Since the inception of stock trading, scholars and investors have searched for reliable methods to forecast the course of stock values the next day. Since there are several variables that might influence the stock values of the next day, forecasting stock prices is a challenging undertaking. Stock Market Forecasting (SMF) is a forward-looking process anticipating future stock values, allowing to make sound financial decisions. In order to create predictions, academics and investors have started using machine learning approaches in conjunction with technical indicator analysis. However, the precision of the predictions is lacking. One of the progress in applying ML, particularly LSTM networks, to stock market forecasting lies in automating this process. Human bias implies that the same predictions can be misleading and contribute to the fact that they need to use ML and AI technology. The data used was fetched from finance.yahoo.com, and for confidence in the data, it took steps such as lemmatisation, null value management and deletion of duplicates. A total of four different ML prediction methods were utilised: LSTM is also being used ANN, CNN, K-Nearest Neighbour and many other algorithms. The model's performance was evaluated using measures including F1-score(Fs), recall(Rc), accuracy(Acc), and precision(Pr). Outcomes showed that the models were not all equally successful; however, the LSTM model had the best accuracy at 93%. Future attempts might consider other categorisation strategies and improving preprocessing methods to improve model performance and forecast Acc

Trauma plays a major role in the formation of an individual’s self A devalued self often emerges from having a marginal status in the society, where traumas from racism, poverty, violence and exploitation are more likely to occur. The most difficult aspect of traumatic situations for victims is feeling that one is powerless to change his or her situation. Terrorism in Literature puts forth a number of thought-provoking questions before the readers as well as the writers. It ranges from the ethical function of literature to reconsidering our cultural assumptions about identity, relationality, and intentionality, to what contingencies determine how or if the individuals survive the devastations of trauma. Hosseini’s The Kite Runner published in the year 2003, introduces the readers to the country of Afghanistan, looks at the universal theme of transgression and forgiveness, of homeland and exile. This novel serve as a medium for the hapless millions of Afghans to voice the trauma of their existence. The Kite Runner (2003) gives a vision of what Afghanistan was before its association as a haven for terrorists. Hosseini deals with the theme of terrorism in his first novel The Kite Runner, in which he has given a genuine insight to the people of the world about the extremists Taliban, in particular, and the culture, and the traditions of Afghan people in general. This paper seeks to examine Hosseini's depiction of the complexities of life in Afghanistan during the regime of the Talibans. It shows how the country was seen as an asylum for some terrorists and their allies who are accused of the attack in the United States and other countries. Moreover, the paper addresses the need to fight against terrorism as a phenomenon to which the world became the witness of this gruesome reality. It, therefore, provides an analysis of terrorism, revealing the people's suffering caused by this terrorism. This paper seeks to examine Hosseini's depiction of the complexities of life in Afghanistan during the regime of the Talibans.It shows how the country was seen as an asylum for some terrorists and their allies who are accused of the attack in the United States and other countries

This document gives formatting instructions for authors preparing papers for publication in the International Journal. The authors must follow the instructions given in the document for the papers to be published. You can use this document as both an instruction set and as a template into which you can type your own text.Artificial Intelligence (AI) plays a vital role in addressing the challenges of rapid urbanization and sustainable city development. The motive of this study is to explore how AI technologies can enhance urban planning processes for smart cities. The method involves analyzing AI applications such as machine learning, predictive analytics, and Internet of Things (IoT) integration for data-driven decision-making in areas like traffic control, energy management, and infrastructure optimization. Key results indicate that AI-based planning improves efficiency, reduces resource wastage, and enhances real-time monitoring of urban systems. The study concludes that incorporating AI into urban planning not only supports sustainable development but also helps create intelligent, resilient, and livable cities capable of adapting to future demands

Wildfires pose a significant threat to ecosystems, human lives, and infrastructure worldwide. Traditional wildfire detection and risk assessment methods often suffer from limitations such as delayed detection and low confidence in certain regions. In this paper, we propose a novel computational system based on Machine Learning for wildfire risk assessment using data collected by drones. The system can integrate various sensors to capture spatiotemporal data on environmental factors such as temperature, humidity, and vegetation. By leveraging high-resolution data collected through autonomous drone missions, our system enhances wildfire risk estimation and enables proactive mission planning. Although the system is mainly designed to address wildfire monitoring using drone-collected data, it can be easily adapted to other environmental monitoring applications and other sources of data. We demonstrate the effectiveness of our approach through a comprehensive evaluation and validation process in both simulated and real world environments. Our work contributes to advancing wildfire monitoring capabilities, improving early detection, and mitigating the impact of wildfires on communities and the environment

Sweet marjoram (Origanum majorana) is a pleasant-smelling perennial herb from the mint family. It is mainly grown in Mediterranean countries and also in many other places, including India. For a very long time, people have used it not only as a flavorful kitchen herb but also as a natural remedy. In traditional medicine, it is commonly used to help with digestion problems, breathing issues, heart troubles, joint pain, and disorders related to the nerves.Scientific studies show that sweet marjoram contains many beneficial natural chemicals, especially essential oils. These oils are rich in compounds like carvacrol, thymol, linalool, terpineol, and eugenol, which are responsible for its strong aroma and many of its health benefits. Advanced laboratory studies have also discovered some unique compounds in this plant, including 1H-indole-2-carboxylic acid, lariciresol, isolariciresol, and procumboside B.Procumboside B is especially important because it shows strong effects on the immune system. It helps activate immune cells by increasing the production of substances like nitric oxide and certain immune signals, and it also improves the surface activity of specific immune markers on macrophages. These actions are linked to its effect on important immune signaling pathways in the body. In modern medicine, sweet marjoram has been found to show many helpful properties, such as reducing inflammation, protecting the liver, fighting microbes, helping control blood sugar, supporting heart health, protecting against tumors, reducing anxiety, improving digestion, and helping wounds heal faster. Nutritionally, it is a rich source of vitamins and minerals like beta-carotene, vitamin A, iron, lutein, zeaxanthin, and folate. These nutrients contribute to its antioxidant effects and may help in improving hemoglobin levels. Overall, sweet marjoram is more than just a culinary herb. It is a powerful natural plant with strong health-boosting and immune-supporting properties. Its wide range of benefits supports its importance in traditional medicine and shows its potential for future use in modern healthcare and natural medicine

