), 2014.
[9] Nobuo Sato, Yasunari Obuchi, “Emotion Recognition using Mel-Frequency Cepstral Coefficients”, Journal of Natural Language Processing, pp 83-96, 2007.

Shinde Ashok R., Agnihotri Prashant P., Raut S.D., Khanale Prakash B., "Emotion Recognition in Marathi Language by using Fast Fourier Transform," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.43-47, 2019.

Intel has launched a 32bit microprocessor which was designed for high performance and to drive the most advanced computer-based applications. The 80386 offers the system designer many new and powerful capabilities, including unprecedented performance of 3 to 4 million instructions per second. The addressing modes of 80386 support efficient access to the elements of the standard data structures.

memory; virtual; instruction; microprocessor

[1] Anup A. Pachghare, G.K. Andurkar, Amruta M. Kulkarni,“A REVIEW ON MICROPROCESSOR AND MICROPROCESSOR SPECIFICATION”, International Journal of Science, Engineering and Technology Research (IJSETR) Volume 2, Issue 2, February 2013.
[2] Khaled El-Ayat, Rakesh K. Agarwal,” The Intel 80386- Architecture And Implementation”, Published in IEEE Micro 1985DOI:10.1109/mm.1985.304507.
[3] Ziff Davis,” New Coprocessor for new era”, PC Magazine February /March 1982.
[4] Bryan Ford and Russ Cox Massachusetts Institude of Technology”Lightweight,User-level Sandboxing on the x86”.
[5] Zatar, W. Nasr, G.E.,2002,”An Implementation Scheme for a Microprocessor emulator”,Otago Univ.,Byblos,IEEE.
[6] https://en.wikipedia.org/wiki/Intel_80386
[7] https://whatis.techtarget.com/definition/Intel-80386
[8] https://css.csail.mit.edu/6.858/2014/readings/i386.pdf
[9] Magazine: Games vs. Hardware. The History of PC video games
[10] Tiwari, R. Sam and S. Shaikh, "Analysis and prediction of churn customers for telecommunication industry," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 218-222. doi: 10.1109/I-SMAC.2017.8058343.
[11] S. Navadia, P. Yadav, J. Thomas and S. Shaikh, "Weather prediction: A novel approach for measuring and analyzing weather data," 2017 International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, 2017, pp. 414-417. doi: 10.1109/I-SMAC.2017.8058382.
[12] S. Shaikh, S. Rathi and P. Janrao, "IRuSL: Image Recommendation Using Semantic Link," 2016 8th International Conference on Computational Intelligence and Communication Networks (CICN), Tehri, 2016, pp. 305-308. doi: 10.1109/CICN.2016.66
[13] S. Shaikh, S. Rathi and P. Janrao, "Recommendation System in E-Commerce Websites: A Graph Based Approached," 2017 IEEE 7th International Advance Computing Conference (IACC), Hyderabad, 2017, pp. 931-934. doi: 10.1109/IACC.2017.0189
[14] A. Fasiku, Ayodeji Ireti, B. Olawale, Jimoh Babatunde, C. Abiola Oluwatoyin B., "Comparison of Intel Single-Core and Intel Dual-Core Processor Performance", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.1-9, 2013
[15] M. Sora, J. Talukdhar, S. Majumder, P.H Talukdhar, U.Sharmah, "Word level detection of Galo and Adi language using acoustical cues", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.10-13, 2013
[16] Manish Mishra, Piyush Shukla, Rajeev Pandey, "Assessment on different tools used for Simulation of routing for Low power and lossy Networks(RPL)", International Journal of Scientific Research in Network Security and Communication, Vol.7, Issue.4, pp.26-32, 2019

Hamira Shaikh, Mosina Shaikh, Shahid Shaikh, Aatif Qureshi, Farman Khan, Viren Patel, Shakila Shaikh, Shiburaj Pappu, "Closer Look at the New Processor: 80386," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.48-51, 2019.

