Authors - Banda Rithija, MV Parth, Haripriya L, Skandan SS, Manju Abstract - The task of identifying Cryptographic Algorithms from ciphertext is a challenge within digital forensics and security auditing, when there is no knowledge of either the plaintext or the key used. As modern encryption algorithms increase in sophistication, their output becomes indistinguishable from random noise, rendering traditional pattern recognition techniques ineffective. This paper proposes a two-stage Hierarchical Cipher Classifiers, the first stage discriminates among three major Cryptographic Families: Symmetric, Asymmetric, and Hash; the second stage identifies the specific algorithm within those families in the context of six Modern Encryption Standards: Advanced Encryption Standard, Triple Data Encryption Standard, Blowfish, Rivest–Shamir–Adleman, ElGamal, and Secure Hash Algorithm 256-bit. In order to achieve high accuracy, we developed a hybrid feature space consisting of 167 attributes that included both Statistical and Transform- Domain Features.We incorporated SHapley Additive exPlanations (SHAP) into our classifiers to address the concern of the black-box nature of Deep Learning. Empirical Results indicate that the Hierarchical Classifier Structure has produced a substantial reduction in the rate of misclassifications compared to flat classifiers, offering a transparent and effective tool for automated cryptanalysis.
Authors - Megha Potdar Abstract - This paper delineates a compact microstrip patch antenna that operates within the frequency range of 6.5 to 8.5 THz and exhibits a resonance frequency of 7.344 THz. The antenna maintains a flat, compact shape that is well-suited for terahertz circuit integration and also incorporates circular and U-shaped patch modifications that enhance radiation efficiency, gain, and band-width. According to the simulation results, the device has a gain of 7.042 dBi, a VSWR of 1.1329, a low return loss of –24.109 dB, and a wide impedance bandwidth of 1.119 THz. It demonstrates consistent radiation patterns and effective impedance matching across the operating frequency range, indicating that the proposed design outperforms conventional THz patch antennas and rep-resents a highly efficient solution for high-speed terahertz communication, im-aging, and sensing applications.
Authors - Babatunde David Ikudehinbu, Atefeh Khazaei, Hamidreza Khaleghzadeh Abstract - In this paper, we outline the design and implementation of a novel electronic voting kiosk, dubbed BlockVote, which helps counter identity-related fraud and data tampering via biometric and blockchainbased approaches. The proposed system is a standalone embedded system running on an ESP32-S3 SoC-based microcontroller. The system includes a touchscreen display for user input and an optical fingerprint sensor for identity checking. This collected bio-data and voting selection are then integrated in such a manner that a secure transaction is created through cryptography. This is then sent through the Node.js gateway, which leads it to the secure Ethereum-based blockchain network. Such an application of physical verification technologies with blockchain technology ensures that the proposed voting system is more secure than the traditional e-voting machines or e-voting websites. Block-vote is a hybrid security system in which hardware-based verification techniques are combined with blockchain-based data management in a power-saving, compact format. The prototype has shown proof of its functional viability, its module-based construction, and its reliability, particularly in the field of embedded systems. The experimental results demonstrate the system’s high precision, low latency, and robustness against illegitimate use. The suggested framework demonstrates the practical feasibility of blockchain and biometric technology in the creation of trustworthy electronic voting systems that can be used in both urban and rural areas.
