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2024 International Conference on Artificial Intelligence and Emerging Technology, Global AI summit 2024

作者： Fatima, N. sabiyath singh, Ramendra swathi, Akuthota Choudhary, Amar Ahmed, Azhar sharma, Chhaya B.S. Abdur Rahman Crescent Institute of Science & Technology Department of Computer Science and Engineering Chennai India Ghaziabad India Cvr College of Engineering Department of Computer Science and Engineering Mangalpalli Rangareddy India Alliance University Department Electronics and Communication Engineering Bengaluru India Kcc Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Raj Kumar Goel Institute of Technology Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350379716

The present research provides an analysis of the development and implementation of a deep learning-b.s.d diagnosis system for liver cancer. The provided system was developed using a dataset encompassing 2300 sensor readings, including imaging, clinical, and genomic data. A range of deep learning features was implemented to improve the early detection of liver cancer such as CNN, VGG 16, VGG 19, and Inception V3. The data were divided into two categories: one is for training, and another used for testing. The results of the data analysis have shown that VGG 19 provides the highest diagnostic accuracy of 98.76%, with VGG 16, Inception V3 and CNN displaying the levels of 94.5%, 93.4%, and 90.25%, respectively. The Model performance analysis was also augmented at the application of precision, recall, and F1 score indicators, as well as using AUC-ROC values which highlighted VGG 19 as the most effective algorithm. Another method of evaluation was the application of confusion matrices for plotting the results b.s.d on the number of true positive, true negative, false positive, and false negative detections, where all the samples from the dataset were added to each model calculation. The obtained results have shown the importance of multimodal data for the increase of diagnosis accuracy and revealed the promising potential of advanced computation techniques in medical analytics. In such a way, it can be stated that the developed deep learning models contribute to the ability of healthcare specialists in the definition of liver cancer at early stages, which allows timely interventions and facilitates the development of a range of personalized treatment protocols. The application of such algorithms has the potential to reshape liver cancer diagnostics, where major tasks are associated with further improvement and validation of the developed algorithms b.s.d on clinical case studies. © 2024 IEEE.

关键词： Contrastive Learning

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An efficient text-b.s.d image retrieval using natural language processing (nlp) techniques 6th

An efficient text-based image retrieval using natural langua...

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6th International Conference on Information system Design and Intelligent Applications, INDIA 2019

作者： Ashok Kumar, P.M. subha Mastan Rao, T. Arun Raj, L. Pugazhendi, E. Department of Computer Science Engineering K.L. University Vaddeswaram522502 India Department of Computer Science Engineering CMR Technical Campus Hyderabad501401 India Department of Computer Science Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai600048 India Department of Information Technology Anna University MIT Campus Chennai600048 India

ISBN: (纸本)9789811553998

The image retrieval system is a computer system for browsing, searching and retrieving images from a large datab.s. of digital images or text. Most traditional and common methods of image retrieval utilize some method of adding metadata such as captioning, keywords or descriptions to the images so that retrieval can be performed over the annotation words. Manual image annotation is time-consuming, laborious and expensive. To address this, there has been a large amount of research done on automatic image annotation. With the rapid development of information technology, the number of electronic documents and digital content within documents exceeds the capacity of manual control and management. The usage of images is increased in real time. so, the proposed system concentrates on retrieving image by using the text-b.s.d image retrieval system. Text documents are given as input to the preprocessing stage, and features are extracted using TF-IDF. Finally, document clustering method can be used to automatically group the retrieved documents into list of meaningful categories. Document clustering clusters the document of different domains and latent Dirichlet allocation (LDA), each document may be viewed as a mixture of various topics where each document is considered to have a set of topics, and after that relevant documents are retrieved and then images of those relevant documents are retrieved. © springer Nature singapore Pte Ltd 2021.

关键词： K-means clustering

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Notice of Violation of IEEE Publication Principles: A Tool for Identifying Ragas Using MIDI (Musical Instrument Devices) for CMIR (Classical Music Information Retrieval)

Notice of Violation of IEEE Publication Principles: A Tool f...

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IEEE International symposium on Dependable, Autonomic and secure Computing (DAsC)

作者： R. sudha Department of Computer Science and Engineering B.S. Abdur Rahman University Chennai Tamil Nadu India