No doubt, the notable and bursting growth in data and databases has produced an imperative necessity for new mechanism and devices that can rationally and spontaneously convert the handled data into helpful and valid information and knowledge. Data mining is such a style that evolves non axiomatic, tacit, formerly anonymous, and possibly beneficiary information from data in databases. In this paper we achieved some Enhancements in K_way Method In "APRIORI" Algorithm for Mining the Association Rules Through Embedding SQL Commands.

Ddata mining; association rules; relational, database; Apriori ; SQL

[1] R. Agrawal, T. Imielinski, A. Swami. Mining Association Rules between Sets of Items in Large Databases. In Proc. of the ACM SIGMOD Conference on Management of Data, 1993.
[2] R. Agrawal, R. Strikant. Fast Algorithms for Mining Association Rules. In Proc. of the Very Large Database (VLDB) Conference, 1994.
[3] Mirela Danubianu, Stefan Gheorghe Pentiuc, Iolanda Tobolcea. Mining Association Rules Inside a Relational Database – A Case Study. IARIA, 2011.pp14-20
[4] Cyrille Masson, Céline Robardet, Jean-François Boulicaut: Optimizing subset queries: a step towards SQL-based inductive databases for itemsets.in processing of ACM symposium of applied computing SAC 2004: 535-539
[5] Jamil, H.M. Ad hoc association rule mining as SQL3 queries. Proceedings IEEE International Conference on Data Mining, 2001, 609 – 612.
[6] Gang Fang Zu-Kuan Wei Yu-Lu Liu . An algorithm of improved association rules mining. In proceeding of International Conference on Machine Learning and Cybernetics, 2009, 133 - 137
[7] J. Han, Y. Fu, K. Koperski, W. Wang, and O. Zaiane. DMQL: A data mining query language for relational datbases.In Proc. of the 1996 SIGMOD workshop on research issues on data mining and knowledge discovery, Montreal, Canada, May 1996.
[8] Sunita Sarawagi, Shiby Thomas, Rakesh Agrawal, integrating Association rule mining with relational database systems, Proceedings of the 1998 ACM SIGMOD international conference on Management of data, Volume 27 Issue 2.
[9] D. Mirela, G.Stefan, T. PentiucIolanda. Mining Association Rules Inside a Relational Database – A Case Study. The Sixth International Multi-Conference on Computing in the Global Information Technology(ICCGI 2011). June 19-24, 2011 Luxembourg.14-19.
[10] Rao, V.V., R, “Efficient association rule mining using indexing support,” Proceedings of the International Conference on Recent Trends in Information Technology (ICRTIT), 3-5 June 2011, Chennai, Tamil Nadu. pp. 683 – 688.

Basel A. Dabwan, Mukti E. Jadhav, "Enhanced K_way Method In "APRIORI" Algorithm for Mining the Association Rules Through Embedding SQL Commands," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.52-56, 2019.

In India, people have started using twitter and nowadays, its craze has overshadowed the users all day. In India, a Twitter user across India was predicted to be more than 34 million in 2019. Twitter data is a very huge amount of data that can be used for the prediction of various diseases. Tweets are strongly related to Dengue cases. Dengue is a viral-borne disease that is also one of the widespread waterborne diseases. Nowadays people are trying a lot to avoid being a victim of dengue. But this communicable disease has highly increased alongside the urbanization rate in the tropical rain forest region. In this research paper, we focused on the retrieval of tweets using a hashtag keyword using a free analytic tool Vicinitas. We collected a set of 102 tweets to train a classifier to identify dengue, record and predict the emergence and transmission of dengue in a population. WEKA is a collection-set for machine learning and it is free open-source software. In this research, we used the dengue datasets with a total of one hundred two instances of dengue and two attributes i.e., text and class to determine accuracy using the various classifying algorithms. For the best outcome, we used seven classification techniques for accuracy. The main methodology and the techniques we used for predicting the dengue are J48, Naïve Bayesian, SMO, and Random tree, ZeroR, Random Forest and REP Tree. We after evaluating various attributes of the result finally concluded that Bayes obtained the highest accuracy rate.

