bluuebunny/crossref_metadata_2025 huggingface dataset:
Robust Video Watermarking Algorithm based on DCT-SVD approach and Encryption
Authors: Saxena Praful , Kumar Santosh
Published: September 2023

Sharing of digital media content over the internet is increasing everyday .Digital watermarking is a technique used to protect the intellectual property rights of multimedia content owners. In this paper, we propose a robust video watermarking scheme that utilizes Discrete Cosine Transform (DCT) and Singular Value Decomposition (SVD) for embedding a watermark into video frames. The proposed method uses encryption to make the watermark more robust against malicious attacks. The encryption key is used to modify the watermark before it is embedded in the video frames. The modified watermark is then embedded in the DCT and SVD coefficients of the video frames. The experimental results show that the proposed method provides better robustness against various attacks such as compression, noise addition, and filtering, while maintaining good perceptual quality of the watermarked video. The proposed method also shows better resistance against geometric attacks such as cropping, rotation, and scaling. Overall, the proposed method provides an effective solution for protecting the intellectual property rights of multimedia content owners in video distribution and transmission scenarios.
Enhancing Requirements Change Request Categorization and Prioritization in Agile Software Development Using Analytic Hierarchy Process (AHP)
Authors: Asad Kashif , Muqeem Mohd.
Published: September 2023

Software development now relies heavily on agile methods, which call for the efficient administration and prioritization of change requests. In order to improve requirement prioritization using the Analytic Hierarchy Process (AHP) in Agile methods, this study article presents a new framework for classifying software requirements into Small Change Requests (SCRs) and Large Change Requests (LCRs). The paper examines the difficulties associated with requirement prioritization and categorization in Agile settings and offers a methodical system for dividing change requests into categories based on complexity, impact, and timeline. In order to provide a thorough grasp of the project scope and objectives, the framework considers both functional and non-functional needs. A case study containing several Agile software development projects is used to evaluate the performance of the suggested categorization and prioritization model. According to the findings, the combination of SCR and LCR categorization with AHP enables more effective teamwork and greater matching of development goals with partner objectives. The research also shows that the suggested framework's integration into the Agile development process results in a more efficient decision-making process, less time wasted on talks, and improved resource distribution. The model aids in risk mitigation by allowing a methodical and quantifiable approach to requirement prioritization. These risks are related to quick changes in project scope and changing client requirements. By presenting a fresh framework for requirement categorization and prioritization, this study adds to the current discussion on successful requirement management in Agile methods. Agile software development projects become more effective and adaptable overall thanks to the incorporation of AHP, which guarantees a more methodical and objective prioritization process. This study has the potential to greatly improve the administration of shifting needs and user expectations in Agile settings by offering a structured method to classify and rank change requests.
Hybrid Encryption of Cloud Processing With IOT Devices Using DNA And RSA Cryptography
Authors: Agarwal Sohit , Joshi Gagan
Published: September 2023

The research paper titled "Hybrid Data Encryption as well as Decryption Using Hybrid RSA and DNA" develops a hybrid cryptosystem by combining the usefulness of such an asymmetric-key (public-key) cryptosystem with the efficacy of a symmetric-key (private-key) cryptosystem. These two types of cryptosystems use different types of keys. The method addresses concerns regarding the users' right to privacy, authentication, and accuracy by utilizing a data encryption process that is secure in both directions. Both the process of encrypting data and the process of decrypting data, which are both utilized by the system, are two different encryption methods. It has been suggested that a hybrid encryption algorithm, which combines DNA and RSA, be used for file encryption in order to address the issues with efficiency and security. The results of the testing show that the RSA and DNA hybrid encryption algorithm is suitable for use. In this particular research project's hybrid encryption and decoding for cloud processing with IOT devices, the DNA and RSA algorithms were used.
Protecting Children from Harmful Audio Content: Automated Profanity Detection From English Audio in Songs and Social-Media
Authors: Murugan T Senthil , Kalyan V Sai Pavan
Published: September 2023

A novel approach for the automated detection of profanity in English audio songs using machine learning techniques. One of the primary drawbacks of existing systems is only confined to textual data. The proposed method utilizes a combination of feature extraction techniques and machine learning algorithms to identify profanity in audio songs. Specifically, the approach employs the popular feature extraction techniques of Term frequency–inverse document frequency (TF-IDF), Bidirectional Encoder Representations from Transformers (BERT) and Doc2vec to extract relevant features from the audio songs. TF-IDF is used to capture the frequency and importance of each word in the song, while BERT is utilized to extract contextualized representations of words that can capture more nuanced meanings. To capture the semantic meaning of words in audio songs, also explored the use of the Doc2Vec model, which is a neural network-based approach that can extract relevant features from the audio songs. The study utilizes Open Whisper, an open-source machine learning library, to develop and implement the approach. A dataset of English audio songs was used to evaluate the performance of the proposed method. The results showed that both the TF-IDF and BERT models outperformed the Doc2Vec model in terms of accuracy in identifying profanity in English audio songs. The proposed approach has potential applications in identifying profanity in various forms of audio content, including songs, audio clips, social media, reels, and shorts.
Gas Sensor Array Drift in an E-Nose System: A Dataset for Machine Learning Applications
Authors: Kumar Jambi Ratna Raja , Chouksey Prateeksha
Published: September 2023

