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In recent years, route prediction and planning services have gained significant popularity, thanks to the abundance of geo-information and the rise of various applications. With the increasing global population and widespread adoption of smartphones and GPS devices, a vast amount of geo-data is being generated. Route prediction plays a crucial role in reducing travel time, effort, and cost. In this project, our main objective is to develop a web-based application that can generate scalable travel itineraries. To achieve this, we propose the Multiple-Destination Route Prediction (MDRP) algorithm, which predicts optimal paths based on geographical data. These geographical data points are then visualized on a map using a map matching tool, providing the user with the final results. Real datasets, publicly available, Utilizing Road network spatial data along with GPS traces collected from users, are used to conduct experiments. Generating multiple valid node sequences of varying lengths in a sequential manner poses a challenge due to the need for multiple passes through the database. However, the experiments conducted on these real datasets have demonstrated that our proposed MDRP algorithm efficiently predicts optimized shortest paths to multiple locations.

Geographic Information Systems (GIS), Route Prediction Systems, Data Mining, GPS, Travel Pattern, Geospatial Database, Spatial Data Analysis, Location-Based Services, Path Prediction, Trajectory Analysis, Context-Aware Computing, Probabilistic Model, Map Matching.

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[28]. Sudhir Kumar Adhlaka, Neelam Duhan, Komal Kumar Bhatia and Himanshu Sharma, “Route Prediction Techniques using GPS Traces and Spatial Data,” Proceedings of INDIACom IEEE International Conference on “Computing for Sustainable Global Development”, BVICAM, New Delhi, 13th-15th, March, pp.1475-1481, 2019.
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Nidhi Soni, Ajay Jangra, "An In-Depth Exploration of Route Prediction Algorithms: A Comprehensive Analysis," International Journal of Computer Sciences and Engineering, Vol.11, Issue.6, pp.1-9, 2023.

Steganography is a technique for obscuring sensitive data in images or other media so that it cannot be accessed by nefarious parties. Image steganography hides secret messages inside pictures so only the person sending the message and the one receiving it can see what it says. Image steganography uses images to hide extra data by including or changing its image bits. We came up with a new way to share information called "Novel LSB Method” (NLSBM). It starts by turning the words into a picture so that it`s safe to share. This means that the way the text looks is changed, but the actual words stay the same. This is done to ensure that the text is safe when it is sent. You can use different types of pictures, like black and white or colourful, to do secure transmission. Our new way of hiding messages is really helpful because you don`t need another picture to hide your message in. Also, it is small and very fast compared to older methods.

Steganography, Secure transmission, NLSBM, Cover selection.

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[14]. Halder, T.; Karforma, S.; Mandal, R. (2019): A block-based adaptive data hiding approach using pixel value difference and LSB substitution to secure e-governance documents. Journal of Information Processing Systems, Vol.15, no.2, pp.261-270, 2019.
[15]. Kumar, M. V.; Mamatha, E.; Reddy, C. S.; Mukesh, V.; Reddy, R. D. (2017): Data hiding with dual based reversible image using sudoku technique. International Conference on Advances in Computing, Communications and Informatics, pp.2166-2172, 2017.
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[17]. M. Singh, R. Sharma, D. Garg, A New Purposed Issue for Secure Image Steganography Technique Based On 2-D Block DCT and DCT, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.2, Issue.7, pp.29-34, 2012.
[18]. A. A. Ali, A. H. S. Saad, New Image Steganography Method by Matching Secret Message with pixels of Cover Image (SMM), International Journal of Computer Science Engineering and Information Technology Research (IJCSEITR), Vol.3, Issue.2, pp.1-10, 2013.
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[24]. R. Yang, Z. Zheng, J. Wei, Cover selection for image steganography based on image characteristics. Journal of Optoelectronics Laser Vol.25, Issue.4, pp.764–768, 2014.
[25]. Z. Wang, X. Zhang, Secure cover selection for steganography. IEEE Access 7, pp.57857–57867, 2019.

