iosr-JCE  International Conference on Advanced Information Technology and Applications 2K23

Abstract: Vehicular ad hoc network (VANET) is a subclass of MANETs are vulnerable to various kinds of threats due to their dynamic nature and lack of central point of control. Vehicles (nodes) in VANETs share real-time information about their movements, traffic and road conditions. Existing cooperative IDS are vulnerable that share misleading and manipulated information and disrupts the IDS normal condition. Hence, in this paper proposed an intelligent collaborative model based on data mining for intrusion traffic detection system that can detect the attacks.......

Keywords - VANET, Anomaly detection, Data mining, IDS, Collaborative IDS

[1]. Pathan, A.S.K. (Ed.)Security of Self-Organizing Networks:MANET, WSN, WMN, VANET; CRC Press:BocaRaton,FL,USA,2016.
[2]. Muhammad Imran, Farrukh Aslam Khan, Haider Abbas,Mohsin Iftikhar. 2019. Detection and prevention of blackhole attacks in mobile ad hoc networks, in: Proceedings ofSecurity in Ad Hoc Networks (SecAN) Workshop, 13thInternationalConferenceonAd-HocandWirelessNetworks,AdHocNow2019,and Benidorm, Spain.
[3]. Zhang, H.; Dai, S.; Li, Y.; Zhang, W. Real-time Distributed-Random-Forest-Based Network Intrusion Detection System Using Apache Spark.In Proceedings of the2 018 IEEE 37th International Performance Computing and Communications Conference(IPCCC),Orlando,FL,USA,17–19November2018;pp.1–7.
[4]. Kumar,N.;Chilamkurti,N.Collaborative trust aware intelligent intrusion detection in VANETs.Comput.Electr.Eng.2014,40,1981–1996.
[5]. Sharma, S.; Kaul, A. A survey on Intrusion Detection Systems and Honeypot based proactive securitymechanisms in VANETs and VANETCloud.Veh.Commun.2018,12, 138–164.

Abstract: Speech recognition identifies the capability of software or hardware to receive a voice signal, Manipulates the speaker's features in the speech signal, and recognize the speaker thereafter. In general, the process of speech recognition involves three main criteria: acoustic processing, feature extraction, and classification/recognition. The main aim of feature extraction is to illustrate a speech signal using a predetermined number of systems needs a high computation speed. Processing speed plays a vital role in speech recognition in real-time systems. It requires the use of current technologies and wild algorithms that stimulate the acceleration in extracting the feature parameters from speech signals. The experimental results show that the proposed method successfully extracts the signal features. It also achieves unified classification presentation compared to other conventional speech recognition algorithms.
Keywords: Speech Recognition, Neural Networks, Deep Learning, Machine Learning, Speech-to-text.

[1]. Pahini A. Trivedi, "Introduction to Various Algorithms of Speech Recognition: Hidden Markov Model, Dynamic Time Warping and Artificial Neural Networks," International Journal of Engineering Development and Research, Volume 2, Issue 4, 2014. [2]. M. S. Hossain and G. Muhammad, "Emotion recognition using deep learning approach from audiovisual emotional big data," Inf. Fusion, vol. 49, pp. 6978, Sep. 2019. [3]. M. Chen, P. Zhou, and G. Fortino, "Emotion communication system," IEEE Access, vol. 5, pp. 326337, 2016. [4]. Ondruska P., J. Dequaire, D. Z. Wang and Posner, End-to-end tracking and semantic segmentation using recwrent neural networks. Master Thesis, Cornell University, Ithaca, New York, USA, 2016. [5]. N. D. Lane and P. Georgiev, "Can deep learning revolutionize mobile sensing?" in Proc. ACM 16th Int. Workshop Mobile Comput. Syst. Appl., 2015, pp. 117122.

Abstract: Data science is the study of interdisciplinary field which extracts hidden knowledge and meaningful insights from many structured and unstructured data, using various scientific methods, machine-learning algorithms, data mining techniques and big data. The healthcare is an industry which generates huge amount of data based on Patient demography, Doctors profile, Planning of Treatments, Results obtained from medical examinations, insurance, etc. Such data snatch the attention of data scientists. The field of data science provides technical support to manage, process and to analyze the huge of amount of data generated by healthcare. The healthcare data needs effective analysis to obtain the accurate results......

Keywords- Big data, Data mining, Data analytics, Healthcare, Healthcare informatics.