Gas sensor arrays are widely used in various applications such as environmental monitoring, industrial process control, and medical diagnosis. However, one of the main challenges in using gas sensor arrays is their tendency to drift over time, which can significantly affect their accuracy and reliability. In this research paper, we present a gas sensor array drift dataset that can be used to evaluate and develop drift compensation techniques. The dataset consists of measurements from an array of eight metal oxide gas sensors exposed to six different target gases at varying concentrations over several months. The paper also describes the experimental setup, data acquisition process, and preliminary dataset analysis. Our results show that the sensor array exhibits significant drift over time and that the drift patterns vary depending on the target gas and concentration. This dataset can provide a valuable resource for researchers and engineers working on gas sensor array applications and can help advance the development of more robust and accurate gas sensing systems.
Design of Turbo Trellis Coding Modulation Scheme of Rate 4/9 for Rician Fading Channel
Authors: Goswami Raj Kumar , Kandula Srinivasa Rao , Rao S.V. Rama , Sharief A.H. , Seelam Koteswararao , Melingi Sunil Babu , Nambari Swathi
Published: September 2023

When the fading channels are encountered during data communication, errors are likely to occur at the receiving end due to multipath propagation. Researchers have been consistently striving to develop Error Correction Schemes that can effectively handle these errors and ensure error-free data reception at the receiver end. Of particular interest are the Forward Error Correction Schemes that can be implemented at the transmitter end itself. However, the implementation of error correction coding through these schemes incurs additional costs in terms of bandwidth expansion, as extra bits need to be added to facilitate error correction. Fortunately, there exists one coding scheme called Trellis Coded Modulation (TCM), which addresses this issue. TCM selects a modulation scheme based on the rate of the convolutional coding scheme. However, this coding technique has limitations in correcting the number of errors, leading to the development of Turbo Coding. This scheme utilizes two coders at the transmitter, arranged in either serial or parallel configuration, and a suitable decoder at the receiver. A design of Turbo Coding scheme has been presented in this paper, that employs convolutional coders having rate 2/3, in a serially concatenated configuration, providing an effective rate of 4/9. This turbo coding scheme is then applied to TCM scheme in order to preserve the bandwidth. Therefore, if using the convolutional coding scheme of rate 2/3, the modulation scheme is 8-QAM and in order to preserve bandwidth after coding, using the Turbo coding scheme of rate 4/9, then the modulation scheme will be 512-QAM. The simulations have been conducted in MATLAB and the error correcting capabilities of the designed scheme in comparison with convolutional coding scheme using the constituent convolutional encoder have also been compared. It has been observed that in the Rician fading channel conditions, the Turbo Trellis Coding Modulation Scheme provides approximately 5 dB gain compared to the convolutional coding scheme.
A Novel Self-Organizing Map (SOM) With Data Mining Model for Formulation of Vocational Education Policies
Authors: Yuan Shunqi , Jiang Shihui
Published: September 2023

Data mining is a process of extracting valuable and previously unknown patterns, insights, and knowledge from large datasets. data mining techniques enable the analysis of educational data to inform multi-level vocational education policies that contribute to regional sustainable development. By leveraging the power of data, policymakers can make evidence-based decisions, align vocational education with regional needs, and enhance the effectiveness and relevance of vocational education programs in promoting sustainable development. In this paper proposed an Associative Rule Kohonen SOM (AR-KS) for multi-level vocational educational policies assessment for sustainable development. Initially, the proposed AR-KS model collects relevant data, including information on vocational education programs, student performance records, regional development indicators, and policy documents. This data is then preprocessed to ensure consistency and quality. The next step involves training a Kohonen SOM using the preprocessed data. The SOM forms a topological map where each neuron represents a unique combination of policy variables and regional development factors. This map captures the multidimensional relationships among the variables and provides a visual representation of the policy space. The relationship between the patterns is computed with the associative rule-based training of SOM between variables. The analysis of the AR-KS framework allows policymakers to assess the impact of different vocational education policies on sustainable development goals. Overall, the proposed AR-KS framework offers a novel approach for assessing multi-level vocational educational policies in the context of sustainable development.
IoT-Integrated System for Continuous Assessment of Elementary School Martial Arts Education with Automated Classifier
Authors: Song Yazhou , Mohamed Shapie Mohamad Nizam
Published: September 2023