J. Chandrashekhara, Vinay S., "A Novel Approach for Secure overlay Image Selection in Steganography," International Journal of Computer Sciences and Engineering, Vol.11, Issue.6, pp.10-14, 2023.

Stock Trading Decisions are important in selection of the right stock at the right time. There are traditional and regular methods for identifying superior stocks for investment but looking into volatility of current market scenario, new technologies must be incorporate to accomplish the target. Here, we presented a review on use of pattern recognition approach and machine learning techniques for Stock Trading Decisions. Usually common patterns are seen in the buying and selling data of stocks for a specific business house. Analysing these data patterns with the use of machine learning approach will produce a better result for Trading Decision. Different machine learning models has been built and applied by different authors to achieve better stock trading decisions.

Pattern, Candlestick, ANN, CNN, Open, Close, High, Low.

[1]. T. Fischer, C. Krauss, “Deep Learning With Long Short-Term Memory Networks for Financial Market Predictions”, European Journal of Operation Research, Vol. 270, No 2, pp.654-669, 2018.
[2]. Ding, Xiao, Yue Zhang, Ting Liu, Junwen Duan, "Deep learning for event-driven stock prediction." In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI 2015)”, pp.2327-2333, 2015.
[3]. Hendrik. Bessembinder, Kalok Chan, "Market Efficiency And the Returns to Technical Analysis", Financial Management, pp.5-17, 1998.
[4]. Ben R. Marshall, Martin R. Young, Lawrence C. Rose. "Candlestick Technical Trading Strategies: Can They Create Value for Investors?." Journal of Banking and Finance, Vol. 30, No 8, pp.2303-2323, 2006.
[5]. T. H. Lu, "The Profitability of Candlestick Charting in the Taiwan Stock Market." Pacific-Basin Finance Journal, Vol. 26, pp. 65-78, 2014.
[6]. T. H. Lu, Yi-Chi Chen, Yu-Chin Hsu. "Trend Definition or Holding Strategy: What Determines The Profitability of Candlestick Charting?.", Journal of Banking & Finance, Vol. 61, pp. 172-183, 2015.
[7]. Min. Zhu, Said Atri, Eyub Yegen, "Are Candlestick Trading Strategies Effective in Certain Stocks With Distinct Features?”, Pacific-Basin Finance Journal, Vol.37, pp. 116-127, 2016.
[8]. Shi Chen, Si Bao, Yu Zhou, "The Predictive Power of Japanese Candlestick Charting In Chinese Stock Market", Physica A: Statistical Mechanics and its Applications, Vol. 457, pp. 148-165, 2016.
[9]. J. Fock, Henning, Christian Klein, Bernhard Zwergel. "Performance of Candlestick Analysis on Intraday Futures Data", The Journal of Derivatives, Vol. 13, No 1, pp. 28-40, 2005.
[10]. Matthieu Duvinage, Paolo Mazza, Mikael Petitjean, "The Intra-Day Performance of Market Timing Strategies And Trading Systems Based on Japanese Candlesticks." Quantitative Finance, Vol. 13, No 7, pp. 1059-1070, 2013.
[11]. Marc`Aurelio Ranzato, Y-Lan Boureau, Yann Cun. "Sparse Feature Learning for Deep Belief Networks", Advances In Neural Information Processing Systems, Vol. 20, 2007.
[12]. Yaohu Lin, ,Shancun Liu, Haijun Yang, Harris Wu, Bingbing Jiang, "Improving Stock Trading Decisions Based on Pattern Recognition Using Machine Learning Technology", PloS one, Vol. 16, No. 8, 2021.
[13]. Marc Velay, Fabrice Daniel, "Stock Chart Pattern Recognition With Deep Learning." arXiv preprint arXiv:1808.00418 , 2018.
[14]. D. Sorna Shanthi, T. Aarthi, A.K. Bhuvanesh, RA. Chooriya Prabha,"Pattern Recognition in Stock Market”, International Journal of Computer Science and Mobile Computing, Vol. 9, No 3, pp.106 – 111, 2020.
[15]. Li. Yawei, Liu. Peipei, Wang. Ze, "Stock Trading Strategies Based on Deep Reinforcement Learning", Scientific Programming, Vol. 2022, pp. 1-15, 2022. https://doi.org/10.1155/2022/4698656
[16]. A. HhUpreti, A. Agrawal, J. K. Joshi, S. Seniaray,(2022). “Quantitative Study of Candlestick Pattern & Identifying Candlestick Patterns Using Deep Learning For The Indian Stock Market”, International Journal of Health Sciences, Vol. 6 No S3, pp. 5739–5749, 2022.
[17]. Üzeyir AYCEL, Yunus SANTUR, "A New Algorithmic Trading Approach Based on Ensemble Learning And Candlestick Pattern Recognition in Financial Assets", Turkish Journal of Science and Technology, Vol. 17, No 2, pp. 167-184, 2022.
[18]. Ni Putu Winda Ardiyanti, Irma Palupi, Indwiarti Indwiarti. "Trading Strategy On Market Stock By Analyzing Candlestick Pattern Using Artificial Neural Network (Ann) Method", Journal Media Informatika Budidarma,Vol. 5, No 4, pp. 1273-1282, 2021.
[19]. T, T. Ho, Y. Huang, “Stock Price Movement Prediction Using Sentiment Analysis And Candlestick Chart Representation”, Sensors, Vol. 21, No 23, pp. 7957, 2021.
[20]. U. GOKUL, “Forecast Share Price Using Technical Analysis Tool”, Pacific International Journal, Vol. 4, No 1, pp. 01-06, 2021.
[21]. J. H. Chen, Y. C. Tsai, “Encoding Candlesticks As Images For Pattern Classification Using Convolutional Neural Networks”, Financial Innovation, Vol. 6, No 1, pp. 1-19, 2020.

