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Exploring Machine Learning Models to Predict the Diamond Price: A Data Mining Utility Using Weka
In contrast to gold and platinum, whose values may be fairly determined, determining a diamond's worth involves a far more complex set of considerations. The appropriate rate is based on many factors, not just one of the stones. Diamonds are graded based on their appearance, carat weight, cut quality, and how well they have presented dimensions like a table's surface, depth, and breadth. In order to accurately forecast diamond prices, this study seeks to develop the most effective approaches possible. Different machine learning classifiers are trained on the diamond dataset to forecast diamond prices based on the features. This article shows how to analyze diamond prices using WEKA's data mining software. Diamond data have been utilized for this study. These methods include M5P, Random Forest, Multilayer perceptron, Decision Stump, REP Trees, and M5Rules. For the purpose of estimating the cost of a diamond, different Machine Learning classifiers are compared and contrasted. Performance measures and analysis showed that Random Forest was the best-performing classifier. Experimental findings show, as shown by the coefficient of correlation that Random Forest is better than other classification methods. 2023 IEEE. -
Evolutionary algorithm based feature extraction for enhanced recommendations
A major challenge to Collaborative Filtering systems is high dimensional and sparse data which they have to deal with. Feature selection techniques partly address this problem by reducing the feature space and retaining only a representative subset of features. However these techniques do not address the sparsity problem which affects both quality and quantity of recommendations. A more promising direction would be to construct/extract new features which are low dimensional, dense and have more discriminative power. Content based construction of features has been explored in the past. This work proposes a evolutionary algorithm based feature extraction techniques which discover hidden features with high discriminative capacity. Such an approach offers the advantage of discovering features even in the absence of additional information such as item contents etc. The proposed approach is contrasted with content based feature extraction techniques through experiments and the ability of the new approach in discovering interesting and useful features is established. -
User profiling based on keyword clusters for improved recommendations
Recommender Systems (RS) have risen in popularity over the years, and their ability to ease decision-making for the user in various domains has made them ubiquitous. However, the sparsity of data continues to be one of the biggest shortcomings of the suggestions offered. Recommendation algorithms typically model user preferences in the form of a profile, which is then used to match user preferences to items of their interest. Consequently, the quality of recommendations is directly related to the level of detail contained in these profiles. Several attempts at enriching the user profiles leveraging both user preference data and item content details have been explored in the past. We propose a method of constructing a user profile, specifically for the movie domain, based on user preference for keyword clusters, which indirectly captures preferences for various narrative styles. These profiles are then utilized to perform both content-based (CB) filtering as well as collaborative filtering (CF). The proposed approach scores over the direct keyword-matching, genre-based user profiling and the traditional CF methods under sparse data scenarios as established by various experiments. It has the advantage of a compact user model representation, while at the same time capturing the essence of the styles or genres preferred by the user. The identification of implicit genres is captured effectively through clustering without requiring labeled data for training. 2014 Springer International Publishing Switzerland. -
Recurrent Neural Networks in Predicting the Popularity of Online Social Networks Content: A Review
An online social network is a web platform that individuals use to make social relationships with people who share similar interests, activities, connections, and backgrounds. All online social networks differ in the number of features they provide and their format. In recent years, drastic growth has been seen in the users of online social networks like Flickr, Instagram, Pinterest, Twitter, etc. Among all the features of online social networks, content sharing is the one being widely used by individual users and large organizations. Due to this, content popularity prediction has been extensively studied nowadays, considering various aspects related to it. The study throws light on the use of machine learning techniques in this field. Various algorithms have been used to handle popularity prediction, including classification, regression, and clustering techniques. It is feasible to extract the essential information from such content using machine learning algorithms and utilize the retrieved information in a variety of ways, the majority of which are commercial in nature. The goal of this study is to review and analyze various recurrent neural network (RNN) approaches for predicting the popularity of social media content. The Electrochemical Society -
Popularity Prediction of Online Social Media Content: A Bibliometric Analysis