Notice of Violation of IEEE Publication Principles "A Tool for Identifying Ragas Using MIDI (Musical Instrument Devices) for CMIR (Classical Music Information Retrieval)" by R. sudha, in the Proceedings of the 8th IEEE International Conference on Dependable, Autonomic and secure Computing, December 2009, pp. 837-842 After careful and considered review of the content and authorship of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE's Publication Principles. This paper contains portions of text from the paper(s) cited below. A credit notice is used, but due to the absence of quotation marks or offset text, copied material is not clearly referenced or specifically identified. "Raga Mining of Indian Music by Extracting Arohana-Avarohana Pattern" by surendra shetty and K.K. Achary International Journal of Recent Trends in engineering, Vol 1, No 1, May 2009, pp. 362-366 The main motive behind Ragas identification is that it can be used as good b.s.s for music information retrieval of carnatic music songs or film songs b.s.d on carnatic music. The input polyphonic music signal is analyzed and made to pass through a signal separation algorithm to separate the instrument and the vocal signal. The frequency components of the signal are then determined and we map these frequency in to swara sequence ant thereby determine the Ragas of the particular song.30- 40 a sample of b.s. Ragas from 72 is being identified which will help more for music learners and musicians. still now only vocal type of identification is being carried out and we provide the identification with string type Instruments of 1 or 2 instruments mixture (violin/sitar/both).

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Investigations in single document summarization by extraction method

Investigations in single document summarization by extractio...

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International Conference on Computing, Communication and Networking, ICCCN

作者： shanmugasundaram Hariharan Rengaramanujam srinivasan Research Scholar Faculty of Information Technology B.S. Abdur Rahman Crescent Engineering College Chennai Tamil Nadu India Faculty of Computer Science and Engineering B.S. Abdur Rahman Crescent Engineering College Chennai Tamil Nadu India

Due to the rapid growth of the Internet and online information services, huge information is readily made available online. There is no time for anybody to read everything but critical decisions are required to be made quickly. This has led to large-scale research efforts in text summarization. This paper combines contributions by term frequency weight, position weight and commendation weight to form an effective extraction b.s.d summary. We have analyzed the significance of each feature and we found that a combination of all three weights results in a better summary as close as human generated summary. We also show that our summarizer outperforms commercially available summarizers like Copernic summarizer and Microsoft summarizer.

关键词： Data mining Humans Frequency Information technology computer science Educational institutions Web and internet services Large-scale systems Datab.s.s Information retrieval

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Role of ontology in automatic construction of stories for kids

Role of ontology in automatic construction of stories for ki...

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International Conference on Intelligent Agent & Multi-Agent systems (IAMA)

作者： A. Jaya G.V. Uma Computer Science & Engineering B.S. Abdur Rahman Crescent Engineering College Chennai India Faculty of Computer Science & Engineering Anna University Chennai India

story is a description of a chain of events told or written in prose or verse. It gives an excitement for the people in the world irrespective of their age and it is an interesting way of transferring knowledge from one person to other in the form of narrated sequence of events arranged in a chronological order to convey the message. In the artificial intelligence era, construction of stories by the system has the significant role in natural language process. Constructing the stories automatically by the system is significant in the artificial intelligence era. Most of the story generation models have concentrated on syntactic level rather than semantics. syntactically constructed story may lose their interestingness. To provide the semantics for the constructed story, ontology has significant role. Ontology is a formal explicit shared conceptualization, which is used to provide the domain knowledge. This paper focuses on reasoning the sentences to check the semanticness of the constructed story using ontology.

关键词： Ontologies Natural languages computer science Artificial intelligence Vocabulary Knowledge transfer Educational institutions Terminology Information systems Information processing

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MANPOWER-PROFILING APPROACH TO sHIPYARD PRODUCTIVITY MANAGEMENT

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NAVAL ENGINEERs JOURNAL 1980年第4期92卷 49-56页

作者： LAMARTIN, FH POWELL, EF USN (RET.) Capt. Fredrick H. Lamartin USN (Ret.) received his B.S. degree in Naval Engineering from the U.S. Naval Academy in 1944 and his B.S. degree in Electrical Engineering and M.S. degree in Nuclear Engineering from Massachusetts Institute of Technology. He has over 30 years of professional experience in the areas of Ship Systems Marine and Industrial Engineering and Logistics. After retirement following 27 years service in the U.S. Navy he joined Booz Allen & Hamilton Inc. where he is currently a Senior Associate performing logistics engineering assignments for the TRIDENT System Project Manager. He has been a member of ASNE since 1969. Mr. Edward F. Powell formerly a Project Engineer with Booz Allen & Hamilton Inc. in the field of Applied Research was involved in the quantification and evaluation of contractor shipbuilding claims against the U.S. Navy and was a prime contributor to the U.S. Navy Shipbuilding Claims Manual. He has 13 years of shipbuilding and heavy construction experience primarily in the functions of Industrial Engineering. Production Planning and Cost Control. He received his B.S. degree in Management from Old Dominion University in 1962 and his MBA degree from the University of Florida in 1968. Currently he is the Research Manager for the Commonwealth of Virginia.