The IoT system would facilitate the continuous assessment of students' martial arts development by tracking their progress over time. The integration of Internet of Things (IoT) technology in elementary school martial arts education has the potential to revolutionize the learning experience and promote holistic development. This paper proposed an IoT-integrated system for the assessment of elementary school martial arts education. The proposed architecture incorporates an Automated Ranking Parallel Classifier (ARPC) model. The proposed ARPC system incorporates physical health monitoring and assessment, providing real-time data on students' physical activity, heart rate, and other relevant health metrics. The IoT system enables continuous monitoring of students' physical activity, allowing instructors to track their progress, level of engagement, and effort. the system monitors students' physical health parameters, such as heart rate, respiration rate, and sleep patterns, enabling early detection of health issues and supporting overall well-being. The ARPC model comprises the ranking of the features in the IoT data with parallel data processing. With ARPC the activities of the students are monitored and martial arts education in elementary schools is examined. Simulation analysis stated that the ARPC model achieves a higher classification accuracy of 99.73% compared with the conventional state-of-art techniques.
Analysis of Colored Pottery Decoration using Hidden Markov Model Directional Clustering Classification with Deep Learning
Authors: Zhang Pingping
Published: September 2023

Colored pottery decoration is an important cultural artifact that carries significant imagery, symbols, and cultural connotations. This paper presented an in-depth analysis of colored pottery decoration by employing a novel approach, Hidden Markov Model Directional Clustering Classification (HMMDCC), combined with deep learning techniques. The evaluated data comprehensive dataset of colored pottery designs, representing different historical periods and cultural contexts. The imagery, symbols, and cultural connotations embedded in the designs are extracted through a combination of computer vision and image processing techniques. The HMMDCC model is then utilized to perform directional clustering, which identifies spatial relationships and patterns within the decoration elements. To enhance classification accuracy and capture intricate patterns, deep learning techniques are incorporated into the HMMDCC model. The deep learning model is trained on the dataset, enabling it to recognize and classify the imagery, symbols, and cultural connotations present in colored pottery decoration. The findings of this study shed light on the hidden meanings and cultural significance associated with colored pottery decoration. The application of the HMMDCC model with deep learning showcases its effectiveness in analyzing and interpreting complex visual data. The results contribute to a deeper understanding of the historical and cultural contexts in which colored pottery decoration emerged, providing valuable insights for archaeologists, historians, and art enthusiasts.
Feature based-Learning with Data Increasing for video Recommendation and Computing
Authors: Reddy K Ramakrishna , Dhanasekaran B. , Sharma V. K.
Published: September 2023

Image content analysis is crucial for determining the reliability of a link between two videos. Video characteristics are increasingly being used in image and video representation as custom pre-trained picture and video convolutional neural networks become generally available. People also have limited access to video editing tools for a variety of reasons, such as ownership and privacy concerns. You don't need to go back to the source video data to use the refined features again. An affine transformation, for instance, can be used to map a well-studied function onto an unfamiliar domain. To do this, we use a unique triplet failure in conjunction with the re-learning strategy. We propose a contemporary data augmentation method that may be applied to functionality on various frames for videos as an alternative to employing specific motion data. Extensive testing on the well-known Hulu content-based Video Relevance challenge demonstrates the process's efficacy and provides solid evidence of state-of-the-art performance.
Leveraging Big Data Analytics for Cultural Teaching Competence in international Chinese Linguistic Learning using Weighted Random Forest Model
Authors: Guo Cui , Liu Xu
Published: September 2023

The teaching of Chinese language and culture has gained significant importance on the global stage due to China's growing influence in various domains. International Chinese language teachers play a crucial role in promoting cross-cultural understanding and facilitating effective communication between Chinese and non-Chinese speakers. This paper aims to explore the concept of cultural teaching competence for international Chinese language teachers, with a focus on the Chinese national context and the application of a cultural teaching framework supported by big data analytics. The model uses the Integrated Machine Learning Teaching Framework (iMLTF). The model constructs the cultural teaching framework for the evaluation of the International Chinese Language based on Chinese National Context. The iMLTF model uses the Multivariant examination integrated with the Weighted Random Forest model. The simulation analysis expressed that the proposed iMLTF model achieves the higher classification accuracy value of 98% compared with the conventional state-of-art techniques.
Shaping the Future of Animation towards Role of 3D Simulation Technology in Animation Film and Television
Authors: Li Wang , Subri Shafilla Binti , Khalis Faryna Binti Mohd , Lian Bai , Wenhua Liu , Zhongyao Yi
Published: September 2023

