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Predicting Coal Prices: A Machine Learning Approach for Informed Decision-Making
This research addresses the critical need for accurate coal price prediction in the dynamic global market, crucial for informing strategic decisions and investment choices. With coal playing a vital role in the world energy mix, its price fluctuations impact industries and economies worldwide. The study employs advanced machine learning models, including Linear Regression, Random Forest, SVM, Adaboost, and ARIMA, to enhance prediction precision. Key features such as S&P 500, Crude Oil Price, CPI, Exchange Rates, and Total Electricity Consumption are identified through feature importance analysis. The Random Forest model emerges as the most effective, emphasizing the significance of key variables. Leveraging explainable AI techniques, the study provides transparent insights into model decision-making, offering valuable information for risk management and strategic decision-making in the volatile coal market 2024 IEEE. -
Sustainability Indicators and Ten Smart Cities Review
The motivation of smart cities is to improve the standard of living of citizens and enhance the use of technology in sustainable city services. A city's sustainability can be measured using various sets of smart indicators. This study will analyse urban sustainability indicators as a research problem for ten smart cities. The review of smart cities will focus on the Internet of things (IoT), Mobile devices, and Artificial intelligence technologies (Sensors in street lights, smart homes) that help our citizens transform from rural to urban areas towards sustainability. This research uses a qualitative framework for the taxonomy of the literature for the terms 'smart city' and 'sustainability' Further, the characteristics, critical technology, and IOT application for mobility are elaborated upon. Finally, we discuss ten smart city review proposals reports, based on their sustainability indicators around the world. Concluding and Future studies could focus on using sustainable indicators for developing smart cities in India. 2023 IEEE. -
Machine Learning Approach for the Prediction of Consumer Food Price Index
The price of food and food related items are dynamic. A measure change in the price affects the buying behaviour of the consumer and monetary policies by the Government. The Consumer Food Price Index (CFPI) reflects the variations in food prices during a certain period. In India, the CFPI is released monthly by the Central Statistical Organization. It also reflects the inflation and helps the Government to take corrective measures in time. In this paper we have applied the machine learning approach in forecasting the consumer food price index in India. In specific, this work has focused on the applicability of Artificial Neural Network (ANN) models with back propagation learning in predicting the future values of CFPI. The monthly data for rural, urban and combined from the period 2013 to 2021 have been used to train and validate the models. The Mean Absolute Percentage Error (MAPE) values have been used to validate the accuracy of the models. The experimental results show that a simple ANN model with back propagation algorithm is highly capable in forecasting the future values of CFPI. 2021 IEEE. -
Image Analysis of MRI-based Brain Tumor Classification and Segmentation using BSA and RELM Networks
Brain tumor segmentation plays a crucial role in medical image analysis. Brain tumor patients considerably benefit from early discovery due to the increased likelihood of a successful outcome from therapy. Due to the sheer volume of MRI images generated in everyday clinical practice, manually isolating brain tumors for cancer diagnosis is a challenging task. Automatic segmentation of images of brain tumors is essential. This system aimed to synthesize previous methods for BSA-RELM-based brain tumor segmentation. The proposed methodology rests on four fundamental pillars: preprocessing, segmentation, feature extraction, and model training. Filtering, scaling, boosting contrast, and sharpening are all examples of preprocessing techniques. When doing segmentation, a clustering technique based on Fuzzy Clustering Means (FCM) is used to breakdown the overall dataset into numerous subsets. The proposed approach used the region of filling for feature extraction. After that, a BSA-RELM is used to train the models with the input features. The proposed technique outperforms BSA and RELM, two of the most common alternatives. There was a 98.61 percent success rate with the recommended method. 2023 IEEE. -
An Analysis of Manufacturing Machine Failures and Optimization Using Replacement Year Prediction
