Parag S. Deshpande

Despite significant advances, modern Vision-Language Model (VLM) platforms remain constrained by the scarcity of training data in non-English languages, limiting their global applicability in Text extraction for regional languages written in Devanagari scripts. Recognizing Devanagari text, particularly in literary materials, poses significant challenges in achieving high accuracy and efficient execution. Effective Text extraction in current VLM platforms must perform several pre-processing steps to retain crucial information while preserving sentence structure and meaning. This study proposes an image enhancement method for Devanagari text based on geometric filters and clustering algorithms.By leveraging the unique geometrical characteristics of Devanagari characters, the technique accurately detects Devanagari sentences in various font sizes, orientations, and degraded hand-held camera-captured images. It is robust to variations in document rotation, size, and color.The method was tested on a dataset of images from books, papers, brochures, and pamphlets, demonstrating superior performance over existing OCR pre-processing techniques for current VLM platforms. The proposed approach achieved a success rate of 95.23% in locating Devanagari sentences, improving the accuracy of current VLM platforms Text extraction.

The exponential increase in vehicles, quick urbanization, and rising demand for transportation are straining the world’s road infrastructure today. To have a sustainable transportation system with dynamic traffic volume, an Adaptive Traffic Signal Control system (ATSC) should be contemplated to reduce urban traffic congestion and, thus, help reduce the carbon footprints/emissions of greenhouse gases. With dynamic cleave, the ATSC system can adapt the signal timing settings in real-time according to seasonal and short-term variations in traffic demand, enhancing the effectiveness of traffic operations on urban road networks. This paper provides a comprehensive study on the insights, technical lineaments, and status of various research work in ATSC. In this paper, the ATSC is categorized based on several road intersections (RIs), viz., single-intersection (SI) and multiple-intersection (MI) techniques, viz., Fuzzy Logic (FL), Metaheuristic (MH), Dynamic Programming (DP), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL), and hybrids used for developing Traffic Signal Control (TSC) systems. The findings from this review demonstrate that modern ATSC systems designed using various techniques offer substantial improvements in managing the dynamic density of the traffic flow. There is still a lot of scope to research by increasing the number of RIs while designing the ATSC system to suit real-life applications.

The advancements in the smart grid involves high penetration of renewable energy resources into distribution system by building a microgrid. However, the intermittent nature of the nondispatchable generation leads to the issue of demand supply mismatch in the microgrid system. It will be sensible for microgrids to trade energy with one another. This requires a smart distributor, which collects data regarding superfluous energy from providers and takes fair decision of microgrid-generated energy allocation among consumers. This paper presents a starvation-free optimal microgrid-generated energy allocation policy (SF-OEAP) in a smart distributed multimicrogrid system. A novel optimal energy allocation policy is proposed wherein, prediction accuracy of net load forecast and revenue index of consumer microgrid are considered as vital parameters. In a prioritized energy distribution mechanism, it may happen that the consumer microgrid can be starved of microgrid-generated energy. Therefore, in addition to vital parameters, starvation parameter is considered in designing energy allocation policy for sustaining the consumers in the smart distributed multimicrogrid system. Furthermore, the proposed scheme uses weights to control the importance of each of the parameters in the energy allocation policy. The proposed SF-OEAP is simulated and justified so that it is stable enough to apply in a real-time electricity market.

EFFICIENT APPROACH FOR VIEW SELECTION FOR DATA WAREHOUSE USING TREE MINING AND EVOLUTIONARY COMPUTATION

Multilingual character recognition from the images of aged Indian documents is challenging because of the complex character grapheme of the Indian language scripts. Feature extraction plays the most important role in recognition of such images. In this paper, we have proposed a set of feature vectors (FVs) which are based on shape geometry (SG) decoding of the input character. The first FV is based on SG decoding of the input character using triangular area (TA) calculation. The second FV, namely, SG using perpendicular distance is extracted by dividing the input image into individual components and the shape of the individual component is decoded into shape symbols by comparing the normalized perpendicular distances of the individual pixels of the component onto the line joining the end points of the component. Apart from the proposed FVs, we have used crossing count features. These FVs are represented as the string of shape operators; hence, we have used minimum edit distance classifier to recognize the input character. The proposed character recognition technique is evaluated using the characters extracted from printed aged multilingual Indian documents having English, Devanagari, and Marathi scripts and achieved encouraging results. To further assess the performance of the proposed system, we have considered publicly available media-lab license plate benchmark database and achieved significant performance.

Discovery of cause–effect relationships, particularly in large databases of time-series is challenging because of continuous data of different characteristics and complex lagged relationships. In this paper, we have proposed a novel approach, to extract cause–effect relationships in large time series data set of socioeconomic indicators. The method enhances the scope of relationship discovery to cause–effect relationships by identifying multiple causal structures such as binary, transitive, many to one and cyclic. We use temporal association and temporal odds ratio to exclude noncausal association and to ensure the high reliability of discovered causal rules. We assess the method with both synthetic and real-world datasets. Our proposed method will help to build quantitative models to analyze socioeconomic processes by generating a precise cause–effect relationship between different economic indicators. The outcome shows that the proposed method can effectively discover existing causality structure in large time series databases.

This paper presents two novel geometrical feature extraction techniques for license plate (LP) characters recognition, written in English alphabets and numerals. A robust character recognition technique has to recognize different format, broken, and distorted characters by extracting invariant and discriminative character features. In this paper, we have extracted two types of geometrical features and a statistical crossing count feature with the help of horizontal, vertical, right diagonal, and left diagonal scan lines and centroid of the character to be recognized. For character recognition, we have used edit distance metric. The proposed technique can be enhanced to recognize any language scripts and is tested for Indian scripts. The proposed LP characters recognition technique is evaluated using 741 images of media-lab benchmark database, 49 images of Israeli vehicle LPs, and 110 images of proprietary Indian vehicle LPs having different environmental conditions and plate variations. The proposed technique achieved success rate of 98.8% for LP characters recognition which outperforms many LP character recognition techniques in the literature.