Associate Professor and Head, Department of Artificial Intelligence and Data Science, Vidyavardhini's College of Engineering and Technology, Maharashtra, India
Authors - Aiswarya Rajan K K, K Nattar Kannan Abstract - This study presents a systematic literature review on the emergence, adoption, and challenges of AI-driven Human Resource Management (AI-HRM). Thematic synthesis and bibliometric insights were used to analyze eighteen Scopus-indexed studies published between 2019 and 2024 using the PRISMA framework. Using the Technology Acceptance Model (TAM/UTAUT), Socio-Technical Systems (STS) Theory, and Responsible AI principles, the review shows how AI improves HRM by automating repetitive tasks, facilitating data-driven decision-making, and allowing for individualized employee development. However, ethical risks like algorithmic bias, lack of transparency, privacy issues, and employee resistance continue to be major obstacles. The results imply that only when technological capabilities are in line with human judgment, organizational culture, and ethical governance can AI pro-vide long-term value in HRM.
Authors - Padma Lakshmi G, Swetha V, Monik Raj Murugan S, Srinivasa Perumal R, Lakshmi Priya G G Abstract - We have proposed ”Haze to vision: Pipeline for Underwater Image Restoration, Enhancement and Object detection”.The images captured underwater suffer from bluish tint,greenish tint,haze,color distortion. As light travels in water it will undergo scattering, refraction and absorption, the higher the wavelength will be observed first, and the lower wavelength will be absorbed later. This phenomenon affects the bluish/greenish color in the captured images . To study underwater species, underwater environments, we need good quality images and videos. The images captured underwater are poor quality. There have been several researches yet they have many drawacks.We have proposed pipeline.Our model consists of restoration,enhancement,object detection. Restoration process built from deep convolutional neural network called autoencoder .Which has been trained by 5000 synthetic images. The second model is the self-supervised enhancement model. The selfsupervised model is trained for 10,000 epochs of 5,000 datasets.We have used the customized gan model to obtain the best results.We have also used transfer learning and residual network for the improvement of the model.We have reached the PSNR value of 38.33 . CIQUE value 0.82 and UIQM 0.5.Our third model is object detection model. We have used the latest version of YOLOv5 for the betterment and the best object detection model.
Authors - Kishore S, Jeganathan L, Janaki Meena M, Ummity Srinivasa Rao, Jayaram Balabaskaran Abstract - Finding movies from an enormous number of movies that fit our interests and preferences becomes a challenging endeavor. Because recommendation systems address information overload by recommending the most appropriate products to users, they have become widely used in today’s world. The majority of recommendation systems disregard the constraints of the user such as not suggesting certain exceptional movies to them because they aren’t as popular as others. Furthermore, the lack of transparency about how these recommendation algorithms operate creates concerns regarding accountability. In this work, we propose an improved ALS-based recommendation framework that is implemented on Apache Spark and uses HDFS for processing and storing data. In order to address the long tail bias problem, we utilize the ALSbased framework that enhances exposure to low-frequency items through strong interaction filtering. This study employs SHAP to improve transparency and facilitate fairness analysis by explaining the elements generating recommendations to overcome this limitation. Root Mean Square Error (RMSE) and Top-K long-tail exposure metrics are used to assess the model’s performance on a large movie interaction dataset.
Authors - Pradnya Gotmare, Aryan Halkude, Manish Potey Abstract - The high pace of the data-driven applications growth in the distributed settings has enhanced the pressure to ensure that the data sharing infrastructure remains secure, efficient, and privacy-sensitive. The classic centralized data sharing architectures have the intrinsic limitations of being single-point-of-failure, untransparent, and unauthorized access to data, and prone to data corruption. To curb these hurdles, this paper proposes a decentralized approach of sharing secure data with the use of blockchain technology. The suggested system also uses the decentralized and unalterable features of blockchain to provide data integrity, transparency, and confidence among the involved parties without involving third-party intermediaries. Access control policies are the policies implemented using smart contracts to allow only trusted users to access the shared data. The solution is to keep sensitive information in off-chain repositories, where blockchain limitations of storage and scalability do not exist, yet cryptographic hash values and access control measures (ACMs) are stored in the blockchain registry. This design makes sure that the data transactions are confidential and data verifiability and auditability maintained.
Authors - Mutiara Ayu Mawaddah, Norhalina Senan, Mohd Norasri Ismail, Larisang, Muchlis Almubaraq Abstract - With the growing use of smart meters, massive amounts of electricity consumption data are being generated every day. Managing and analyzing this data efficiently is a big challenge. In this study, we generated a smart meter dataset of 10 million records, adding realistic anomalies such as missing values, noise, and unusual spikes to reflect real-world conditions. The data was stored in Hadoop Distributed File System (HDFS) on a single-node virtual machine running on Kali Linux for distributed processing . Using Apache PySpark, we cleaned the data, filled in missing values, identified outliers, and normalized features. For predicting electricity consumption, we trained a linear regression model which achieved a Root Mean Squared Error (RMSE) of 0.0141 and a R2 score of 0.9891, showing that the model predicts consumption very accurately. Overall, this study demonstrates a practical end-to-end approach that combines big data tools and machine learning for smart meter analytics. In the future, this workflow could be extended to multi-node clusters to improve fault tolerance and handle even larger datasets.