Sonalika Nayak, Jibendu Kumar Mantri, Prasanta Kumar Swain, "Pattern Recognition and Machine Learning Approach for Stock Trading Decisions: A Review," International Journal of Computer Sciences and Engineering, Vol.11, Issue.6, pp.15-21, 2023.

In today`s age of digitization and automation, the life of humans is increasingly easier because virtually everything has been reprogrammed and replaces old manually generated procedures. Today`s people make use of the Internet to this day in their everyday lives, and that leaves them unable to function without it. The Internet of Things offers a platform that allows devices to connect, detect and remote control objects on the network infrastructure. Lab automation is a major focus of our project. Currently, none of the colleges` and schools` labs are automated. Wireless systems, can be a great asset for automation systems. The presence of students in the lab, the lab`s temperature, and its humidity can be checked automatically. Automated labs means monitoring entry of students and person’s entering in the labs and providing security to labs when there is off period of institute it helps to security when anyone trying to enter in the labs. Software provides platform to students and teacher to meet online at one place. The system extends the home robotization technology to the council laboratories, and hence to produce a smart laboratory.

Face Recognition; Temperature sensor; Thief surveillance, Submission portal, ESP32 webcam.

[1]. Deepak Adhav, Rahul Pagar, Ravi Sonawane, Sachin Tawade, “Smart Laboratory,” International Journal of Trend in Scientific Research and Development (IJTSRD), Vol. 3, Issue 3, pp.504-509, 2019.
[2]. Deborah Knox, Ursula Wolz (joint chairs), Daniel Joyce, Elliot Koffman, Joan Krone, Atika Laribi, J. Paul Myers, Viera K. Proulx, Kenneth A. Reek “Use of laboratories in Computer Science education: guidelines for good practice Report of the Working Group on Computing Laboratories,” Vol.28, Issue.SI, pp.167-181, 1996.
[3]. Suiqun Li1 , Xiang Gao , Wenjie Wang , Xinrui Zhang,, “Design of smart laboratory management system based on cloud computing and internet of things technology,” Journal of Physics: Conference Series. pp.1-7, 2020. DOI:10.1088/1742-6596/1549/2/022107