The application of 3D simulation technology has revolutionized the field of animation film and television art, providing new possibilities and creative opportunities for visual storytelling. This research aims to explore the various aspects of applying 3D simulation technology in animation film and television art. It examines how 3D simulation technology enhances the creation of realistic characters, environments, and special effects, contributing to immersive and captivating storytelling experiences. The research also investigates the technical aspects of integrating 3D cloud simulation technology into the animation production pipeline, including modeling, texturing, rigging, and animation techniques. This paper explores the application of these optimization algorithms in the context of cloud-based 3D environments, focusing on enhancing the efficiency and performance of 3D simulations. Black Widow and Spider Monkey Optimization can be used to optimize the placement and distribution of 3D assets in cloud storage systems, improving data access and retrieval times. The algorithms can also optimize the scheduling of rendering tasks in cloud-based rendering pipelines, leading to more efficient and cost-effective rendering processes. The integration of 3D cloud environments and optimization algorithms enables real-time optimization and adaptation of 3D simulations. This allows for dynamic adjustments of simulation parameters based on changing conditions, resulting in improved accuracy and responsiveness. Moreover, it explores the impact of 3D cloud simulation technology on the artistic process, examining how it influences the artistic vision, aesthetics, and narrative possibilities in animation film and television. The research findings highlight the advantages and challenges of using 3D simulation technology in animation, shedding light on its potential future developments and its role in shaping the future of animation film and television art.
Deep Learning-Based Big Data Analytics Model Based on Teaching Reforms in Three-Dimensional Composition
Authors: Cao Yuewang , Liu Yafang
Published: September 2023

With the development of online education and big data analysis, new teaching models and methods have emerged. The integration of online and offline teaching modes based on big data analysis has become an effective way to promote teaching reform and practice in the field of three-dimensional composition. It is important to incorporate teaching reform into the teaching of three-dimensional composition to improve the quality of education and better prepare students for their future careers. This paper evaluated the contribution of teaching reform to the improvement of student performance. This paper designed a Deep Learning (DL) big data analytics model for data clustering and classification. The student performance is monitored for both online teaching and offline teaching classes. The collected data is clustered with the directional clustering process for the computation of feature space. With the estimated feature space value Hidden Markov Model (HMM) is implemented for the estimation of statistical data derived from the feature spaces. The extracted data were applied over the RESENT- 50 model for the classification of students’ performance. The data analysis with DL model stated that student performance in offline teaching is more significant than offline teaching in 3-dimensional aspects.
Automated Personalized Big Data Model to Promote Traditional Culture with Aesthetic Education
Authors: Aliu Rongyu , Lertlit Supinda
Published: September 2023

Big data can make significant contributions to the field of aesthetic education in universities. By analyzing large amounts of data, researchers can gain insights into student engagement with artistic content and better understand how students learn and appreciate the arts. Aesthetic education is a field of study that focuses on the cultivation of aesthetic sensibility and appreciation, as well as the development of skills in various forms of artistic expression. Aesthetic education in universities is that it helps to develop students’ emotional intelligence and empathy. Hence, in this paper constructed the automated framework model based on big data is constructed for Aesthetic education in universities. The constructed model is termed the Mamdani Fuzzy Set Optimization (MFsO) for the personalized automated model. The student information associated with aesthetic education in universities is processed with MFsO model. The MFsO model uses the fuzzy set rules for the personalized comments to the students for the promotion of tradition among students. The model uses the Flemingo Optimization model for the computation of the effective features in the big data for the generation of rules. The automated model uses the deep learning architecture model for the data transmission to the students. The comparative analysis stated that the proposed MFsO model performance is effective compared with the conventional techniques for the personalized automated system design.
SWOT Analysis with Novel NimbusTech Cloud Computing based Research on Regional Tourism Poverty Alleviation Development Model
Authors: Zhang Lujie , Omar Shida Irwana Bt
Published: September 2023

