Volume-12 Issue-3, February 2023 – International Journal of Innovative Technology and Exploring Engineering (IJITEE)

D105514040325

IJITEE Journal — Thu, 24 Jul 2025 11:48:04 +0000

The International Journal of Innovative Technology and Exploring Engineering (IJITEE) has ISSN: 2278-3075 (online), an open-access, peer-reviewed, periodical monthly international journal. This journal is published by Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP) in January, February, March, April, May, June, July, August, September, October, November and December.The journal aims to publish high–quality peer–reviewed original articles in the area of Engineering and Technology that covers Computer Science and Engineering, Information Technology, Electrical and Electronics Engineering, Electronics and Telecommunication, Mechanical Engineering, Civil Engineering and all interdisciplinary streams of Engineering and Technology. #Smart Computing and Information Processing #Signal and Speech Processing #Image Processing and Pattern Recognition #WSN#Artificial Intelligence and machine learning #Data mining and warehousing #Data Analytics #Deep learning #Bioinformatics #High Performance computing #Advanced Computer Networking #Cloud Computing #IoT #Parallel Computing on GPU #Human Computer Interactions #Recent Trends in Microelectronics and VLSI Design #Process & Device Technologies #Low-Power Design #Nanometer-Scale Integrated Circuits #Application specific ICs (ASICs) #FPGAs #Nanotechnology #Nano Electronics and Quantum Computing #3. Challenges of Industry and their Solutions, Communications #Advanced Manufacturing Technologies #Artificial Intelligence #Autonomous Robots #Augmented Reality #Big Data Analytics and Business Intelligence #Cyber Physical Systems (CPS) #Digital Clone or Simulation #Industrial Internet of Things (IIoT) #Manufacturing IoT #Plant Cyber Security #Smart Solutions – Wearable Sensors and Smart Glasses #System Integration #Small Batch Manufacturing #Visual Analytics #Virtual Reality #3D Printing # Internet of Things (IoT) #Internet of Things (IoT) & IoE & Edge Computing #Distributed Mobile Applications Utilizing IoT #Security, Privacy and Trust in IoT & IoE #Standards for IoT Applications #Ubiquitous Computing #Block Chain-enabled IoT Device and Data Security and Privacy #Application of WSN in IoT #Cloud Resources Utilization in IoT #Wireless Access Technologies for IoT #Mobile Applications and Services for IoT #Machine/ Deep Learning with IoT & IoE #Smart Sensors and Internet of Things for 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IoT-Based Real-Time Human Motion Recognition Based on Skeletons for Next Generation Healthcare using Deep Learning Techniques
Subrata Kumer Paul¹, Rakhi Rani Paul², Md. Ekramul Hamid³
¹Subrata Kumer Paul, Department of CSE, University of Rajshahi, Rajshahi, Bangladesh, Department of CSE, Bangladesh Army University of Engineering & Technology (BAUET), Natore, Rajshahi, Bangladesh.

²Rakhi Rani Paul, Department of CSE, University of Rajshahi, Rajshahi, Bangladesh. Department of CSE, Bangladesh Army University of Engineering & Technology (BAUET), Natore, Rajshahi, Bangladesh.

³Ekramul Hamid, Department of CSE, University of Rajshahi, Rajshahi, Bangladesh.

Open Access | Editorial and Publishing Policies | Cite | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The Internet of Things (IoT) and mobile technology have transformed healthcare by enabling real-time monitoring and diagnosis of patients. Human Motion Recognition (HMR) plays a vital role in healthcare systems in the 21st century, yet existing solutions face challenges such as high computational demands, low accuracy, and limited adaptability. This paper introduces a novel approach to real-time HMR using skeleton data with ENConvLSTM. This hybrid deep learning model combines EfficientNet and ConvLSTM to improve both accuracy and processing speed. The EfficientNet excels in image classification, while ConvLSTM processes sequential data, making this hybridization well-suited for dynamic motion analysis. Applied to the “NTU RGB+D 120” public dataset, our model achieves an accuracy of 94.85% for cross-subject and 96.45% for cross-view evaluations, addressing key healthcare-related activities across 12 medical classes. Moreover, the dataset presents several challenges, including a large data size, high dimensionality, class imbalance, noisy data, and limited domain labelling, all of which require advanced processing and modelling techniques. Our model’s unique use of skeleton data tracking 25 body joints enables the detection of subtle motion changes, ideal for early diagnosis and intervention in healthcare scenarios. Additionally, we integrate the system with a Raspberry Pi and a GSM module, providing real-time alerts via Twilio’s SMS service to instantly notify caregivers and patients. However, this solution is pivotal in modern healthcare, offering scalable, efficient, and proactive patient monitoring, leading to improved outcomes and reduced healthcare costs.