[1]. Sengupta PP (2013) Intelligent platforms for disease assessment: novel approaches in functional echocardiography. JACC: Cardiovascular Imaging 6(11):1206–1211.
[2]. Muni Kumar N, Manjula R (2014) Role of big data analytics in rural health care-a step towards svasth bharath. Int J Comp Sci Inform Technol 5(6):7172–7178
[3]. Ren Y, Werner R, Pazzi N, Boukerche A (2010) Monitoring patients via a secure and mobile healthcare system. IEEE Wirel Commun 17(1):59–65
[4]. IBM Corporation (2013) Data‐driven healthcare organizations use big data analytics for big gains.
[5]. Burghard C (2012) Big data and analytics key to accountable care success. IDC health insights :1–9

Abstract: Man or woman search has come to be a prime field due to its need in network and inside the area of research among researchers. These challenge objectives to find a probe character from whole scene which indicates super importance in video surveillance field to long lost humans, re-identity, and verification of person. In remaining few years, deep studying has played unremarkable function for the solution of re-identification trouble. Deep studying indicates high-quality performance in person (Re-ID) and search. Researchers experience more flexibility in presenting new methods and solve challenging issues such as low decision, pose variant, history clutter, occlusion, viewpoints, and low illumination......

Keywords: Person Re-Identification, Privacy, Person search, Metric learning.

[1]. Z. Liang, H. Zhang, S. Sun, M. Chandraker, Y. Yang, Q. Tian. Person re-identification in the wild, Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (2017), pp. 1367–1376.
[2]. L. Zheng, Y. Yang, A. G. Hauptmann. Person re-identification: Past, present and future, arXiv Preprint, 2016 arXiv:1610.02984.
[3]. S. Zhai, S. Liu, X.Wang, J. Tang, FMT: fusingmulti-task convolutional neural network for person search, Multimed. Tools Appl. (2019), pp. 1–12.
[4]. T. Xiao, S. Li, B. Wang, L. Lin, Xi Wang. Joint detection and identification feature learning for person search, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2017), pp. 3415–3424.
[5]. X. Chu, W. Ouyang, H. Li, X. Wang, Structured feature learning for pose estimation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 4715–4723.

Abstract: Applications have evolved for provisioning smart technology, smart services, smart industry, smart management, and smart life. However, for holistic operation in the whole network, it requires seamless communication from the sensor/actuator to the gateway. A dynamic channel allocation and organization approach is proposed in to improve the performance of relay-based large networks. A MAC protocol proposed by Kumar et al. uses a static channel allocation mechanism for the distributed relay nodes. A dynamic channel allocation and organization approach is proposed in to improve the performance of relay-based large networks. The DAC scheme divides beacon into different slots of equal duration...............

Keywords: IoT, RAP, MCS, DAC and STA

[1]. Want, R., Schilit, B.N., Jenson, S.: Enabling the Internet of Things. Computer 48(1) (2015) 28–35.
[2]. Lin, T., Rivano, H., Le Mou¨el, F.: How to choose the relevant MAC protocol for wireless smart parking urban networks? In: 11th ACM symposium on Performance evaluation of wireless Adhoc, Sensor, & Ubiquitous Networks, ACM (2014) 1–8.
[3]. Winter, J.M., Muller, I., Soatti, G., Savazzi, S., Nicoli, M., Becker, L.B., Netto, J.C., Pereira, C.E.: Wireless Coexistence and Spectrum Sensing in Industrial Internet of Things: An Experimental Study. International Journal of Distributed Sensor Networks 11(11) (2015) 1–12
[4]. Bellalta, B., Bononi, L., Bruno, R., Kassler, A.: Next generation IEEE 802.11Wireless Local Area Networks: Current status, future directions and open challenges. Computer Communications 75 (2016) 1–25.
[5]. Ahmed, N., Rahman, H., Hussain, M.I.: A comparison of 802.11ah and 802.15.4 for IoT. ICT Express 2(3) (2016) 100–102.

Abstract: The transmission dynamics mathematical model of infectious disease is an essential disease controlling technique, which is being used on the occurrence of HBV to value the varying immunization strategies. In this paper, we analyze the transmission dynamics models through mathematical modeling using Homotopy perturbation method (HPM) which defines how to control the impact of HBV. To get the solution for nonlinear ordinary differential equations,Homotopy Perturbation Method (HPM) has been used. Here, we have discussed the numerical simulations up to six order approximation and error analysis with the help of Matlab software. SIDBRA model has been considered as the best modal to control the viral infections. Thus, examining the dynamics of Hepatitis B viral infection is mainly focused and also shows how given antibiotic (vaccination) control the disease.