Shabina Sayyad-Modi, Vaishnavi Rajendra Chavan, Rutuja Sanjay Kadam, Nikita Shankar Mane, Aditya Pramod Jadhav, Ketan Mahesh Doshi, "System for Safe Smart Lab Administration and Control," International Journal of Computer Sciences and Engineering, Vol.11, Issue.6, pp.22-25, 2023.

Around 315 million students are enrolled in universities in India, which has the world`s largest body. There are an enormous number of students who graduate each year with different degrees. In their education, they require various certificates, transcripts, degrees, diplomas, transfer certificates, etc., Due to the high number of graduate students, it can be difficult to establish whether an academic document is legitimate, and without any validating authority, it becomes even more challenging. To address this, we suggest using blockchain technology to create digital transfer certificates that are tamper-proof and will serve as a reliable form of authentication both in academic institutions and workplaces. Also, it will help the institute Tracking of these certificates and their verification manually becomes a hectic job and also the process will be handy for the students. Through the unique QR code of each document, certain authorities can easily verify the documents.

Blockchain, Digital certificate, Certificate Generation and Verification.

[1].J. Rooksby and K. Dimitrov, “Trustless education? A blockchain system for university grades,” New Value Transactions Understanding and Designing for Distributed Autonomous Organizations Workshop at DIS 2017, 2017.
[2].T. Arndt, “Empowering university students with blockchain-based transcripts,” in Proc.CELDA 2018, Budapest, Hungary, October, pp.21-23, 2018
[3].Development and Evaluation of Blockchain based Secure Application for Verification and Validation of Academic Certificates.Elva Leka1,2,* and Besnik Selimi1, Annals of Emerging Technologies in Computing (AETiC) Vol. 5, No. 2, 2021

Shabina Sayyad-Modi, Rashmi Kishor Shingate, Rutuja Girish Jagtap, Mayuri Dattatray Kadam, Rutuja Hemant Sabale, "Smart Transfer Certificate Generator and Employer Verification Using Blockchain," International Journal of Computer Sciences and Engineering, Vol.11, Issue.6, pp.26-29, 2023.

This paper puts forward a novel approach in big data processing and it is aimed at cutting computational time and enhancing classification accuracy. The research focuses on the relevance and significance of hybrid algorithms, specifically combining the Ball Tree and Weighted k Nearest Neighbors (k-NN) algorithms. The objective of this study is to address the limitations of traditional algorithms by reducing computational time while improving classification accuracy. The methodology employed in this research is the constructive research method, which allows for the development and evaluation of new algorithms. This methodology was chosen as it facilitates the creation of innovative approaches to tackle the challenges of big data processing. Experimental results demonstrate that the proposed hybrid algorithm yields promising outcomes. When classifying the MNIST dataset, the algorithm achieved an accuracy rate of 97%, misclassifying only 256 out of 10,000 images. The harmonic mean between precision and recall was found to be 0.999716, indicating a high level of performance. Notably, the computational time required for classification was significantly shorter compared to traditional classification techniques. Overall, the hybrid algorithm combining the Ball Tree and Weighted k-NN proved to be an effective solution for big data processing. By reducing computational time and enhancing accuracy, it presents a valuable contribution to the field. This research opens avenues for further exploration and application of hybrid algorithms in various domains where efficient and accurate big data processing is crucial.

Big Data, Ball Tree Algorithm, Classification, Weighted K-Nearest Neighbours (WKNN), Hybrid Algorithm, K- Nearest Neighbours, MNIST Dataset.