Keywords: Real-time Human Motion Recognition (HMR), EN Conv LSTM, Skeleton Data, NTU RGB+D 120 Dataset.
Scope of the Article: Computer Science and Applications

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C94520212323

IJITEE Journal — Sat, 11 Feb 2023 11:09:54 +0000

Thwart and Safeguard of Cyber Crime & Cyber Attack in Computer Networks
Kurian M.J¹, Sreekanth D²
¹Dr. Kurian M.J, Department of Computer Applications, Baselios Poulose II Catholicos College, Piravom, (Kerala), India.
²Dr. Sreekanth D, Head of Research and Solutions, ICT Academy of Kerala (Kerala), India.
Manuscript received on 29 January 2023 | Revised Manuscript received on 04 February 2023 | Manuscript Accepted on 15 February 2023 | Manuscript published on 28 February 2023 | PP: 25-32 | Volume-12 Issue-3, February 2023 | Retrieval Number: 100.1/ijitee.C94520212323 | DOI: 10.35940/ijitee.C9452.0212323

Open Access | Editorial and Publishing Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The term “cyber” is closely related to or characteristic of the culture of computers, computer networks, information technology, and virtual reality. The Internet is a global network of billions of computers and other electronic devices that use standardised communication protocols. A total of 5.07 billion people worldwide use the internet today, equivalent to 63.5% of the world’s total population. Internet users continue to grow as well. As of 2022, China had over one billion internet users, more than any other country in the world. India ranked second, with nearly 933 million Indians accessing the internet via any device. Cybercrime is a type of criminal activity that targets or utilises a computer, a computer network, or a networked device. The slogans used to address data privacy are: “Data privacy belongs to you.” Secure it, protect it, and block hackers. This paper highlights the issues related to categories and the impact of cybercrime, as well as fundamental cyber laws, security threats, and protection, serving as a warning to internet users.

Keywords: Adware, Cybercrime, Cyber bulling, Cyber spying, DDOS, DoS, Phishing, Ransomeware, Spyware
Scope of the Article: Computer Network

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B94060112223

IJITEE Journal — Sat, 04 Feb 2023 12:59:45 +0000

Determine the Undervalued US Major League Baseball Players with Machine Learning
Lu Xiong¹, Kecheng Tian², Yuwen Qian³, Wilson Musyoka⁴, Xingyu Chen⁵
¹Lu Xiong, Assistant Professor, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA.
² Kechen Tian, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA.
³ Yuwen Qian, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA.
⁴Wilson Musyoka, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA.
⁵Xingyu Chen, Department of Mathematical Sciences, Middle Tennessee State University, Murfreesboro, USA.
Manuscript received on 27 December 2022 | Revised Manuscript received on 01 February 2023 | Manuscript Accepted on 15 February 2023 | Manuscript published on 28 February 2023 | PP: 17-24 | Volume-12 Issue-3, February 2023 | Retrieval Number: 100.1/ijitee.B94060112223 | DOI: 10.35940/ijitee.B9406.0212323

Abstract: Baseball is a sport of statistics. The industry has accumulated detailed statistical data on both offence and defence for over a century. Experience has shown that data analysis can provide a competitive advantage over teams that do not utilise such analysis. Over the last two decades, the development of machine learning and artificial intelligence has enabled the creation of more advanced algorithms for analysing data in baseball. In the following research, we will run different ML models using sci-kitlearn and H2O on Colab, and the Caret package on RStudio to examine the datasets (hitting dataset and salary dataset) and determine the undervalued players by predicting the number of runs scored in the following year. We will compare machine learning regression algorithms and ensemble methods, providing comprehensive explanations of the results. The suggestion of which model is superior in terms of prediction accuracy will be determined.