Keywords: Mathematical modeling, HPM, HBV, Dynamics, Transmission.

[1]. Peifeng Liang,JianZu, and GuihuaZhuang.: A literature rerview of mathematical models of hepatitis B virus transmission applied to immunization strategies. J Epidemiol.28(5), 221-229 (2018)
[2]. Lavanchy D.: Hepatitis B virus epidemiology, disease burden, treatment, and emerging prevention and control measures. J Viral Hepat. 11, 97–107 (2004)
[3]. Weinbaun CM, MAST EE, Ward JW.: Recommendations for identification and public health management of person with chronic hepatitis B virus infection. Hepatology. 49(5 Suppl), S35 – S44 (2009)
[4]. Ciupe, S., Ribeiro, R., Perelson, A.: Antibody responses during hepatitis B viral infection. PLoS Comp. Biol. 10(7), 1-16 (2014)
[5]. Shepard CW, Simard EP, Finelli L, Fiore AE, Bell BP.: Hepatitis B virus infection: epidemiology and vaccination. Epidemiol Rev. 28, 112-125 (2006)

Abstract: Consumer behaviour can be defined as those acts of individuals (consumers) directly involved in obtaining, using, and disposing of economic goods and services, including the decision processes that precede and determine these acts. Understanding how consumers make purchase decisions can help in several ways, especially in policy decisions. Consumer behaviour, in its broadest sense, is concerned with understanding both how purchase decisions are made and how products or services are consumed or experienced. Consumers are active decision-makers. They decide what to purchase, often based on their disposable income or budget.

[1]. 'Definepedia.In'. Definepedia - Knowledge Is for Sharing, https://www.Definepedia.in/. Accessed 27 Feb. 2023.
[2]. 'Definepedia.In'. Definepedia - What is Consumer Behaviour?, https://www.Definepedia.in/. Accessed 27 Feb. 2022.
[3]. Polymeros Chrysochou, (2017, March). Consumer Perception of Product Risks and Benefits•, 409-428.
[4]. Yurievna, Shvets Irina. 'Economic Changes and Their Impact on Consumer Behaviour: An Empirical Study in the Recent Economic Scenario'. ECS Transactions, vol. 107, no. 1, Apr. 2022, pp. 18165–74. DOI.org (Crossref), https://doi.org/10.1149/10701.18165ecst.
[5]. Roma, P., & Hursh, S. R. (2016). Hypothetical purchase task questionnaires for behavioral economic

Abstract: Underwater Wireless Sensor Network is focused on research in a variety of sectors. Environmental monitoring, underwater oil and gas extraction, military surveillance, smart farming, communications, and other applications are just a few of the key applications that Underwater Wireless Sensor Networks are used for. There are difficulties in replacing nodes in underwater wireless sensor networks, as well as limitations on the network lifetime, poor video processing, high energy consumption, and so on. High energy consumption while preserving security is the emphasis of this research. IoT is being used by developing depth-based routing algorithms. IoT depth base routing is used to save energy and improve security. It assesses energy usage, alive node counts, sink utilisation, and end-to-end latency in this section. The data security, network longevity, and accessibility are all improved by the work processes.

Keywords: sink utilization, energy consumption, depth-based routing protocol.

[1]. N. Usman, O. Alfandi, S. Usman et al., ―An energy efficient routing approach for IoT enabled underwater wsns in smart cities,‖ Sensors, vol. 20, no. 15, 2020.
[2]. A. Khan, I. Ali, A. Ghani et al., ―Routing protocols for underwater wireless sensor networks: taxonomy, research challenges, routing strategies and future directions,‖ Sensors, vol. 18, no. 5, 2018.
[3]. S. V. Kochergin and V. V. Fomin, ―Variational identification of the inderwater pollution source power,‖ in Processes in GeoMedia-Volume II, Springer, 2021.
[4]. P. N. Mahalle, P. A. Shelar, G. R. Shinde, and N. Dey, ―Introduction to underwater wireless sensor networks,‖ in The Underwater World for Digital Data Transmission, Springer, 2021.
[5]. G. Sahu and S. S. Pawar, ―IOT-based underwater wireless communication,‖ in Innovations in Computer Science and Engineering, Springer, 2021.