The manufacturing industry is highly susceptible to equipment failures, leading to costly downtime, production delays, and increased maintenance expenses. Effective maintenance planning and resource allocation depend on the early detection of possible faults and the precise forecasting of replacement years. The fundamental technique for assuring operational resilience, limiting disruptions, and improving preventative maintenance processes is manufacturing failure analysis. It entails the methodical analysis of failures and spans several sectors, including the automobile, aerospace, electronics, and heavy machinery. In this research, an integrated methodology for predicting replacement years in the manufacturing industry using operations research approaches and the Python-based machine learning algorithm Random Forest Classifier (RFC) is proposed. The program first calculates the total failure rate after importing manufacturing data from a dataset. The failure rate for each manufacturing line is then determined, and the lines with a high failure rate are identified. The program uses machine learning to improve the analysis by teaching a Random Forest classifier to anticipate failures. The model's performance is assessed by measuring the accuracy of a test set. To determine machine replacement years, it also incorporates replacement theory assumptions. Based on the company's founding year and the current year, it determines the replacement year considering the machine's lifespan. This program's advantages include recognizing production lines with high failure rates, employing machine learning to forecast problems, and offering suggestions on when to replace machines. Manufacturers may enhance their processes, lower failure rates, and increase overall efficiency by utilizing statistical analysis, machine learning, andoptimizationstrategies. As technology advances, the field of failure analysis will continue to evolve, enabling firms to achieve improvements. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
The Interplay Between Artificial Intelligence and Operations Management
Artificial intelligence incorporated into machines utilizes capacities to work and replaces people. Knowledge is the method of assembling empowered machines that can mirror human tasks as unique. With digitalization, organizations are remodifying their enterprises and making new business possibilities. Because of AI innovation, organizations change their dynamic cycles and systems and everyday tasks to accomplish the upper hand. An impressive development in the utilization of AI for activities, the board, is fully intent on observing answers for issues that are expanding in intricacy and scale. With the turn of growth and advancement of data innovation, competitiveness has become increasingly more in-depth worldwide. The AI has its own explanations behind examining and settling the sorts of issues that prompted a critical measure of exploration along with the conventional operational research discipline. Many organizations have estimated the fate of operations management. This paper lines with a descriptive research study highlighting the importance of process operations. This paper gives insights about AI technology into operations management and suggests selecting process technology and deploying AI into operations management. 2023 American Institute of Physics Inc.. All rights reserved. -
Experimental scrutinization on production of biogas from vegetable and animal waste
Anaerobic fermentation is a highly promising technology for converting biomass waste into methane, which may directly be used as an energy source. The objective of this research was to investigate production rate of biogas from camel dung, chicken dropping and vegetable waste. Attempts have been made in this study to optimize various parameters in order to determine the most favorable conditions for maximum biogas production from three different types of wastes such as camel dung (CAD), chicken droppings (CHD) and vegetable waste (VW). The amount of biogas produced from the wastes is compared as: VW >CHD>CAD. The results showed that biogas produced from VW is 720 ml in 32 days as compared to CHD and CAD which are 600 ml in 36 days and 80 ml in 40 days respectively. The effect of the pH and temperature on the amount of biogas produced was also studied. The experiments were conducted in temperatures ranging from 36 C to 44 C. 2023 Author(s). -
Studies on design and simulation of di-methyl ether plant of 160 TPD capacity
Energy is needed to run almost everything we see around us, from cars to the electricity power generation plants and everything else. Fossil fuels produce harmful products on combustion. One such eco-friendly alternative is Di-Methyl Ether also known as DME. In this study, a DME plant of capacity 160 tons per day was designed. Methanol dehydration process has been adapted as the process of production. The purity of DME from this plant is 99.5% by weight. Distillation column T-202 and heat exchanger E-208 were designed in detail. The detail design results showed that T-202 needed 6 stages for separation and a column diameter of 1.28m, while the simulation results showed 7 stages and column diameter as 1.285m for the same separation process. Furthermore, E-208 is of type 1-2 shell & tube heat exchanger with 307 tubes and tube length of 5.5m, however the Aspen EDR simulation results were also in close agreement with 304 tubes and 5m length for the same heat exchanger. This paper presents the results of simulation results of the simulation of these equipment's and full plant done using Aspen plus V8.8 software and Aspen EDR. 2023 Author(s). -
