Volume 20 Issue 8 2025

Grey wolf optimizer (GWO) is arising from grey wolves. The grey wolf algorithmic rule imitate the hunting mechanism of grey wolf. This algorithmic rule is very successful in engineering field. GWO algorithmic rule is very popular in research and analyses in terms of anatomy and manipulation. In (GWO) algorithmic rule author saw that half of the iterations are dedicated to anatomy and half is dedicated to manipulation. There is a right balance b/w anatomy and manipulation which shows approximation of global optimum. To overcome this effect a new modified algorithm (MGWO) is introduced. MGWO focus on proper stability between anatomy and that shows manipulation optimal performance of the algorithmic rule .The Modified algorithmic rule is benefits from high anatomy as compared to original GWO. MGWO has an ability to solve real world optimization problem.

India has announced the demonetization of currency notes from economy on 8 Nov. 2016, which results into the removing of 86% of total circulated money from economy. The immediate reduction in the circulation of money has created significant disruption in the economy. The present paper is concerned with the impact of demonetization over the stock prices in the Indian stock market. The Percentage and GARCH model are used to analyze the available secondary data of stock prices. The results indicate that there is fluctuating trend of prices after implementing demonetization; however this trend does not exist for longer time period. Further analysis shows significant difference in the standard deviation of the observations, indicates the presence of volatility up to some extent. In general the study concludes that demonetization has only short term impact over stock prices.

Intelligent machines can replace or changes human life in so many areas in the future. Artificial intelligence is the intelligence of machines which is related to humans brain, i.e computers behave like humans. Application area of artificial intelligence has a huge impact on our day to day life to solve any complex problem in many areas like science , business, engineering, medicines and the systems that can learn data i.e Machine learning , describes the ability of computers to learn more data. Artificial intelligence is used to design power system oscillations which is due to networks like communication networks. As the size and quality of power system consisting of generators, transmissions etc, increases the risk of faults. As the power system explores day by day because if Artificial intelligence, it seems to be more complex because of the technology, requirements. etc. This paper gives an overview of these technologies and describes Machine learning feature, algorithms etc.

Nowadays certain actions are taken to improve the level of cleanliness in the country. People are getting more active in doing all the things possible to clean their surroundings. Various movements are also started by the government to increase cleanliness and waste management. IoT and machine learning based waste management systems are very effective systems nowadays for societal use. In this paper we have discussed existing methods of segregation of waste, and how IoT and machine learning can be useful to solve major problems related to waste management. This paper guides about how to segregate biodegradable and non biodegradable waste and recycle biodegradable waste for making compost. Also we have discussed various relevant solutions proposed by various researchers in the related work.

The human voice can be characterized by several attributes such as pitch, timbre, loudness, and vocal tone. It has often been observed that humans express their emotions by varying different vocal attributes during speech generation. This paper presents an algorithmic approach for detection of human emotions with the help speech .The prime objective of this paper isto recognize emotionsin speech and classify them in 6 emotion output classes namely angry, fear, disgust, happy, sad and neutral. The proposed approach is based upon the Mel Frequency Cepstral coefficients (MFCC) uses Crema-D database of emotional speech. Data Augmention is perfomed on input data audio file,such as Noise, High Speed, Low Speed etc. are added, thus more the varied data is available to the model better the model understands. Feature extraction is done using MFCC and then the extracted features are Normalized(for Independent Variable), Label Encoding(for Dependent Variable(for SVM,RF)),One Hot Encoding(for Dependent Variable(for CNN))is done. After thisthe datasetis divided into Train, Test and given to different models such as Convolutional Neural Network(CNN),Support Vector Machine(SVM), Random Forest(RF) for Emotion prediction. We report accuracy, f-score, precision and recall for the different experiment settings we evaluated our models in. Convolutional Neural Network(CNN) was found to have the highest accuracy and predicted correct emotion 88.21%ofthe time. Hence, deduction of human emotionsthrough speech analysis has a practical plausibility and could potentially be beneficial for improving human conversational and persuasion skills.

In this research paper, the effect of seismic load for overhead water tank, the water is the most essential element to a life on the earth. There is two different types of tank, of same capacity but having different height and different diameter of the overhead water tank in seismic zone (zone IV and V), this study on displacement of tank, time period, base shear and base moment cofficient of different height and diameter of overhead water tank, seismic plays an important in design of tank structure because of dynamic nature. Effect of seismic is predominant on tank structure, height of the struture in this paper the compression of the tank their height and its different diameter on tank for analysis of seismic loads on tanks

Panels help in constructing affordable and low cost construction. it's eco-friendly material and is advantageous over conventional building materials and avoids heavy weight building like with concrete. This paper shows the production of gypsum panels with using of agricultural waste such as rice straw, rice husk with glass fiber is very affordable and can easily be casted at sites.

A Monowave synthetic route for the production of Palladium nanoparticles using Gum karaya(GK) is examined for in vitro cytotoxic efficacy against MCF-7. The present work proves the influence of gum karaya composition on the size and morphology of the produced Palladium nanoparticles. The biosynthetic method is quick, simple and easily synthesized and color changes observed in Ultraviolet and visible spectroscopy. Subsequently, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FTIR) and Transmission electron microscopy (TEM) analyses were employed to characterize the as synthesized GKPdNP. The XRD investigation confirmed the presence of Pd (0) nanoparticles and interactions between the bio degradable compounds of the Gum and the produced GKPdNP were confirmed by the FTIR spectra. TEM images demonstrated that the nanoparticles has a size distribution of 3±2 nm. Moreover, the anticancer activity of the GKPdNP was tested against MCF-7 strains. All synthesized Pd nanoparticles exhibited good antioxidant activity

The Proliferation of AI Technologies has led to a surge in patent filings, reflecting heightened competition amongst market players to secure proprietary rights over AI-related innovation. AI has emerged as a novel intervention with far-reaching implications for market sustainability in the context of a shifting market scenario. Integration of AI tools into daily life would represent an intersection of digital innovation and social welfare. This interaction necessitates re-evaluation of strategies for protecting intellectual property, ensuring that the patent system fosters innovation while safeguarding the rights of creators in an era defined by rapid technological advancement. However, the current AI market trend must create a synergy between Digital India initiatives while exploring sustainability metrics and future projections. This also necessitates thorough analysis, given long-term market viability, legal and regulatory compliance, as well as ethical concerns, which can be evaluated by incorporating performance indices, such as a sustainability scorecard, quality management (including risk management), to ascertain market readiness. Another critical point of contention arises concerning patenting AI innovations because the present IP Laws in India do not correspond to it. This seems to pose a stumbling block in achieving development initiatives. Patenting AI technologies can penetrate over 70% of the target demographic, driven by policy incentives and digital infrastructure enhancement. This would additionally increase consumer confidence and promote sustainable investment. This paper examines the implications of AI-driven inventions within the legal framework and the broader market sustainability agenda, focusing on the challenges and opportunities presented by this emerging technology. Furthermore, this study aims to provide evidence-based recommendations for fostering an ecosystem that resonates with the long-term aspiration of a digitally empowered and environmentally conscious India.