Keywords: Sports Analytics, Machine Learning, Ensemble Methods, Deep Learning
Scope of the Article: Machine Learning

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C94460212323

IJITEE Journal — Sat, 04 Feb 2023 12:52:31 +0000

Pertinence of Internet of Things (IoT) in the Paddy Agriculture Sectors in Indian Context
Kurian M.J¹, Juby George²
¹Dr. Kurian M.J, Department of Computer Applications, Baselios Poulose II Catholicos College, Piravom, Piravom, India.
²Dr. Juby George, Department of Computer Applications, Marian College Kuttikanam, Kuttikanam, India.
Manuscript received on 27 January 2022 | Revised Manuscript received on 02 February 2023 | Manuscript Accepted on 15 February 2023 | Manuscript published on 28 February 2023 | PP: 10-16 | Volume-12 Issue-3, February 2023 | Retrieval Number: 100.1/ijitee.C94460212323 | DOI: 10.35940/ijitee.C9446.0212323

Abstract: In the modern era, technology has played an inevitable role. The population of India is almost equivalent to 17.7% of the world population. Based on arable land area, India is the second-largest in the world. Although India is the second-largest producer of rice, the most significant problems faced by farmers in our country are a lack of knowledge about technological development. The number of smartphone users is expected to exceed 800 million by the end of 2022. Under these circumstances, the Internet of Things (IoT) plays a significant role in the agricultural field in terms of productivity by monitoring the daily progress of the field. IoT aims to take internet connectivity to another level by connecting multiple devices simultaneously to the internet, thereby facilitating man-to-machine and machine-to-machine interactions. With the agricultural apps installed on their smartphones, farmers can track the progress of their crops as well as the schemes and subsidies announced by the authorities. This paper highlights the significance of the Internet of Things in the agricultural sector.

Keywords: Cloud, Greenhouse, Internet, IoTs, Kisan Yojana, Precision Farming, RFID, Sensors
Scope of the Article: IoT

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C94180212323

IJITEE Journal — Thu, 12 Jan 2023 12:16:35 +0000

Template-driven Real-Time Data Acquisition Technique with Big Data
Sowmya R¹, Suneetha K R²
¹Sowmya R, Research Scholar, Department of Computer and Engineering, Bangalore Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India.
²Dr. Suneetha K R, Professor, Department of Computer and Engineering, Bangalore Institute of Technology, Bengaluru, Visvesvaraya Technological University, Belagavi (Karnataka), India.
Manuscript received on 03 January 2023 | Revised Manuscript received on 09 January 2023 | Manuscript Accepted on 15 February 2023 | Manuscript published on 28 February 2023 | PP: 1-9 | Volume-12 Issue-3, February 2023 | Retrieval Number: 100.1/ijitee.C94180212323 | DOI: 10.35940/ijitee.G9418.0212323

Abstract: The development of Big Data (BD), which is used to obtain numerous data from various domains, is brought about by technological advancement. However, managing the information and extracting knowledge from it is the most challenging and problematic. Thus, this paper proposed a template-centric new Data Acquisition (DAQ) methodology. The stock market data is gathered from several structured or unstructured data sources. After the DAQ criterion, templates are created for the gathered data. The stock market data is collected based on its Application Programming Interface (API) and transmitted via the transmission protocols during the DAQ process. To effectively remove redundant data, the transmitted data is pre-processed and stored efficiently in the network for further real-time analysis. Finally, the performance of the proposed technique is evaluated. According to the experimental and empirical evaluations, the proposed system outperforms the other methods.

Keywords: Big Data, Pre-processing, Decision Trees, Data reduction, Cleansing, Data Integration, Real-Time.
Scope of the Article: Big Data Analytics

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