Controlling Node Failure Localization in Data Networks Using Probing Mechanisms
In this paper, we prospect the potency of node failure localization in network communication from dual states (normal/fizzle) of the source to destination paths. To localize the failure nodes individually in the scheduled nodes, dissimilar path states must connect with various events of failure nodes. But, this situation is inapplicable or not easier to investigate or apply on enormous networks due to the obligation of any viable failure nodes. This objective is to deploy the set of adequate conditions for recognizing a set of failures in a set of arbitrary nodes which can be verified in a stipulated time. To avoid the above situation, probing mechanisms are assimilated additionally as a combination for network topology and locations of scrutinizes. Three probing mechanisms are considering which vary depending on measurement paths. Both the procedures can be transformed into single-node possessions by which they can be calculated effectively based on the given conditions. The exceeding measures are proposed for measuring the potency of failure localization which can be utilized for assessing the effect of different factors, which comprises topology, total monitors, and probing mechanisms. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Character recognition for Malayalam palm leaf manuscripts: An overview of techniques and challenges
Kerala is a small, ocean-facing state in South India and has been home to several ancient civilizations in the past. The yesteryears have rewarded the state with great cultural heritage, monuments, historic artifacts and the like. Palm leaf manuscript is one such antiquity. Before paper became common, palm leaf was the medium for writing in Kerala. Such manuscripts capture the glory of our past and deals with different domains such as arts, astrology, medicine, science, religion and spirituality. Palm leaf manuscripts have value both as a cultural asset and as a knowledge repository. Palm leaf manuscripts are organic and degrades with age. The environmental conditions can also accelerate its degradation. A viable solution in preserving the knowledge contained in these manuscripts is Handwritten Character Recognition (HCR). Digitized manuscripts have infinite life. Character recognition in Indian languages, including Malayalam, is considered a complex process mainly due to the size of character set, the similarity of characters and the presence of compound characters. This paper surveys existing works in the field of HCR relevant to Malayalam palm leaf manuscripts. 2023 Author(s). -
Traffic Optimization and Route Detection Based on Air Quality and Pollution Level
This research outlines the development of a groundbreaking Traffic Optimization and Route Detection system based on pollution and air quality. Urbanization has led to increased vehicular traffic, exacerbating concerns about air pollution and its adverse effects on public health. The proposed system aims to address this critical issue by integrating real-time environmental data into route recommendations, prioritizing routes that minimize exposure to high-pollution areas. Beyond improving air quality, the system promotes the health and well-being of commuters, encourages the adoption of eco-friendly transportation modes, and contributes to overall environmental sustainability. An air quality detection system is developed to gather data for the development of the system. This innovative approach aligns with the goals of efficient urban mobility, sustainable transportation, and data-driven decision-making. Through this research, we anticipate providing valuable insights into the potential impact of integrating pollution and air quality considerations into urban transportation systems, ultimately contributing to healthier and more sustainable urban environments. 2024 IEEE. -
GUI-Based Percentage Analysis forCuring Breast Cancer Survivors
The modeling approach is increasing the intensity of research in all the domains. The present paper deals with predictive modeling and probabilities. Data analysis is a technique used to transform, reconstruct, and revise some information that is an essential step to achieve the goal or the end result. The present study involves the usage of logistic regression technique for data analysis. Various domain-specific methods pertaining to science, business, etc., are available for data analysis which plays a key role in decision-making and model building. The significance of this analysis is to get the percentage of the survival of patients with advanced breast cancer. 2020, Springer Nature Singapore Pte Ltd. -
The Latest Technology and its Integration for the Development of Healthcare(Medical)