[1]. Kalio, Q.P, Nwiabu, N, “A Framework for Securing Data Warehouse Using Hybrid Approach,” International Journal of Computer Science and Mathematical Theory, Vol.4, No.1, pp.3-4, 2019.
[2]. Prasad, Lakshmi Y.” Big Data Analytics Made Easy”. Vol 1, Punjab : Notion Press, 2016
[3]. Farhad, M., Hamid, N., & Bahman, J., “Energy-Efficient Big Data Analytics”., Advances in Computer., Vol.100, 2015
[4]. Nwiabu, N., Adeyanju, I. “User Centred Design Approach to Situation Awareness,” International Journal of Computer Application, Vol.49. Issue.27, pp 26-30, 2012
[5]. Kramer, Oliver. “Dimensionality Reduction with Unsupervised Nearest Neighbors”. “Intelligent Systems Reference Library” First Edition, Vol 51, Berlin : Springer Berlin, Heidelberg, 2013. ISBN 978-3-642-38652-7..
[6]. Kumar , N., Zhang, L., & Nayar, S. “What Is a Good Nearest Neighbors Algorithm for Finding Similar Patches in Images?” [ed.] David Forsyth, Philip Torr and Andrew Zisserman. Marseille : “In the Proceedings of the European Conference on Computer Vision.” Vol.5303, pp.364-379, 2008, Springer, Berlin, Heidelberg, ISBN: 978-3-540-88688-4.
[7]. Dudani, S.A. “The Distance-Weighted k-Nearest-Neighbor Rule.” IEEE Transactions on Systems, Man, and Cybernetics 4, Vols. SMC-6, pp.325-327. May 21, 1975.
[8]. Kiruthika, R., & Vijayakumar, V. “An Enhanced Fuzzy Based Linkage Clustering Algorithm (EFCA) in High Dimensional Data,” International Journal of Computer Sciences and Engineering, Vol.8, No 2, pp.12-17, February 28, 2020, E-ISSN: 2347-2693.
[9]. Gupta, A., & Pratik, G. “Implementation of K-Means Clustering in Big Data Environment,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.11, pp.38-44, November 10, 2019, E-ISSN: 2347-2693.
[10]. Iqbal, R., Doctor, F., More, B., Mahmud, S., Yousuf, U. “Big Data analytics and Computational Intelligence for Cyber–Physical Systems: Recent trends and state of the art applications,” Future Generation Computer Systems, Vol.105, pp.766-778, 2020. https://doi.org/10.1016/j.future.2017.10.021.
[11]. Rawal, B., & Ruchi, A. “Improving Accuracy of Classification Based on C4.5 Decision Tree Algorithm Using Big Data Analytics.” Advances in Intelligent Systems and Computing (AISC),. Vol.711, No 7, pp.203-210. ISBN 978-981-10-8055-5.
[12]. Jothi, J. M., Arockiam, L “A Framework to enhance the Accuracy of Aspect level Sentiment Analysis in Big Data, ”Conference on Inventive Computing and Informatics,” Vol.17, pp.452-457, 2017, ISBN: 978-1-5386-4031-9.
[13]. Guo-Feng, F., Yan-Hui, G., & Jia-Mei, Z. “Application of the Weighted K-Nearest Neighbor Algortihm for Short-Term Load Forecasting,” Multidisciplinary Digital Publishing Institute, Vol.12, Issue.5, pp. 1-19, 2019.
[14]. Chakravarthy, S. S., Bharanidharan, N., & Rajaguru, H. “Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer,” Innovation and Research in BioMedical Engineering(IRBM), Vol.44, Issue.3 pp.50-62. June 15, 2023 ISSN 1959-0318.
[15]. Naz, G., Muhammad, A., Aldahmani, S., & Khan, Z.A “Weighted k-Nearest Neighbours Ensemble With Added Accuracy and Diversity,” In the Proceedings of the IEEE International Conference on Big Data 2022 , Osaka., pp.3341-3347, 2022.
[16]. Wan, W., & Lee, H. J. “Deep feature representation and ball-tree for face sketch,” International Journal of System Assurance Engineering and Management, Vol.11, pp.818-823. September 3, 2019.
[17]. Giang, N. T., Huong, H. L., Tai, H. P., & Hiep, X. H. “A parallel algorithm for determining the communication radius of an automatic trap based on balltree structure,” In the Proceedings of the Eighth International Conference on Knowledge and Systems Engineering (KSE) Hanoi Vietnam. pp.139-143, 2016.
[18]. Neeraj, K., Li, Z., & Nayar, S. “What is a good Nearest Neighbors Algorithm for Finding Similar Patches in Images.” In the Proceedings of the 10th European Conference on Computer Vision - ECCV 2008, pp.364-378, 2008. 978-3-540-88689-1.
[19]. Nwiabu, N., Alison, I., & Oyeneyin, B. “Modelling Case-Based Reasoning in Situation-Aware Disaster Management,” Communications of the IIMA, Vol.19, Issue.1, pp.1-19, 2021.

