Human-like care is difficult to replicate. Due to the lack of a reliable vision-based fall detection AI, it is often more effective to assign a lot of manpower towards vision-based detections that have not been efficiently implemented.

Ms Inturi Anita Rani, Research Scholar in the Department of Computer Science Engineering, working with her supervisor, Dr V. M. Manikandan, has worked on a paper titled, “A Novel Vision-Based Fall Detection Scheme using Keypoints of Human-Skeleton with Long Short-term Memory Network” in the Arabian Journal for Science and Engineering published by Springer with an Impact Factor of 2.33.

Abstract of the research:

Humans are skilled at visually recognizing and classifying actions in videos, but it’s tough to automate this process. Human action detection in videos is useful in applications like automated surveillance, assisted living, human-computer interaction, content-based video retrieval, and video summarization. The ability to recognize atomic actions like “walking,” “bending,” and “falling” is critical for activity analysis when monitoring elderly people’s daily activities. Our paper presents a new promising solution for fall detection using vision-based approaches. In this approach, we analyse the human joint points which are the prime motion indicators. A set of keypoints of the subject are acquired by applying the AlphaPose pre-trained network. These keypoints are inferred to be the joint points of the subject. The acquired keypoints are processed through a framework of convolutional neural network (CNN) layers. Here, the spatial correlation of the keypoints is analysed. The long-term dependencies are then preserved with the help of long short-term memory (LSTM) architecture. Our system detects five types of falls and six types of daily living activities. We used the UP-FALL detection dataset for validating our fall detection system and achieved commendable results when compared to the state-of-the-art approaches. For comparison, we employed the OpenPose network for keypoint detection. It is inferred from the results that the AlphaPose network is more precise in keypoint detection.

About the research paper:

In this paper, the author proposes a vision-based system that is capable of detecting various types of falls accurately through video processing with the help of a machine learning approach.

Implementation of the research:

The proposed scheme can be used to monitor the activity of elderly people and if any unusual falls happen, the information can be shared with caretakers to ensure emergency services.

The Department of Computer Science Engineering is proud to announce that Dr Sriramulu Bojjagani has published a paper titled “Blockchain-Based Security Framework for Sharing Digital Images using Reversible Data Hiding and Encryption” in the journal Multimedia Tools and Applications (MTAP) having an impact factor of 2.757.

The paper is published in collaboration with D.R Denslin Brabin from the Department of Computer Science and Engineering, DMI College of Engineering, Tamil Nadu and Christo Ananth from the Department of Electronics and Communication Engineering, St. Mother Theresa Engineering College, Tamil Nadu.

Abstract of the Research

Security is an important issue in current and next-generation networks. Blockchain will be an appropriate technology for securely sharing information in next-generation networks. Digital images are the prime medium attacked by cyber attackers. In this paper, a blockchain-based security framework is proposed for sharing digital images in a multi-user environment. The proposed framework uses reversible data hiding and encryption as component techniques. A novel high-capacity reversible data hiding scheme is also proposed to protect digital images. Reversible data hiding in combination with encryption protects the confidentiality, integrity and authentication of digital images. In the proposed technique, the digital image is compressed first to create room for data hiding, then the user signature is embedded; afterwards, the whole image is encrypted. For compression, JPEG lossy compression is used to create high capacity. For encryption, any symmetric block cipher or stream cipher can be used. Experimental results show that the proposed blockchain-based framework provides high security and the proposed reversible data hiding scheme provides high capacity and image quality.

Fig 1: The process of encoding during reversible data hiding

Dr Sriramulu Bojjagani also intends to work on the development of block-chain based solutions to intelligent transport systems and on addressing the challenges of security issues involved in connected and autonomous vehicles.

The Department of Computer Science and Engineering is delighted to announce that Dr Rajiv Senapati and his research group; Abhiram Chakravadhanula, Jaswanth Kolisetty, Karthik Samudrala and Bharat Preetham have published a research article titled “A Novel Decentralized Security Architecture for the Centralized Storage System in Hadoop using Blockchain Technology” in the Scopus indexed IEEE 7th I2CT (The premier conference for the latest discoveries in Convergence in Technology in Asia Pacific).

Over the past decades, the Big Data ecosystem is experiencing a humungous explosion in the generation and exchange of information. Trading such delicate information can serve as a profitable knowledge resource in the present economy. However, serious concerns have been raised about the security and assurance of delicate data as the conventional safety architecture in centralized storage systems does not meet its nuanced requirements. Blockchain technology offers a promising solution for big data protection due to its decentralized nature. Through this research article, Dr Rajiv Senapati and his students intend to introduce a decentralized security architecture in centralized storage systems such as Hadoop to address its existing vulnerabilities.

Abstract of the Research

Big Data is huge in volume, diverse in information, and growing at flourishing rates. The major distributed file systems in the current market in Big Data Analysis include Apache Hadoop, Storm, Cassandra, Flink, Cloudera, and many more. Hadoop is an open-source framework divided into Hadoop Distributed File System (HDFS) and Map-Reduce. Hadoop plays a leading role in storing and processing Big Data in contemporary society as it is cost-effective and can manage large volumes of data in low-cost commodity hardware. HDFS is a type of Data Warehouse which is scalable and has fast access to information.

Metadata is the information about the data, such as which block is storing on what datanode, how many replications that block has, and on which datanodes those replications reside. In HDFS, this metadata is stored at a fixed place in namenode, and attackers can access the metadata and modify it without notice. Also, metadata is mutable, which means the attacker can erase his presence easily.

In this paper, to resolve this issue, we have provided a mechanism using blockchain technology that follows a decentralized architecture against the centralized architecture followed by HDFS. Hyperledger Fabric (HLF) is the blockchain proposed to be effective and trusted for such a purpose. HLF is a private blockchain with a distributed immutable ledger. The metadata will be stored in the ledger. If an attacker tries to modify the data, he cannot erase his presence as the ledger is immutable, unlike HDFS. Further, the work proposed in this paper can be extended in real-time HDFS with a secure ledger and multiple nodes.

Mr Mudigonda Himansh, a 3rd-year BTech-CSE student, has presented a research paper in the IEEE International Conference on Innovations in Information Technology-2022 organised by IIIT Kottayam, Kerala India, in Feb-2022. The paper will be published in IEEE Xplore (Scopus indexed). Dr V M Manikandan, Assistant Professor in Computer Science and Engineering at SRM University-AP, is the co-author of the presented paper titled “A Statistical Study and Analysis to Identify the Importance of Open-source Software”.

Open-source software has picked up pace in the past decade with support from multinational conglomerates and huge open-source communities. We hear a lot about the success of many open-source projects, but we fail to understand how many do not make it. In this paper, Mr Himansh and Dr Manikandan understand the dynamics behind open-source software. They start with the need for open-source alternatives. Then look at a few concerns open-source software developers and maintainers face. Next, we would understand the various requirements of open-source software. Later, we would touch upon the different attributes that affect the selection of open-source software and the decisions to make while building general-purpose, open-source software. Then we would analyse the 5-determinants of open-source software success. Finally, we would look at the data collected from 482 data points from 24 countries and then analyse the data by forming graphs and charts.

This research provides the required knowledge to the open-source and non-open source software developers before-during-after development and production of the software or project. “Our future work is focused on the applications and predicting the scalability of large-scale OSS with machine learning,” says Dr Manikandan.