An online social network is a platform that enables individuals to interact with others who have similar backgrounds, preferences, activities, and associations. The number of features available and the format of each online social network range widely. Users of online social networks, such as Twitter, Instagram, Flicker, and Pinterest, have increased dramatically in recent years. Content sharing is the most popular feature of online social networks, used by both specific users and big enterprises. This study has used bibliometric methods to analyze the growth of the social media popularity prediction on online social network content from 2013 to 2022. The publications have been extracted from the dimensions database, and the VOS viewer software was used to visualize research patterns. The finding provides valuable information on the publication year, authors, authors country, authors organizational affiliations, publishing journals, etc. Based on the findings of this analysis, researchers will be able to design their studies better and add more insights into their empirical studies. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
K-shell jump ratio and jump factor of 3d elements
Employing a simple 2?-geometrical configuration method, K-shell absorption jump ratio and jump factor have been estimated in a few 3d elements viz. Co, Ni, Cu and Zn. The target elements in the form of thin foils were excited using 32.86 keV K X-ray photons from a weak137Cs radioactive source. The emitted K X-rays were detected using a low energy HPGe X-ray detector spectrometerand the K X-ray production cross-section and K X-ray intensity ratios for all the target elements were measured. Then, using the measured data, the K-shell absorption jump factor and jump ratios have been evaluated. The obtained results agree within the experimental uncertainties with previous values reported in the literature. 2018 Author(s). -
A critical study on acetylene as an alternative fuel for transportation
With the traditional power sector hindered by fuel shortage and climate changes, the promotion of green energy becomes the most prioritized objective of the government. The ministry's move becomes significant because conversion to cleaner energy sources is the best way to minimize global warming and to reenergize the global economy. Among the available alternative gaseous fuels, acetylene caters to these needs because of its property similarities with hydrogen. In this research, the suitability of acetylene as an engine fuel is analyzed. Also, the production methods, combustion properties, abnormal combustion, and safety issues were discussed. This review paper describes about the various possible modes of fuel induction techniques to be adopted. The research establishes the utility of acetylene as a commercial fuel for internal combustion engines in the future years by the adoption of suitable methodologies. 2021 Author(s). -
Low cost energy management for demand side integration
Numerous batteries are outfitted with a state ofcharge (SoC) demonstrating the relaxation about the charge. Building a beneficial BMS is to check while thinking about, to that amount regardless we work now not holds a reliable method in imitation of study condition state of-charge, the nearly imperative measurement concerning a battery. Perusing the relaxation about the vigour of a battery is more unpredictable than administering thin fuel as in automobiles. An electrochemical cell diminishes its greatness and the in-and-streaming coulombs are counted for SOC. The BMS together which offers commitment while charging yet releasing; that detaches the battery if the SOC is below certain percentage. Various laws like Peukerts Law for battery capacity have been employed to determine the discharge rate of the battery. Arduino Uno is used for the input parameters required for Peukerts Law and various other calculations for significant monitoring. To address the existing complicated BMS, a new approach has been provided using IoT platform and making the understanding of BMS in much similar perspective. 2018 IEEE. -
Security Intensification using Blockchain coupled with Internet of Things: Proposal, Challenges and Anatomization
Internet of things is an important part of our day-to-day life where all things are connected in the network with the internet. The number of devices linked to the network grows steadily each day in recent years. The innovation in the manufacturing industry also the reason for the production of different devices that uses various technologies to make a possible connection between the devices. Even though the Internet of Things has been developing and demonstrating its potential in recent years, its security when connected to the internet is in doubt. Blockchain is a disruptive technology that provides security to their network without tampering with the data in the network. Researchers and experts have recommended using the blockchain to address security vulnerabilities in the Internet of Things. In this paper, we have analyzed some of the issues which are occurring while integrating blockchain into the Internet of things. The major issues were discussed and which will be helpful to move towards the research direction to solve those problems. 2022 IEEE. -
An IoT-Based Model for Pothole Detection
Maintenance of the good roads plays a very important role in the growth of the country. Poorly maintained roads can lead to potholes which causes severe accidents. To overcome the damage caused by poor roads, the pothole detection model has been proposed in this paper. In recent days, the Internet of Things (IoT)-embedded models are developed in different applications. The main objective of the proposed work is to design the IoT prototype to collect data which can be used to detect potholes and humps. This prototype is embedded with three sensors, namely accelerometer, ultrasonic sensor, and GPS. The data from these sensors is collected by the controller and transmitted by Wi-Fi module to store in the cloud. The collected data can be downloaded as a spreadsheet from the cloud and can be used for different data analysis applications like pothole notifier application. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Building an Industry Standard Novel Language Model Using Named Entities