Cloud computing has the potential to revolutionize microfinance by providing access to affordable and scalable computing resources. Microfinance institutions (MFIs) can use cloud computing to streamline their operations, reduce costs, and improve their services to clients. Cloud computing can also help MFIs reach new clients by providing a cost-effective platform for deploying and managing new services. Regional tourism can have a significant impact on the local economy, creating jobs and generating income for local businesses. It can also provide opportunities for cultural exchange and contribute to the preservation of natural and cultural heritage sites. A poverty alleviation development model is a framework or approach that is used to reduce poverty in a specific region or community. Effective poverty alleviation development models require collaboration between the government, non-governmental organizations, and local communities. They should also be based on an understanding of the specific needs and priorities of the target population, as well as the broader social, economic, and political context in which they operate. This paper presents a SWOT analysis of a novel cloud computing environment, called NimbusTech, through the lens of a microfinance model for poverty alleviation with a focus on regional tourism. The SWOT analysis examines the strengths, weaknesses, opportunities, and threats of using NimbusTech to support microfinance initiatives aimed at reducing poverty levels in regions that rely on tourism. The analysis highlights that NimbusTech's strengths include its scalability, flexibility, and cost-effectiveness, which make it an ideal platform for implementing microfinance programs. On the other hand, its weaknesses include data security and privacy concerns, which could undermine trust in the platform. The opportunities for NimbusTech include the potential for leveraging big data analytics to improve the effectiveness of microfinance programs, while the threats include competition from other cloud computing platforms and potential regulatory barriers. Overall, the analysis shows that NimbusTech has the potential to support microfinance initiatives and promote regional tourism as a means of reducing poverty levels, but careful attention must be paid to its weaknesses and threats to ensure its success..
Internet of Things (IoT) based Investigation between Instructors' Insight of Constructivist Learning Theory and Learners Performance analysis in Higher Vocational Accounting Training
Authors: Chen Lidan , Lertamornsak Ganratchakan
Published: September 2023

The teaching mode of the higher vocational accounting profession plays a significant role in fostering students' professional knowledge, skills, and competencies. In recent years, constructivist learning theory has become increasingly popular in the field of education due to its emphasis on active student engagement, knowledge construction, and the integration of real-world experiences. This research paper aims to explore the application of constructivist learning theory in the teaching mode of higher vocational accounting profession. The aim of this study is to investigate the relationship between teachers' perception of constructivist learning theory and students' academic performance in higher vocational accounting education with the integration of the Internet of Things (IoT). A weighted approach model with optimization is proposed to quantify the impact of the teachers' perception of constructivist learning theory on the students' academic performance. Data is collected from 300 higher vocational accounting students and 30 accounting teachers using a self-designed questionnaire. The results indicate that the teachers' perception of constructivist learning theory has a significant positive impact on students' academic performance. Moreover, the proposed weighted approach model with optimization shows promising results in accurately measuring the impact of teachers' perception of constructivist learning theory on students' academic performance. This study provides valuable insights for higher vocational accounting education with IoT and suggests that teachers should incorporate constructivist learning theory principles in their teaching practices to improve students' academic performance..
Traffic Path Recommendation Model based on a Weighted Sum of Extracted Parameter
Authors: Khairnar Hitendra Shankarrao , Sonkamble B. A.
Published: September 2023

A path recommendation for vehicular traffic is important task of traffic analysis. It is a challenging problem for researchers to extract all paths and recommend the shortest path between Origin and Destination (OD) pairs. This paper comes up with a model which is established on the weighted sum of selected link references to recommend a path for OD pairs. First, to maintain spatial dependence between link references, a vehicular traffic network of roads is proposed as a rectangular coordinate system. The algorithm based on K-means and smoothing is introduced to select link references across OD pairs. A distance aggregation algorithm is proposed to evaluate all possible paths across an OD pair. Finally, out of overwhelming paths, the algorithm recommends the shortest distance path across an OD pair. Our proposed model effectively selects the link references and gets an overall shortest path recommendation. The proposed model analyzes the non-Euclidean distance of selected link references. Our experimental analysis shows that on an average, the first four link predictions lead to 77.37% distance coverage for the recommended path.
Open Issues, Research Challenges, and Survey on Education Sector in India and Exploring Machine Learning Algorithm to Mitigate These Challenges
Authors: Kannan K. Rajesh , Abarna K. T. Meena , Vairachilai S.
Published: September 2023

The nation's core sector is education. But dealing with problems in educational institutions, particularly in higher education, is a challenging task. The growth of education and technology has led to a number of research challenges that have attracted significant attention as well as a notable increase in the amount of data available in academic databases. Higher education institutions today are worried about outcome-based education and various techniques to assess a student's knowledge level or capacity for learning. In general, there are more contributors in the academic field than there are authors. Research is being done in this field to determine the best algorithm and features that are crucial for predicting the future outcomes. This survey can help educational institutions assess themselves and find any gaps that need to be filled in order to fulfil their purpose and vision. Machine Learning (ML) approaches have been explored to solve the issues as higher education systems have grown in size.
Streamlined Learning with AI-ML: An Integrated Campus Management Platform
Authors: Sahane Prema , Waghmare Anuja , Singh Rahul , Sukale Vaibhav , Pukale Neha
Published: September 2023