The present study focuses on the preparation of activated carbon (AC) from rice husk waste, an abundant agricultural by-product, using chemical activation with potassium hydroxide (KOH). The developed rice husk activated carbon (RHAC-KOH) was utilized for the adsorptive removal of Bismarck Brown dye from aqueous solutions. The process involved two major steps—carbonization of rice husk at controlled temperature, followed by chemical activation using KOH to enhance surface porosity and adsorption efficiency. The prepared adsorbent was characterized by its surface texture, pore structure, and color removal efficiency, confirming successful activation. Batch adsorption experiments were conducted by varying parameters such as contact time, dye concentration, and adsorbent dosage. The adsorption data were analyzed using kinetic and isotherm models, and results indicated that the process followed the pseudo-second-order kinetic model, suggesting chemisorption as the dominant mechanism. The maximum adsorption efficiency of RHAC-KOH for Bismarck Brown dye was observed at neutral pH (≈7) and a contact time of 150 minutes, demonstrating the potential of rice husk-based activated carbon as a low-cost, eco-friendly, and effective adsorbent for dye- contaminated wastewater treatment.

The growing IoT landscape requires effective server deployment strategies to meet demands including real-time processing and energy efficiency. This is complicated by heterogeneous, dynamic applications and servers. To address these challenges, we propose ReinFog, a modular distributed software empowered with Deep Reinforcement Learning (DRL) for adaptive resource management across edge/fog and cloud environments. ReinFog enables the practical development/deployment of various centralized and distributed DRL techniques for resource management in edge/fog and cloud computing environments. It also supports integrating native and library-based DRL techniques for diverse IoT application scheduling objectives. Additionally, ReinFog allows for customizing deployment configurations for different DRL techniques, including the number and placement of DRL Learners and DRL Workers in large-scale distributed systems. Besides, we propose a novel Memetic Algorithm for DRL Component (e.g., DRL Learners and DRL Workers) Placement in ReinFog named MADCP, which combines the strengths of Genetic Algorithm, Firefly Algorithm, and Particle Swarm Optimization. Experiments reveal that the DRL mechanisms developed within ReinFog have significantly enhanced both centralized and distributed DRL techniques implementation. These advancements have resulted in notable improvements in IoT application performance, reducing response time by 45%, energy consumption by 39%, and weighted cost by 37%, while maintaining minimal scheduling overhead. Additionally, ReinFog exhibits remarkable scalability, with a rise in DRL Workers from 1 to 30 causing only a 0.3-second increase in startup time and around 2 MB more RAM per Worker. The proposed MADCP for DRL component placement further accelerates the convergence rate of DRL techniques by up to 38%.

HPLC is one of the most valuable types of column chromatography techniques used in pharmacy, pharmaceutical business, and biochemistry. It is utilized for the identification, separation, quantification, validation, and optimization of active substances. Drugs can be sorted, located, and measured using a separation process called HPLC [High Performance Liquid Chromatography]. This article focuses on the research, development, and manufacturing of innovative medications. In the human and animal studies as well. Developing and qualifying an HPLC technology requires a number of stages. It depends on the molecule's polarity, solubility, pH, PK, and other factors. Following ICH criteria for HPLC method validation provides information on performance aspects such an important performance characteristics for method validation includes trueness, repeatability, selectivity, proportionality, working intervals, minimum quantifiable concentration, and method resilience

An era of massive data generation and real-time application demands has been ushered in by the Internet of Things (IoT). Strong compute and storage capabilities are offered by traditional cloud-only solutions, but they frequently fall short of the stringent latency requirements of mission-critical IoT applications. Edge computing reduces latency by bringing processing closer to the data source, but it also adds computational and storage limitations. Using a Smart Traffic Monitoring System as a case study, this paper suggests and assesses a hybrid edge-cloud architecture intended for real-time IoT applications. A thorough review of the literature, the problem statement, the suggested architecture, the methodology, a comparison of cloud, edge, and hybrid performance, and deployment recommendations are all presented in this study. The findings show that a hybrid strategy can significantly lower latency while controlling bandwidth and cost

Agriculture contributes enormously to global food security, but crop diseases result in huge yield losses every year, compromising food production globally. Conventional disease identification practices depend mostly on visual inspection by agricultural specialists, which is time-consuming, subjective, and not accessible to small farmers. Deep Learning (DL) has come forward as a revolutionary technology to implement automated plant disease detection with the promise of quick, precise, and scalable applications. This work introduces an end-to-end deep learning-based plant disease detection and classification system using Convolutional Neural Networks (CNNs). The system applies transfer learning using pre-trained networks like VGG16, ResNet50, and MobileNetV2 with a high accuracy and efficiency in computations. The system takes leaf images using smartphones or Internet of Things (IoT)-based cameras, performs processing using a trained CNN model, and makes real-time diagnosis along with treatment suggestions. Experimental outcomes show classification accuracy of over 95% for various crop species like tomato, potato, apple, and corn. Combining this technology with IoT devices and mobile applications facilitates farmers to make prompt decisions based on accurate information, saving losses on crops and ensuring eco-friendly farming. This work contributes to precision agriculture by narrowing the gap between cutting-edge AI technologies and effective farming requirements