Authors - Shilpa Dhopte, Lalit Damahe Abstract - The food portion estimation is a critical component of automated dietary assessment systems, enabling better monitoring of nutritional intake and supporting healthcare, weight management, and public health applications. Traditional self-reporting methods are often inaccurate and time-consuming, motivating the need for computer vision–based approaches that can reliably estimate food portions from images captured in real-world conditions. This paper presents deep learning pipeline for food portion estimation that integrates image preprocessing, deep learning–based segmentation, and geometric volume computation. The data preprocessing with Mask R-CNN used for precise food seg-mentation, providing pixel-level masks and bounding boxes that isolate individual food items from complex backgrounds. The segmented mask is used to estimate the pixel area of the food region. Experimental evaluation demonstrates that the proposed method achieves high segmentation accuracy, with a segmentation IoU of 87.6%, precision of 90.3%, recall of 88.9%, and an F1-score of 89.6%. The pixel area estimation error is limited to 6.8%, resulting in an overall portion estimation accuracy of 89.1%, indicating reliable and consistent performance across different food images. The proposed framework highlights the effectiveness of combining deep instance segmentation with geometric volume estimation for accurate food portion assessment. Future work will focus on multi-view image integration and real-time deployment in mobile dietary monitoring systems to enhance robustness and scalability.
Authors - Md. Shahidul Islam, Md. Murad Hossain, Omar Faruck Ansari Abstract - Time series prediction plays a critical role in monitoring and control of electrical power systems, particularly for detecting frequency fluctuations caused by imbalances between generation and demand. This study proposes an early warning framework for frequency fluctuation events using a hybrid k-Nearest Neighbour (KNN) and Dynamic Time Warping (DTW) approach combined with a global confidence interval based decision mechanism. Electricity frequency data collected from the New Zealand power grid over a six-month period were segmented into training, validation, and testing sequences. Alignment distances between historical and incoming sequences were used to identify precursor patterns indicative of impending frequency disturbances. Experimental results show that the proposed method achieves high warning accuracy with a very low false negative rate, outperforming baseline models such as ARIMA and LSTM. The findings demonstrate that KNN–DTW provides an effective and practical solution for early warning of frequency fluctuations, supporting improved operational reliability in modern power systems.
Authors - Gina Gallegos-Garcıa, Nidia A. Cortez Duarte, Jose A. Arellano Munguıa, Humberto A. Ortega Alcocer Abstract - "Communication has been a topic as ancient as man and at the same time so important that, over time, various forms have been cre- ated to facilitate it, among which stand out: mail, telephony, telegrams, and fax, to name a few. Nowadays many people use instant messaging applications to communicate with each other by feeling that their con- versations are protected. However, that feeling could not be further from reality and should not be taken lightly, since there are always groups focused on taking advantage of the vulnerability of this kind of applica- tions, resulting in users’ privacy being compromised. In this paper, we present the development of an instant messaging application that inte- grates a novel key establishment protocol based on a quantum-resistant algorithm. Our application employs cutting-edge lattice-based crypto- graphic techniques, ensuring robust security against quantum attacks while maintaining operational efficiency. Obtained results show the ap- plication’s viability by offering a practical solution to safeguard mobile communication in the impending quantum era."
Authors - Soumen Halder, Subhamoy Bhaduri, Binayak Mukherjee Abstract - Paraphrasing is significant in applications that require controlled lexical variation to original text with semantic equivalence, especially in educational assessment systems where student answers should be scored on more than surface level matching. Recent transformer-based paraphrasing models do not exhibit regulated structural changes but instead generate uncontrolled changes, are costly in terms of computation, and are not feasible in low-resource or real-time implementations. These limitations are overcome by this work with a lightweight synonymreplacement paraphrasing framework on the basis of exclusive embedding clustering. The proposed EEC-SRP model groups semantically similar words into local embedding clouds and limits the search of synonyms to the tiny areas, which lowers the complexity of search considerably. An embedding augmentation algorithm involves perturbation to form embedding clusters and a neural network is trained to output contextually favorable synonym embeddings in those clusters. Strict semantic fidelity and controlled lexical substitution is ensured by the model by maintaining word count and sentence structure. Experimental analysis of standard paraphrasing tasks show that the suggested methodology attains high levels of semantic similarity, competitive levels of BLEU and ROUGE, and significantly quicker inference than conventional embedding-based and transformer-based models. The proposed model can be effectively implemented in automated assessment systems, controlled text rewriting and resource-constrained applications of natural language processing due to its low memory footprint and computational efficiency.
Authors - Pablo Ramon, Josue Piedra, Nelson Piedra Abstract - The increasing numbers of deepfakes and AI tools have made it difficult to trust digital images these days. Images can be altered and ownership can be established without revealing private information. Current systems have many limitations, and systems that either rely on easyto change metadata or on cryptographic methods that are too costly like ZKSNARKs. To overcome these limitations, an authentication verification model has been presented named ZKP-Guard based on a Dual- Lock architecture framework. The detection system verifies an image is a real image by using ECDSA signatures and a custom ownership in the Schnorr-based Zero-Knowledge Proof for the protocol. This framework was tested on a dataset with significant number of images and produced desired results.
Associate Professor and Head, Department of Artificial Intelligence and Data Science, Vidyavardhini's College of Engineering and Technology, Maharashtra, India