An Integrated Campus Management Platform using Artificial Intelligence (AI) & Machine Learning (ML) is a platform that leverages advanced technologies to optimize the management of various operations in a campus environment. This platform can provide solutions for tasks such as student management, facility management, and academic planning among others. By utilizing AI and ML, the system can learn from historical data and adapt to changing conditions, providing real-time insights and analytics that can be used to make informed decisions. This abstract provides an overview of the Integrated Campus Management platform and the benefits it offers in terms of efficiency, accuracy, and scalability. Additionally, it highlights the role of AI and ML in enhancing the platform’s capabilities and improving the overall campus experience for students, faculty, and staff.
Capuchin Search Particle Swarm Optimization (CS-PSO) based Optimized Approach to Improve the QoS Provisioning in Cloud Computing Environment
Authors: Gupta Manila , Gupta Bhumika , Singh Devendra
Published: September 2023

This review introduces the methods for further enhancing resource assignment in distributed computing situations taking into account QoS restrictions. While resource distribution typically affects the quality of service (QoS) of cloud organizations, QoS constraints such as response time, throughput, hold-up time, and makespan are key factors to take into account. The approach makes use of a methodology from the Capuchin Search Particle Large Number Improvement (CS-PSO) apparatus to smooth out resource designation while taking QoS constraints into account. Throughput, reaction time, makespan, holding time, and resource use are just a few of the objectives the approach works on. The method divides the resources in an optimum way using the K-medoids batching scheme. During batching, projects are divided into two-pack assembles, and the resource segment method is enhanced to obtain the optimal configuration. The exploratory association makes use of the JAVA device and the GWA-T-12 Bitbrains dataset for replication. The outrageous worth advancement problem of the multivariable capacity is addressed using the superior calculation. The simulation findings demonstrate that the core (Cloud Molecule Multitude Improvement, CPSO) computation during 500 ages has not reached assembly repeatedly, repeatedly, repeatedly, and repeatedly, respectively.The connection analysis reveals that the developed model outperforms the state-of-the-art approaches. Generally speaking, this approach provides significant areas of strength for a successful procedure for improving resource designation in distributed processing conditions and can be applied to address a variety of resource segment challenges, such as virtual machine setup, work arranging, and resource allocation. Because of this, the capuchin search molecule enhancement algorithm (CSPSO) ensures the success of the improvement measures, such as minimal streamlined polynomial math, rapid consolidation speed, high productivity, and a wide variety of people.
Air Watch: An Ample Design of Indoor Air Quality Monitoring System
Authors: Theresa W. Gracy , Tamilselvi T. , Florence M. Mary Victoria , Revathi K.
Published: September 2023

The environment is getting contaminated drastically by introducing harmful materials into the atmosphere through the excessive activities of human in adding comfort and luxurious style in their living. The pollutant level in air has high impact of healthiness of the person inhaling it. Air not outside as well indoor is infected by various hazardous particles and gases.  To assess the air quality in the particular environment, it stimulates the need to monitor the hazardous elements listed. The internet of things (IoT) and artificial intelligence (AI) became the part of human life by adding smartness in their daily routines from facilitating control over appliances to own health factor as well automate operations. The primary objective of the effort is to identify the gases that cause air pollution, measure the air quality, and assess the level of pollution so that we can determine which gases cause pollution and at what place is the air being impacted. An IoT based indoor air quality monitoring system is built through incorporating carbon monoxide (CO), carbon dioxide (CO2), Ozone (O3), particulate matters (PM) and volatile organic components (VOC) sensors into Arduino board. The design ensures a complete air monitor, extends reliable service at low cost. A rule based system is developed to automate events upon the estimated air quality index (AQI) out of the sensory circuit.
Some Clustering Methods, Algorithms and their Applications
Authors: Velunachiyar S. , Sivakumar K.
Published: September 2023

Clustering is a type of unsupervised learning [15]. When no target values are known, or "supervisors," in an unsupervised learning task, the purpose is to produce training data from the inputs themselves. Data mining and machine learning would be useless without clustering. If you utilize it to categorize your datasets according to their similarities, you'll be able to predict user behavior more accurately. The purpose of this research is to compare and contrast three widely-used data-clustering methods. Clustering techniques include partitioning, hierarchy, density, grid, and fuzzy clustering. Machine learning, data mining, pattern recognition, image analysis, and bioinformatics are just a few of the many fields where clustering is utilized as an analytical technique. In addition to defining the various algorithms, specialized forms of cluster analysis, linking methods, and please offer a review of the clustering techniques used in the big data setting.
A Detailed Study on Aggregation Methods used in Natural Language Interface to Databases (NLIDB)
Authors: Kolarkar Ashlesha , Kumar Sandeep
Published: September 2023