Authors - Deepika K M, Girish Gowda J, Ravi Honnalli, Nikhil S G Abstract - Cloud computing environments face increasingly sophisticated cyber threats that demand advanced detection mechanisms capable of identifying anomalous behavior in real-time. This study introduces an innovative hybrid temporal anomaly modeling system that integrates Autoregressive Integrated Moving Average (ARIMA) with Long Short-Term Memory (LSTM) networks, augmented by meta-learning fusion strategies. Our method solves the difficult problem of getting high recall rates (>95%) that are needed to keep operational efficiency while reducing missed critical threats. We tested five meta-learning architectures—Logistic Regression, Random Forest, XG-Boost, Gradient Boosting, and Neural Network—along with four rule-based fusion strategies on a large Cloud Anomaly Dataset with 249,595 samples taken from 11 virtual machines over 30 days. The Hybrid-RF (Random Forest) model had the best balance, with a recall of 95.75%, an accuracy of 10.59%, and an F1-score of 11.37%. This was much better than the average in the literature (75-85% recall). We set up the system as a production-ready Flask REST API on Google Cloud Platform, with response times of less than 200 milliseconds. This shows that it is possible to use real-time cloud security monitoring. Our findings demonstrate that metalearning fusion of statistical and deep learning temporal models yields enhanced threat detection capabilities relative to single-model approaches, achieving recall improvements of 10-20% over state-of- the-art methods while adhering to real-time performance constraints.
Authors - Anup Bhitre, Saurabh Nimje, Utkarsha Pacharaney, K. T. Reddy Abstract - Cervical Spinal Stenosis (CSS) is a progressive spinal disorder caused by narrowing of the spinal canal in the neck, potentially leading to severe neurological damage if undiagnosed. Due to rising CSS cases and the limitations of manual MRI analysis—such as subjectivity, time consumption, and inter-observer variation—there is a growing need for automated, reliable diagnostic tools. This study evaluates and compares four AI models—CNN, ResNet50, SVM, and Random Forest—using 1,200 T2-weighted MRI images processed through normalization, segmentation, and augmentation. Performance was measured using accuracy, precision, recall, F1-score, and AUC-ROC. ResNet50 achieved the highest accuracy (93.6%) and AUC-ROC (0.97), demonstrating superior diagnostic performance. SHAP was used for interpretability, highlighting spinal canal diameter and ligamentum flavum thickening as key diagnostic features. The findings confirm that deep learning, especially ResNet50, offers a scalable, interpretable, and clinically effective method for early CSS detection.
Authors - C Ashik Poojary, Chirag B Jogi, Sanath Shetty, Sandhya P, Mahitha G Abstract - Image inpainting plays an important role in restoring and reconstructing degraded or damaged images by filling in missing regions. This work proposes a gated convolutional neural network based on a U-Net architecture to achieve perceptually accurate and high-resolution restoration. The model was trained on a large-scale dataset of over 20,000 images generated with the CelebA dataset along with extensive enhancement using artificial damages such as scratches, cracks, random patches, blurring, sepia-toning, and grayscale degradation. The proposed method performs two phases of restoration: context-aware inpainting, followed by resolution enhancement while preserving both global structure and local texture. Quantitative metrics such as PSNR and SSIM were evaluated, and qualitative comparisons demonstrate faithful texture synthesis and tone-consistent fills across color, grayscale, and sepia domains.
Authors - D.Nagaraju, Padinjaroot Monesh Raj, G .Likith, K .Kavitha, Thella Muni Chandrika Abstract - This Gesture recognition technology is studied in this article as it pertains to controlling music wirelessly via a music controller de-vice. The gesture recognition system highlighted in this study is an innovative advancement in this area. In addition to providing the user with an easy-to-use interface for controlling the volume of music with hand motions, This provides a no-contact way to play percussion instruments where users can play from anywhere, either they have good eyesight or not! In addition to providing users with visual experience while using the application, the application also provides users with 3D graphics and animations that dynamically reflect the user’s movement on the screen as they create percussion music through the application. The entire system is created using JavaScript and thus, is completely platform-independent and will work on any recent web browser.
Authors - Aditya Ajitrao Kulkarni, Mayuri Shelke, Saurabh Babasaheb Gonte, Kalpak Sanjay Kedari, Parikshit Balasaheb Jadhav Abstract - Image inpainting is a basic problem in image restoration that focuses on recovering the missing or damaged areas of an image in a visually plausible and semantically consistent way. However, in practical image restoration tasks like historical photo restoration, images are often degraded by complex damages like cracks, scratches, fading, stains, and tone changes. Conventional image restoration methods relying on interpolation or diffusion have limitations in restoring high-frequency details and global semantic information. This paper presents a gated convolutional neural network with a U-Net structure for effective image inpainting and restoration with resolution enhancement. The proposed network is trained on a large-scale dataset of more than 20,000 synthetically degraded images created from the CelebA dataset, considering various damage patterns like scratches, cracks, random occlusions, blurring, grayscale conversion, and sepia tone transformation. The image restoration process involves two steps: context-aware image inpainting and resolution refinement. The proposed framework is extensively evaluated using PSNR and SSIM metrics for its effectiveness in color, grayscale, and sepia image restoration.