Abstract: The Internet of Things (IoT) describes the network of physical objects things"—that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the internet. The challenges in IoT are to manage, maintain and deal with a lot of heterogeneous devices, security for the data generated by them, interoperability of different data formats, various protocols used for data communication, architecture that will include all the heterogeneous devices. To build a better IoT ecosystem, the open IoT platform has become a popular term in recent years.......

Keywords – IoT, Open source, OpenIoT, architecture, IoT platforms, IoT tools

[1]. https://www.opensourceforu.com/2019/03/openiot-enabling-the-convergence-of-iot-and-cloud-computing/
[2]. Mineraud, J.; Mazhelis, O.; Su, X.; Tarkoma, S. A gap analysis of Internet-of-Things platforms. Comput. Commun. 2016, 89, 5–16. [CrossRef]
[3]. https://www.oracle.com/in/internet-of-things/what-is-iot/
[4]. Derhamy, H.; Eliasson, J.; Delsing, J.; Priller, P. A survey of commercial frameworks for the internet of things. In Proceedings of the 2015 IEEE 20th Conference on Emerging Technologies & Factory Automation (ETFA), Luxembourg, Luxembourg, 8–11 September 2015; pp. 1–8.
[5]. 14. Hammi, B.; Khatoun, R.; Zeadally, S.; Fayad, A.; Khoukhi, L. IoT technologies for smart cities. IET Netw. 2017, 7, 1–13. [CrossRef]

Abstract: A huge repository of terabytes of data is generated each day from modern information systems and digital technologies such as Internet of Things and cloud computing. studyof these massive data requires a lot of efforts at multiple levelsto extract information for decision making. Therefore, big data analysis is a current area of explore and progress. The basic objective of this paper is to explore the possible impact of big data challenges, open examine issues, and various tools associatedwith it. As a result, this article provides a platform to explorebig data at numerous stages. Additionally, it opens a new horizonfor researchers to develop the solution, based on the challenges and open examine issues.

Keywords:- Big data analytics, Massive data, Structured data,Unstructured Data.

[1]. M. K.Kakhani, S. Kakhani and S. R.Biradar, Research issues in bigdata analytics, International Journal of Application or Innovation in Engineering & Management, 2(8) (2015), pp.228-232.
[2]. A. Gandomi and M. Haider, Beyond the hype: Big data concepts, meth- ods, and analytics, International Journal of Information Management, 35(2) (2015), pp.137-144.
[3]. C. Lynch, Big data: How do your data grow?, Nature, 455 (2008), pp.28-29.
[4]. X. Jin, B. W.Wah, X. Cheng and Y. Wang, Significance and challenges of big data research, Big Data Research, 2(2) (2015), pp.59-64.
[5]. R. Kitchin, Big Data, new epistemologies and paradigm shifts,Big Data Society, 1(1) (2014), pp.1-12.

Abstract: Weather Forecasting plays a primary role of our daily routine lives. Weather Forecasting plays a dominant role of AgricultureSector, Tourism Sector and Government Body Agencies. Prior knowledge of weather can be very useful for human to prepare themselves for any undesirableclimatic conditions. Various Weather parameters like temperature, pressure, humidity, wind speed etc, playsimportant role in the analysis of weather condition. Big Data Analytics is the process used to Analyzing the Data Properly Systematically to generate an Accuracy results. Now a day's several parts of society are interested in Big Data Analytics will give accuracy in results........

Keywords: Big Data Analytics, Weather Forecasting, MapReduce and Hadoop.

[1]. 1.Miss. Shraddha V. Shingne, Prof. Anil D.Warbhe and Prof. Shyam Dubey, "Weather Forecasting using Adaptive technique in Data Mining", International Journal on Recent and Innovation Trends in Computing and Communication (IJRITCC), ISSN: 2321-8169, PP: 091 - 095
[2]. Veershetty Dagade, Mahesh Lagali,Supriya Avadhani and Priya Kalekar, "Big Data Weather Analytics Using Hadoop",IJETCSE, ISSN: 0976-1353 Volume-14 Issue-02, April 2015
[3]. Basvanth Reddy and Prof B. A. Patil, "Weather Prediction on Big Data Using Hadoop Map Reduce Technique", IJARCCE, ISSN: 2278-1021 Volume-05, Issue-06, Page No (643-647), June, 2016.
[4]. Zaharia, Matei, et al. "Spark: cluster computing with working sets." HotCloud 10 (2010): 10-10.
[5]. Riyaz P.A., Surekha M.V., "Leveraging Map Reduce With Hadoop for Weather Data Analytics" IOSR Journal of Computer Engineering, Volume 17, Issue 03, May-June 2015

Abstract: The beginning of WWW and the booming of ecommerce applications and social networks all over the world produces a enormous amount of data. The data transmitted through web faces the issue of its security. In this digital era, the issues of network security are considered as the most important issue of the society. Cyber attacks increase with the number of internet users. The need of techniques to secure the information in networks and to protect the computer were discussed in this paper. This paper gives the overview of cryptography in network security.