Healthcare advances that use Artificial Intelligence (AI) to analyze data, use devices, and identify patients offer new possibilities for better patient care, cutting costs, and growing the medical sector. The age of specialized human health tests has begun. It uses noninvasive instruments, sound, visual the use of photography, electronic health tools, embedded health instruments, fluidic diagnostic tracking, and combined data analysis to provide people with tailored medical suggestions. These technologies contribute to early identification and comprehending of health issues linked to chronic illnesses and general health using information analysis and AI-driven ideas. Notable uses include a Parkinson's and Huntington's Under certain circumstances, diabetes, cancer, kidney disease, heart problems, elderly care, and a number of healthcare areas. Industry changes are expected as a result of the latest breakthroughs in outdoor monitors, AI-driven evaluation of data, and healthcare testing technologies. AI systems give data to people and health workers, possibly better their way of life and cutting healthcare costs. These include: tracking the effectiveness of medicines, finding chronic illnesses early, and offering individualized care using medical trends and DNA. In relation to healthcare studies and sensor tracking, this study explains new technologies and advances in diverse fusion methods, materials, and processes. Precise diagnostic info, small merchandise dimensions, and cost are high considerations. Healthcare workers, patients, consumers all benefit from more personal health care services thanks to the merging of AI with information streams. The text highlights both advantages and hurdles while showing the way toward upcoming displays and academic papers that follow a path of growth in the industry. 2024 IEEE. -
Blurred Image Processing and IoT Action Recognition in Academy Training Sport
Smart wearable technologies utilising devices connected to the web (IoT) are on the rise, and many of these new applications involve the identification of athletic performance. Many people across the world participate in soccer, also called football in some regions. Soccer players practise discrete actions (like shooting and passing) in order to ingrain them in muscle memory and speed up their reflexes during actual games. There is always a compromise between blur and noise when processing images. Denoising naturally softens an image because noise is high-frequency information. Deblurring, on the other hand, causes additional noise in the final product. The need to brighten an image in low-light conditions only adds to the difficulty. Noise is introduced into the image during the brightening process itself. Images taken while moving, especially those of wildlife (though not exclusively), will have more blur than those taken while still. Many previous projects have focused on a single problem, but very few have attempted to address the entire set of problems simultaneously. So, we set out to make a way to turn these lowlight, fuzzy images into high-contrast, clear images. A fuzzy invariant space is the result of the union of several fuzzy invariant spaces. After numerous iterations of processing a blurred image, the final stage is to utilise a progressive restoration procedure. The experimental findings demonstrate the effectiveness of the suggested technique in reducing calculation error, improving the recovery effect, and avoiding the noise caused by numerous deconvolutions. This work introduces new concepts and methods for recognition research by applying fuzzy image processing to the study being human mobility and the detection of activities in the realm of IoT. Using the Kinect, an IoT somatosensory camera, we are able to collect 15 3D skeletal elements via its software development kit (SDK). This led to the study of kinesiology and the creation of a motion resolution model that works well with the Internet of Things. 2022 IEEE. -
Future Innovation in Healthcare by Spatial Computing using ProjectDR
Spatial Computation is the next step in the continuing convergence between the digital and physical realms. It is a set of inventions and developments that can better our lives through learning the real world, acknowledging and connecting our connection to, and traveling through various locations in the world. The lack of modern, precise, and effective diagnosis limits the rehabilitation of patients, despite technical advancements in medicines. The capabilities of spatial computing are expanded in a healthcare framework during the care and treatment of the patient. In this article, our purpose is to clarify the function of ProjectDR in the field of healthcare, which enables the display of medical images, such as CT scans and MRI results, directly on the patient's body in a manner that moves as patients do. 2021 IEEE. -
Systematic Literature Review on Industry Revolution 4.0 to Enhance Supply Chain Operation Performance