Dengue, Weka, and Classification

[1] Andrea villanes, Emily Giffiths, Michael Rappa, Christopher G. Healey, “Dengue fever surveillance in India using text mining in public media”, The American Journal of Tropical Medicine & Hygiene, Vol.98, Issue.1, pp.181-191, 2018.
[2] Wajeeha Farooqi, Sadaf Ali, “A critical study of selected classification algorithms for dengue fever and dengue hemorrhagic fever”, In the Proceedings of the 2013 IEEE 11th International conference on Frontiers of Information Technology (FIT 2013), USA, pp.140-145, 2013.
[3] M. Vidhyalakshmi, P. Radha, “Social HashTag Techniques Using Data Mining-A Survey” International Journal of Scientific Research in Computer Science and Engineering, Vol.6, Issue.3, pp.86-92, 2018
[4] N.Saravanan, Dr. V. Gayathri, “Classification of dengue dataset using J48 algorithm and ant colony based a J48 algorithm”, In the Proceeding of the 2017 International Conference on Inventive Computing and Informatics (ICICI 2017), India, pp.1062-1067, 2017.
[5] Tina R.Patil, S.S.Sherekar, “Performance analysis of naive Bayes and j48 classification for data classification”, International journal of computer science and applications, Vol.6, No.2, pp.256-267, 2013.
[6] Shameem Fathima, Nisar Hundewale, “Comparison of classification techniques-SVM and naive Bayes to predict the arboviral disease-dengue”, In the Proceedings of the 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW), Atlanta, GA, pp.538-539, 2011.
[7] Thypparampil Karunakaran Sajana, MarojuNavya, YVSSV.Gayathri, Nimgire Reshma, “Classification of dengue using machine learning techniques”, International Journal of Engineering and Technology, Vol.7, Issue.2.32, pp.212-218, 2018.
[8] Nandini. V, Sriranjitha. R, Yazhini. T. P, “Dengue detection and prediction system using data mining with frequency analysis”, 6th International Conference on Advances in Computing and Information Technology, pp.53-67, 2016.
[9] Nelofar Rehman, “Data Mining Techniques Method Algorithms and Tools”, International Journal of Computer Science and Mobile Computing, Vol.6, Issue.7, pp.227-231, 2017.
[10] Kashish Ara Shakil, Samiya Khan, Shadma Anis, Mansafalam, “Dengue disease prediction using weka data mining tool”, In the Proceeding of IIRAJ International Conference (ICCI-SEM-2K17), India, pp.48-59, 2015.
[11] Iqra Jahangir, Abdul-Basit, Abdul Hannan, Sameen Javed, “Prediction of dengue disease through data mining by using modified apriori algorithm”, In the Proceeding of ACM 4th International Conference of Computing for Engineering and Sciences (ICCES 2018), Malaysia, pp.1-4, 2018.
[12] R. Sanjudevi, D.Savitha, “Dengue Fever Prediction Using Classification Techniques”, International Research Journal of Engineering and Technology (IRJET 2018), Vol.6, Issue.2, pp.558-563, 2018.
[13] M.Bhavani, S.Vinod Kumar, “A data mining approach for precise diagnosis of dengue fever”, International Journal of Latest Trends in Engineering and Technology, Vol.7, Issue.4, pp. 352-359, 2016.
[14] Kamran Shaukat, Nayyer Masood, SundasMehreen, UlyaAzmeen, “Dengue fever prediction: a data mining problem”, Journal of Data Mining in Genomics and Proteomics, Vol.6, Issue.3, pp.1-5, 2015.
[15] Amit Palve, Rohini D.Sonawane, Amol D. Potgantwar, “Sentiment Analysis of Twitter Streaming Data for Recommendation using Apache Spark” International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.99-103, 2017.

Sarita Kumari, K. Jeberson, W. Jeberson, "Dengue Prediction Using Tweets in India," International Journal of Computer Sciences and Engineering, Vol.7, Issue.10, pp.57-63, 2019.