Authors - G Venkata Suresh Reddy, Immanuel Anupalli, P.Sudheer Abstract - Solar photovoltaic (PV) systems require robust and intelligent problem detection systems to guarantee they continue producing energy effectively as they gain traction as a renewable energy source. In order to detect various defects in photovoltaic (PV) systems operating under nonlinear and noisy conditions, this research presents a data-driven fault classification framework that employs machine learning techniques. Electrical data from photovoltaic (PV) panels, including current-voltage (I-V) and power-voltage (P-V) curves recorded in three distinct operating circumstances (Healthy, Shading, and Open-Circuit), formed the basis of the dataset used for training and testing the model. For each condition, crucial electrical characteristics have been used to characterize the system's electrical behavior, including open-circuit voltage, short-circuit current, maximum power point voltage and current, fill factor, and a handful of statistical statistics. Logistic Regression, Naïve Bayes, and k-Nearest Neighbors (KNN) are the three supervised machine learning methods that were employed to detect various errors. Each model was fine-tuned using hyper parameter tweaking and k-fold cross-validation. The classification performance in the comparative performance analysis was greatest for Logistic Regression (96.09% accuracy, 96.25% precision, 96.49% recall, and 96.36% F1-score). Second place went to the KNN model, which had a 95.47% accuracy rate. In contrast, the Naïve Bayes model maintained its reliability, with an accuracy rate of 94.13%. This demonstrates that it is still effective when dealing with nonlinear data that contains noise. According to the overall results, many machine learning algorithms, especially Logistic Regression, do a great job of finding PV problems in real time. The suggested framework is both efficient and useful for real-world PV monitoring systems because it just needs to measure electrical parameters that are easy to get (I-V and P-V data). Using this strategy for preventative maintenance makes solar systems more reliable and increases their production, which in turn cuts down on power losses.
Authors - Premanand Ghadekar, Utkarsh Patil, Niraj Ukare, Vansh Bhatt, Rohan Uplenchwar, Shreya Sidnale Abstract - Traditional multi-agent communication systems rely on fixed security protocols and static message processing pipelines, leaving them vulnerable to advancing cyberattacks and dependent on expensive infrastructure. This paper introduces a Secure Multi-Agent Communicational Protocol designed as a lightweight, affordable framework for small and medium-scale systems to communicate safely without enterpriselevel costs. The current setup depends heavily on predictable session keys, making systems prone to impersonation, replay attacks, token alterations, and man-in-the-middle interceptions. This framework stimulates agentto- agent interactions through three primary components: a predictive security model, a dual-token authentication mechanism, and a protocolaware attack engine. The infrastructure utilizes WebSocket connections integrated with Redis Pub/Sub for real-time messaging. A dynamic session key generation process works alongside a rotating refresh-token system, ensuring that even if a session key is compromised, attackers still require a valid refresh token. The predictive component features a Protocol-Aware XLNet model with a dual-thread structure to examine message sequences and statistical irregularities. A fusion layer integrates these analyses, reporting a Dual-Thread Consistency Score of 0.87 and a 31% gain in early-warning capability. Experimental evaluations demonstrate 93.5% violation sensitivity, 91.7% replay detection accuracy, and 89.3% attack-type classification accuracy. This approach enables timely identification of replay incidents, interceptions, and protocol tampering. Additionally, an independent XGBoost model filters fraudulent links. These enhancements provide substantial gains in early warning capabilities and consistent classification accuracy across various attack categories.