In every Industry, there is a significant amount of text used in their specific domains. As these are less prevalent in the testing set, anticipating entity names in a language model is a problem faced by the entire industry. In this research a unique and very effective strategy for creating exclusionary classification models that could map entity names based on entity type information is provided. A group of benchmark datasets based on Mortgage is presented, which we used to test the below-presented model. According to experimental findings, our model achieves a perplexity level that is 64% higher than that of the most advanced language models. 2022 IEEE. -
Machine Learning Techniques for Resource-Constrained Devices in IoT Applications with CP-ABE Scheme
Ciphertext-policy attribute-based encryption (CP-ABE) is one of the promising schemes which provides security and fine-grain access control for outsourced data. The emergence of cloud computing allows many organizations to store their data, even sensitive data, in cloud storage. This raises the concern of security and access control of stored data in a third-party service provider. To solve this problem, CP-ABE can be used. CP-ABE cannot only be used in cloud computing but can also be used in other areas such as machine learning (ML) and the Internet of things (IoT). In this paper, the main focus is discussing the use of the CP-ABE scheme in different areas mainly ML and IoT. In ML, data sets are trained, and they can be used for decision-making in the CP-ABE scheme in several scenarios. IoT devices are mostly resource-constrained and has to process huge amounts of data so these kinds of resource-constrained devices cannot use the CP-ABE scheme. So, some solutions for these problems are discussed in this paper. Two security schemes used in resource-constrained devices are discussed. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
A Machine Learning Model for Augmenting the Media Accessibility for the Disabled People
In an era characterized by the proliferation of digital media, the need to efficiently use multimedia content has become paramount. This article discusses an innovative technique called 'Fast Captioning (FC)' to improve media accessibility, especially for people with disabilities and others with time restrictions. Modern Machine Learning (ML) algorithms are incorporated into the framework, which speeds up video consumption while maintaining content coherence. The procedure includes extracting complex features like Word2Vec embeddings, part-of-speech tags, named entities, and syntactic relationships. Using annotated data, a ML model is trained to forecast semantic similarity scores between words and frames. The predicted scores seamlessly integrate into equations that calculate similarity, thus enhancing content comprehension. Through this all-encompassing approach, the article offers a comprehensive solution that balances the requirements of contemporary media with the accessibility requirements of people with disabilities, producing a more inclusive digital environment. Machine Learning-based Media Augmentation (ML-MA) has achieved the highest accuracy of 96%, and the captioning is accurate. 2023 IEEE. -
Malicious URL Detection Using Machine Learning Techniques
Cyber security is a very important requirement for users. With the rise in Internet usage in recent years, cyber security has become a serious concern for computer systems. When a user accesses a malicious Web site, it initiates a malicious behavior that has been pre-programmed. As a result, there are numerous methods for locating potentially hazardous URLs on the Internet. Traditionally, detection was based heavily on the usage of blacklists. Blacklists, on the other hand, are not exhaustive and cannot detect newly created harmful URLs. Recently, machine learning methods have received a lot of importance as a way to improve the majority of malicious URL detectors. The main goal of this research is to compile a list of significant features that can be utilized to detect and classify the majority of malicious URLs. To increase the effectiveness of classifiers for detecting malicious URLs, this study recommends utilizing host-based and lexical aspects of the URLs. Malicious and benign URLs were classified using machine learning classifiers such as AdaBoost and Random Forest algorithms. The experiment shows that Random Forest performs really well when checked using voting classifier on AdaBoost and Random Forest Algorithms. The Random Forest achieves about 99% accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
ChIPSeq Analysis with Bayesian Machine Learning
ChIP-sequencing, otherwise called ChIP-seq, is a technique used to identify protein co-operations with DNA. A crucial advancement to the field of bio-informatics, ChIP sequencing is conducted in research labs around the world to get a better understanding of the way transcription factors and other associated proteins influence the gene in many biological processes and in tackling disease states. ChIP-seq is predominantly a field under the domain of biotechnology, however recent advancements and development of tools to process ChIP data have turned the study into one involving bio-informatics, allowing computer scientists and lab technicians to work on an otherwise scholarly field of biochemistry, molecular biology, microbiology and biomedicine. This report illustrates the predominant work-flow undertaken to sequence chromatin from a cell and to gain insights on the gene/protein of interest. Another aspect added is to use Machine Learning with Bayesian statistical techniques for Peak Calling. The different stages enumerated in this paper have been completed either with the R language or on a Web Server titled Galaxy.org. 2019 IEEE. -