Keywords: Security, encryption, decryption, Symmetric encryption, Asymmetric encryption

[1]. Zhijie Liu XiaoyaoXie, Member , IEEE ,School of Mathematics and Computer Science and Zhen Wang, Key Laboratory of Information Computing Science of Guizhou Province , Guizhou Normal University Guiyang , China, The Research of Network Security Technologies.
[2]. The Research of Firewall Technology in Computer Network Security, 2009 Second Asia-Pacific Conference on Computational Intelligence and Industrial Applications by Xin Vue, Wei Chen, Yantao Wang, College of Computer and Information Engineering Heilongjiang Institute of Science and Technology Harbin, China.
[3]. Martin, K.M, Everyday Cryptography Fundamental Principles and Applications, Oxford University Press,2012.
[4]. Paar, C.andPelzl, J, Understanding Cryptography, Springer,2010.
[5]. William Stallings, "Cryptography and Network Security: Principles and Practice", Pearson Education 2013,6th Edition.

Abstract: This paper aims to identify and evaluate three classification methods based on accuracy and kappa statistics, and to visualize them with different levels of rainfall data collected from the India Meteorological Department. The purpose of this research is to determine which classifier is the most effective. The accuracy of various classifiers for the southern states of India is compared and the sensitivity, specificity, accuracy, true positive rate, and false positive rate of each classifier for all states are calculated. Furthermore, a comparison of kappa statistics is conducted by using a confusion matrix.......

Keywords: Naïve Bayes Classifier, k-Nearest Neighbor Algorithm, Support Vector Machine, Confusion Matrix, Precision and Kappa Statistics.

[1]. Chaovalitwongse W and Pardalos P M 2008 Cybernetics and Systems Analysis 44 125–138.
[2]. Orsegino C and Vercellis C 2010 Pattern Recognition 43 3787–3794.
[3]. Sain H and Purnami S Wulan 2015 In Procedia Computer Science 72 59-66.
[4]. B. EmilcyHern´andez, et. al. ," Rainfall Prediction: A Deep Learning Approach", this work is partially supported by the MINECO/FEDERTIN2012-36586-C03-01 of the Spanish Government,2012.
[5]. Ezekiel T. Ogidan, KamilDimililer, YoneyKirsal Ever," Machine Learning for Expert Systems in Data Analysis", DOI 10.1109/ISMSIT.2018.8567251,Oct 2019.

Abstract: Heart related diseases or Cardiovascular Diseases (CVDs) are the main reason for a huge number of death in the world over the last few decades and has emerged as the most life-threatening disease, not only in India but in the whole world. So, there is a need of reliable, accurate and feasible system to diagnose such diseases in time for proper treatment. Machine Learning algorithms and techniques have been applied to various medical datasets to automate the analysis of large and complex data. Many researchers, in recent times, have been using several machine learning techniques to help the health care industry and the professionals.......

Keywords: Cardiovascular Diseases; Support Vector Machines; K- Nearest Neighbour; Naïve Bayes; Decision Tree; Random Forest; Ensemble Models.

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[3] Dhomse Kanchan B and Mahale Kishor M. et al. "Study of Machine Learning Algorithms for Special Disease Prediction using Principal of Component Analysis", 2016 International Conference on Global Trends in Signal Processing, Information Computing and Communication.
[4] R.Kavitha and E.Kannan et al. "An Efficient Framework for Heart Disease Classification using Feature Extraction and Feature Selection Technique in Data Mining ", 2016
[5] Shan Xu ,Tiangang Zhu, Zhen Zang, Daoxian Wang, Junfeng Hu and Xiaohui Duan et al. "Cardiovascular Risk Prediction Method Based on CFS Subset Evaluation and Random Forest Classification Framework", 2017 IEEE 2nd International Conference on Big Data Analysis.