Historically, databases have been the most crucial issue in the study of information systems, and they constitute an essential part of all information management systems. Since, it complicated due to restricting the number of potential users, particularly non-expert database users who must comprehend the database structure to submit such queries. Natural language interface (NLI), the simplest method to retrieve information, is one possibility for interacting with the database. The transformation of a natural language query into a Structured Query (SQL) in a database is known as a "Natural Language Interface to Database" (NLIDB). This study uses NLIDB to handle the works performed under various aggregations with aggregation functions, a grouping phrase, and a possessing clause. This study carefully examines the numerous systematic aggregation approaches utilized in the NLIDB. This review provides extensive information about the many methods, including query-based, pattern-based, general, keyword-based NLIDB, and grammar-based systems, to extract data for a dissertation from a generic module for use in such systems that support query execution utilizing aggregations.
Advance Urban Flood Control System Using Fuzzy Logic and Internet of Things (IoT) for Smart City
Authors: Hingmire Anil M. , R. Bhaladhare Pawan
Published: September 2023

City flood control is a significant concern everywhere due to the constantly changing environment. The modern world needs smart cities with smart infrastructure to manage or control floodwaters. The research objective of this study is to design real time urban flood control methodology, develop the working model and testing the model with result analysis in controlled environment. This research paper proposes a smart water control model based on fuzzy inference system. The research is advancement in the Water Sensitive Storm Water Management System by creating a prototype model and then evaluating it in real-world scenarios using input parameters as rainfall intensity, water flow rate, and water level. The method relies on water catchment flooding data that was collected in real-time using sensors and an autonomous smart controller. The system considers the real-time sensor data from all catchments to make collective decision, which also optimize the use of actuators by conserving the power used by the actuators. In terms of early floodwater control, the recommended approach optimizes the use of actuators with utilizing the existing drainage system. The average water reduction rate at the medium level is 34.8%. At high levels, the average water reduction rate is 61.43%, and at extremely high levels, it 73.63%. A significant reduction of water level achieved in the most inundated area by 73.9 % in high and extreme input parameter value.
Optimal and Efficient Searchable Encryption with Single Trapdoor for Multi-Owner Data Sharing in Federated Cloud Computing
Authors: Veerabhadram Vadlamani , Arul Dalton Gregory
Published: September 2023

Cloud computing, an Internet based computing model, has changed the way of data owners store and manage data. In such environment, data sharing is very important with more efficient data access control. Issuing an aggregate key to users on data enables and authorizes them to search for data of select encrypted files using trapdoor or encrypted keyword. The existing schemes defined for this purpose do have certain limitations. For instance, Cui et al. scheme is elegant but lacks in flexibility in access control in presence of multiple data owners sharing data to users. Its single trapdoor approach needs transformation into individual trapdoors to access data of specific data owner. Moreover, the existing schemes including that of Cui et al. does not support federated cloud.  In this paper we proposed an efficient key aggregate searchable encryption scheme which enables multiple featuressuch as support for truly single aggregate key to access data of many data owners, federated cloud support,query privacy, controlled search process and security against cross-pairing attack. It has algorithms for setup, keygen, encrypt, extract, aggregate, trapdoor, test and federator. In multi-user setting it is designed to serve data owners and users with secure data sharing through key aggregate searchable encryption The proposed scheme supports federated cloud. Experimental results revealed that the proposed scheme is provably secure withrelatively less computational overhead and time complexity when compared with the state of the art.
A CNN and LSTM-based Model for Creating Captions for Photos
Authors: Krishna Thati Bala Murali , Voddi Swathi , Busa Srikanth , Surendra Surendra , Kumar J N V R Swarup , Rao M.V.L.N. Raja
Published: September 2023

Can a machine interpret an image's meaning with the same speed as the human brain when it is seen? This problem was heavily researched by computer vision specialists, who believed it to be unsolvable until recently. It is now possible to develop models that can generate captions for pictures because of advancements in deep learning techniques, accessibility to large datasets, and processing power. This will be accomplished by the Python-based implementation of the article's deep learning convolutional neural network technique and a particular kind of recurrent neural network. Here the proposed model uses CNN and LSTM methods to achieve desired task
Optimized Ensemble Approach for Multi-model Event Detection in Big data
Authors: Swapnika K. , Vasumathi D.
Published: September 2023