Harnessing the Power of Simulation Games for Effective Teaching in Business Schools
This research delves into the effectiveness of simulation games, in business education specifically focusing on how they improve decision making skills, critical thinking, real world business applications, student engagement and problem-solving abilities. While simulation games are widely recognized as cutting edge tools that provide learning experiences beyond traditional methods there remains a gap in empirical research assessing their overall impact on educational outcomes. Using a combination of analysis and qualitative case studies this study seeks to address this gap by examining how simulation games influence factors in business education. The methodology involves using a one-way ANOVA to compare learning outcomes across business disciplines and conducting detailed case studies for context. The results reveal effects of integrating simulation games into curricula on the mentioned learning outcomes. These findings highlight the importance of incorporating simulation games into business education to enhance students learning experiences effectively. By offering insights on optimizing and tailoring the use of simulation games in education settings this study contributes to improving teaching practices in business schools and encourages research into the interaction, between educational technology and learning efficacy. 2024 IEEE. -
Advancements in Sybil Attack Detection: A Comprehensive Survey of Machine Learning-Based Approaches in Wireless Sensor Networks
Wireless Sensor Networks (WSNs) are used in various healthcare and military surveillance applications. As more sensitive data is transmitted across the network, achieving security becomes critical. Ensuring security is also challenging because most sensors are deployed in remote areas, making them vulnerable to many security attacks. Sybil attacks are one of the most destructive attacks. Security against Sybil attackers can be attained by implementing effective detection techniques to distinguish attackers from genuine nodes. This paper reviews existing machine learning-based approaches for detecting Sybil attacks, and their performance is compared based on different parameters. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Human-Computer Interaction: Innovations and Challenges in Virtual Reality
In an effort to shed light on the advances and difficulties that are shaping the area of Virtual Reality (VR), this research paper digs into the ever-evolving world of Human-Computer Interaction (HCI) within the context of VR. We have found important insights with theoretical and practical applications via a careful research technique comprising mathematical modelling, data collecting, and empirical analysis. Through our investigation of new technologies, we have shown the revolutionary potential of haptic feedback systems in VR settings. Our results, backed by a solid mathematical model, provide light on the measurable effect of haptic feedback and suggest it has the potential to radically alter user experiences in fields as diverse as gaming, instruction, and treatment. At the same time, we have overcome a number of obstacles inherent to virtual reality human-computer interaction, including motion sickness. Our mathematical model of motion sickness and its treatment lays the groundwork for creating VR experiences that are both enjoyable and safe for a wider range of users. This study highlights the ethical implications of VR HCI, highlighting the importance of responsible development and deployment in addition to advances and problems. To make sure that the advantages of this gamechanging technology are used in a responsible manner, we discuss issues like privacy, informed consent, and the possibility for addiction in VR. Our results, as we reach the end of our trip, are both a celebration of potential and a guide for where VR HCI is headed. They motivate more research into inclusive and human-centered design, personalized motion sickness prevention, and cutting-edge haptic feedback technologies. To further lead the development of VR HCI, we advocate for continuous multidisciplinary cooperation and the adoption of thorough ethical rules and regulations. 2024 IEEE. -
On degree product induced signed graphs of graphs
A signed graph is a graph with positive or negative signs assigned to edges. An induced signed graph is a signed graph constructed from a given graph according to some pre-defined protocols. An induced signed graph of a graph G is a signed graph in which each edge uv receives a sign (-1)|?(v)-?(u)|, where ?: V(G) ? ?. In this paper, we discuss degree product induced signed graphs and determine the structural properties of these signed graphs such as balancing, clustering, regularity and co-regularity. 2020 Author(s). -
Wearable Leaf-Shaped Slotted Antenna Including Human Phantom for WBAN Applications
A 5.8 GHz leaf-shaped slotted antenna for Wireless Body Area Network (WBAN) applications is presented in this piece of content. The leaf structure includes tri leaves, having a complete ground plane at the lowest floor and a central circle slot. The suggested antenna is 60 mm by 60 mm by 1.16 mm in total dimensions. The ISM (Industrial Science and Medical) band frequency of 5.8 GHz is covered by this antenna's radiation range of 5.5 to 6.4 GHz. The radiated pattern, efficiency, S11 magnitude and gain were the different attributes of the leaf-patterned slot antenna. The creation of a stylish leaf-shaped antenna that can be incorporated into clothing designs is the main goal of this project. This antenna may be used in difficult situations because of its flexible base and conductive fabric. The method considers the needs of wearable antennas, such as the impact of human interactions on this antenna, as well as the opposite. 2023 IEEE.