Industry 4.0 is a notion in which industries automate systems and processes, innovate digitally, and share information. It aims to obtain a smart factory in an attempt to lessen required time in responding to consumer demand or unexpected circumstances and to enhance organizational productivity. The integration of Industry 4.0 and supply chain management (SCM) ensures immense development opportunities for manufacturing firms. This article provides a systematic literature review and formulation of the existing research on Industry 4.0 in SCM, resulting in some intriguing analyses that will be useful to academics and industry, particularly top managers. The content of the article is classified into three categories: exploratory vs. confirmatory, qualitative vs. quantitative, and management level vs. technology level. The findings will benefit managers in understanding the significance of Industry 4.0 and its relationship with SCM. The formation of clusters and their affiliations has resulted in the emergence of new areas requiring managerial attention. The article concludes by examining the possibilities of the present and future research. 2022 ACM. -
AI and Machine Learning Enabled Software Defined Networks
The telecommunications industry has not been exempt from the technology sectors massive artificial intelligence (AI) and machine learning (ML) boom in recent years. Artificial intelligence (AI) and machine learning (ML) provide advanced analytics and automation that are in line with modern networking concepts like software-defined networking (SDN) and software-defined wide-area networks (SD-WAN). Work is being done to determine how AI/ML can benefit SD-WAN and to demonstrate these benefits in a real SD-WAN network using a workable example. Modern ML techniques and algorithms are the extent of AI/ML. Todays Internet is under constant threat from DDoS (Distributed Denial of Service) attacks. As the volume of Internet traffic grows, its getting harder and harder to tell whats legitimate and whats malicious. The DDoS attack was detected using a machine learning approach that makes use of a Random Forest classifier. To better detect DDoS attacks, we tweak the Random Forest algorithm. The proposed machine learning approach outperforms, as demonstrated by our results. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Person re-identification using part based hybrid descriptor
Real time person re-identification systems require robust descriptors for useful feature extraction. This paper focuses on a novel descriptor which can efficiently re-identify persons in varied views and change in illumination. The descriptors detect the features by dividing the person image into multiple parts. We use a combination of local and global feature descriptors to form a reliable descriptor. Performance evaluation is done on a benchmarking dataset. 2016 IEEE. -
Smart Precision Irrigation Techniques Using Wireless Underground Sensors in Wireless Sensors
The term brilliant accuracy agribusiness alludes to advances like the Internet of Things, remote sensors, and artificial reasoning on the ranch. A definitive objective of this paper is to expand the quality and amount of the harvests while advancing the human work utilized, use of water system, and diminishing the water system times in light of climate forecast. This proposed framework utilizes the three kinds of sensors like Moisture sensor is used to detect the dirt dampness, the Humidity sensor is utilized to return how much water is present in the encompassing air, Temperature sensors are utilized to give the temperature of the dirt. Each of the three qualities is passed to the remote sensor hub and moved to the information lumberjacks. At long last, the information lumberjack will send the message like beginning the water system to a programmed water lock framework. When all water measures arrive at the water system limit, the programmed water lock framework will be shut. By using the underground, remote sensors, we can track the conditions of various agricultural applications such as soil properties, seed varieties and monitor the environmental situations. Every gadget contains significant gear like sensors, memory, a processor, and a power source. 2022 IEEE. -
Smart Satellites: Unveiling the Power of Artificial Intelligence in Space Communication-A Study
The incorporation of Artificial Intelligence (AI) into space and satellite communication represents a paradigm shift in the way we explore, navigate, and communicate beyond our planet. This article is about the impact of AI on satellite operations, and the broader field of its communication to the earth. The article explores how AI enhances spacecraft autonomy, mitigates signal degradation, and improves the overall reliability of communication performance Satellite communication benefits from AI-driven advancements, in the areas of signal processing and optimization. Furthermore, examines the integration of AI in space-based challenges and opportunities associated with large-scale satellite networks. AI playing a crucial role in detecting and mitigating cyber security threats in space communication systems. This paper comes up with the perception into the future trends and potential advancements in AI applications for space and satellite communication. 2024 IEEE.