Event detection acts an important role among modern society and it is a popular computer process that permits to detect the events automatically. Big data is more useful for the event detection due to large size of data. Multimodal event detection is utilized for the detection of events using heterogeneous types of data. This work aims to perform for classification of diverse events using Optimized Ensemble learning approach. The Multi-modal event data including text, image and audio are sent to the user devices from cloud or server where three models are generated for processing audio, text and image. At first, the text, image and audio data is processed separately. The process of creating a text model includes pre-processing using Imputation of missing values and data normalization. Then the textual feature extraction using integrated N-gram approach. The Generation of text model using Convolutional two directional LSTM (2DCon_LSTM). The steps involved in image model generation are pre-processing using Min-Max Gaussian filtering (MMGF). Image feature extraction using VGG-16 network model and generation of image model using Tweaked auto encoder (TAE) model. The steps involved in audio model generation are pre-processing using Discrete wavelet transform (DWT). Then the audio feature extraction using Hilbert Huang transform (HHT) and Generation of audio model using Attention based convolutional capsule network (Attn_CCNet). The features obtained by the generated models of text, image and audio are fused together by feature ensemble approach. From the fused feature vector, the optimal features are trained through improved battle royal optimization (IBRO) algorithm. A deep learning model called Convolutional duo Gated recurrent unit with auto encoder (C-Duo GRU_AE) is used as a classifier. Finally, different types of events are classified where the global model are then sent to the user devices with high security and offers better decision making process. The proposed methodology achieves better performances are Accuracy (99.93%), F1-score (99.91%), precision (99.93%), Recall (99.93%), processing time (17seconds) and training time (0.05seconds). Performance analysis exceeds several comparable methodologies in precision, recall, accuracy, F1 score, training time, and processing time. This designates that the proposed methodology achieves improved performance than the compared schemes. In addition, the proposed scheme detects the multi-modal events accurately.
Solar Photovoltaic Parameter Extraction for Three Different Technologies Using Particle Swarm Optimization Method
Authors: Kanekar Krupali , Burade Prakash , Magare Diraj
Published: September 2023

Industry and academia are becoming more interested in solar energy. The problem of providing an alternative to fossil fuels and limiting the environmental damage brought on by their emissions is what has brought about this increased focus. Solar photovoltaic is the subject of a growing number of studies. The goal of the current investigation is to investigate how various technological modules namely single junction amorphous silicon (a-Si), Hetero junction with Intrinsic Thin-layer (HIT) and multi crystalline silicon (mc-Si) are affected by seasonal spectrum variation respond to Indian climatic circumstances. Compared to many other nations, the entire Indian subcontinent has a relatively distinct climate with distinct seasonal patterns. The four seasons that are considered in this study are summer, winter, monsoon, and post monsoon. The impact of each season varies on the spectrum. Such a study will be helpful to measure the parameter connected to the spectrum and assess its impact on the effectiveness of the PV array. In order to conduct accurate performance investigations, the extraction of the right circuit model parameters is essential. The estimation of the solar PV parameter is done by using Particle swarm optimization (PSO) algorithm.
GAIT Technology for Human Recognition using CNN
Authors: Macwan Kiran , Shah Harshal
Published: September 2023

Gait is a distinctive biometric characteristic that can be detected from a distance; as a result, it has several uses in social security, forensic identification, and crime prevention. Existing gait identification techniques use a gait template, which makes it difficult to keep temporal information, or a gait sequence, which maintains pointless sequential limitations and loses the ability to portray a gait. Our technique, which is based on this deep set viewpoint, is immune to frame permutations and can seamlessly combine frames from many videos that were taken in various contexts, such as diversified watching, angles, various outfits, or various situations for transporting something. According to experiments, our single-model strategy obtains an average rank-1 accuracy of 96.1% on the CASIA-B gait dataset and an accuracy of 87.9% on the OU-MVLP gait dataset when used under typical walking conditions. Our model also demonstrates a great degree of robustness under numerous challenging circumstances. When carrying bags and wearing a coat while walking, it obtains accuracy on the CASIA-B of 90.8% and 70.3%, respectively, greatly surpassing the best approach currently in use. Additionally, the suggested method achieves a satisfactory level of accuracy even when there are few frames available in the test samples; for instance, it achieves 85.0% on the CASIA-B even with only 7 frames.
Securing IoT Attacks: A Machine Learning Approach for Developing Lightweight Trust-Based Intrusion Detection System
Authors: Burange Anup W. , Deshmukh Vaishali M.
Published: September 2023

The routing process in the Internet of Things (IoT) presents challenges in industrial applications due to its complexity, involving multiple devices, critical decision-making, and accurate data transmission. The complexity further increases with dynamic IoT devices, which creates opportunities for potential intruders to disrupt routing. Traditional security measures are inadequate for IoT devices with limited battery capabilities. Although RPL (Routing Protocol for Low Energy and Lossy Networks) is commonly used for IoT routing, it remains vulnerable to security threats. This study aims to detect and isolate three routing attacks on RPL: Rank, Sybil, and Wormhole. To achieve this, a lightweight trust-based secured routing system is proposed, utilizing machine learning techniques to derive values for devices in new networks, where initial trust values are unavailable. The system demonstrates successful detection and isolation of attacks, achieving an accuracy of 98.59%, precision of 98%, recall of 99%, and f-score of 98%, thereby reinforcing its effectiveness. Attacker nodes are identified and promptly disabled, ensuring a secure routing environment. Validation on a generated dataset further confirms the reliability of the system.