Azibator Banigo, Nuka Dumle Nwiabu, Vincent Ike Anireh, "Hybrid Classification Algorithm for Improved Big Data Processing," International Journal of Computer Sciences and Engineering, Vol.11, Issue.6, pp.30-36, 2023.

Healthcare delivery in recent years has gained massive improvement. Information Technology has been used to improve the way medical practitioners administer healthcare which has further impacted on the lives of the general populace. This study present an automated system in health monitoring of the elderly. Human Activities Recognition (HAR), heart rate, body temperature, stress level and blood pressure sensor dataset has been used in implementation and a mobile software was developed to simulate the activity, health monitoring and response of the medical personnel to the elderly. The HAR dataset contains 77 sensors in subject 1 and 84 sensors in subject 2. The Agile software development methodology was used in the process of development. The mobile software is used to get the readings of the various wearable and home stationary sensors from which the medical personnel can receive notification of the health information and activity of the patient. The MATLAB scientific programming language has been used to analyze and demonstrate the HAR dataset with some unique activity spikes shown on the graphical illustrations. A mobile application was developed to simulate the readings gotten from the wearable sensors and the activity triggers in the home. The R programming language was used to train and test the wearable sensor performance of the artificial neural model. The body sensor data has been evaluated and analyzed and the performance accuracies are thus: for Heart rate, the performance accuracy recorded for training is 70.4% with a misclassification of 29.6% and testing performance accuracy was 57.4% with a misclassification of 42.4%, Body temperature recorded 65.7% with misclassification of 34.3% for training and 66.7% with misclassification of 33.3% for testing. The performance for Stress level in the training was 65.7% with misclassification of 34.3 and 64.1% with misclassification of 35.9% for testing performance and the performance of blood pressure was 73.1% with a misclassification of 26.9% for training and in testing, 70.4% with misclassification of 29.6% was recorded. The mobile application has performed well during simulation, presenting the readings of the smart wearable devices from the patient and the activities of the patient at home.

Health Monitoring System, Internet of Things, Patient Monitoring, Human Activity Recognition

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Nwokoma Queen Maclean, Taylor Onate Egerton, "Automated Health Monitoring System for the Elderly using Internet of Things," International Journal of Computer Sciences and Engineering, Vol.11, Issue.6, pp.37-44, 2023.

Let Fq be the finite field with q elements, p, q be two odd primes with gcd(2p, q) = 1, multiplicative order of q modulo 2p^m is p^d (0?d?m-1), m ? 1 be an integer. In this paper, we obtain weight distribution of all the minimal(irreducible) cyclic codes of length 2pm over Fq by using their generating polynomials.

Primitive root, Weight distribution, Minimal Cyclic Codes

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Inderjit Singh, Seema Rani, "Weight Distribution of Minimal Cyclic Codes over a Finite Field," International Journal of Computer Sciences and Engineering, Vol.11, Issue.6, pp.45-47, 2023.