Traditional approaches to measuring shipyard productivity have many weaknesses. some approaches lead to general overall measures that provide little insight of causes of productivity changes. Other techniques generate very detailed measures from which general conclusions cannot be drawn. Other methods measure only a small part of all shipwork. some techniques for determining productivity were developed during analyses of manpower conditions at shipyards involved in recently-resolved major shipbuilding claims. This manpower-profiling technology measures productivity b.s.d upon the characteristics, the work force, and the working conditions in the shipyard. The manpower-profiling approach is made up of three elements: 1) Identifying the work force characteristics and working conditions that affect productivity; 2) Establishing the relationship between productivity and each work force characteristic and working condition; and 3) Using the productivity relationships to measure overall productivity. These three elements are discussed in the paper along with suggestions for further development of the approach.

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IoT b.s.d Optical sensor Network For Precision Agriculture

IoT Based Optical Sensor Network For Precision Agriculture

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2024 International Conference on Optimization Computing and Wireless Communication, ICOCWC 2024

作者： Durai, R. s. Vishnu Vijayakumar, R. Lakshmisridevi, s. bhanu, shaik Thasleem Arunkumar, U. Sri Ramakrishna Engineering College Department of Computer Science and Engineering Tamilnadu Coimbatore641022 India B.S. Abdur Rahman Crescent Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu Chennai600048 India Rajalakshmi Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai602105 India Department of Electrical and Electronics Engineering Tamil Nadu Coimbatore641021 India

ISBN: (纸本)9798350383348

Precision agriculture is a cutting-edge farming strategy that maximizes harvests by using cutting-edge technology and data-driven decision-making. Optical sensors and other Internet of Things (IoT) devices have great promise to revolutionize farming operations in this setting. sensor networks and Machine Learning (ML) b.s.d tracking devices are in great demand because of the precise data extraction and analysis they give. This research was undertaken with the goal of reducing agricultural hazards and promoting smart farming practices. Diseases caused by insects and other diseases may reduce crop yields if not addressed quickly. Thus, in this study, we provide a unique artificial swarm fish optimized naive bayes (AsFONb) method for keeping an eye on the health of the soil and preventing diseases from manifesting in cotton plants' leaves. In this research, numerous important indicators of crop growth and health were monitored using Internet of Things (IoT) devices equipped with optical sensors. The environmental factors like as temperature, humidity, light intensity, and chlorophyll content are recorded by these sensors. The proposed method involves sending the collected data to a central server for processing and analysis via wireless transmission. Once the disease has been detected, the information will be sent to the farmers via Android app. The Android app can show the chemical concentration in a container with soil factors like humidity, temperature, and wetness. Using an Android app, you may control the relay and hence the power supply and chemical sprinkler system. The experimental findings demonstrate that the proposed solution outperforms the status quo in disease identification. © 2024 IEEE.

关键词： Agriculture IoT Optimization Approach

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smart Agricultural Field Monitoring system 1st

Smart Agricultural Field Monitoring System

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1st International Conference on Expert Clouds and Applications, ICOECA 2023

作者： Fatima, N. sabiyath Noor Alleema, N. Muthupriya, V. Revathi, s. Akanksha, Geriga balaji, K. Department of Computer Science and Engineering B.S. Abdur Rahman Crescent Institute of Science and Technology Chennai India Department of Information Technology Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (纸本)9789819917440

The global population is increasing rapidly;as a result of it, there will be a high demand in the supply of food and water. To meet the requirement, advancing the agricultural process is one of the most important needs. Therefore, this paper proposes a smart agricultural field monitoring system which consists of four sensors, temperature prediction DHT11 sensors, soil moisture prediction sensors, rain detection sensors and water level monitoring sensors or ultrasonic sensors. If the moisture level decreases, then a notification is sent stating that the soil moisture levels have decreased and the pump motor turns on automatically to supply water to the field. A rain detection sensor is used to predict the rainfall in the field. This model focuses on a fully automated system that helps farmers to get a high yield with less amount of energy and resources in the field. The experimental results reveal the efficient usage of water resources in the agricultural field. Moreover, the data collected from the field are directly stored in the cloud system. © 2023, The Author(s), under exclusive license to springer Nature singapore Pte Ltd.

关键词： Internet of things

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Levels of social network analysis and small team problem solving in the classroom 122

Levels of social network analysis and small team problem sol...

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2015 122nd AsEE Annual Conference and Exposition

作者： simon, Peter A. Finger, susan Krackhardt, David siewiorek, Daniel P. smailagic, Asim Carnegie Mellon University B.S. Civil Engineering United States Carnegie Mellon University Department of Civil and Environmental Engineering United States Carnegie Mellon University Tepper School of Business United States Carnegie Mellon University Department of Electrical and Computer Engineering and Computer Science United States Carnegie Mellon University Institute for Complex Engineered Systems Department of Electrical and Computer Engineering United States

In a collaborative learning environment, transfer of knowledge depends strongly on sociocultural factors including the interaction among the learners as well as the interactions with the instructor. An understanding of some of the factors that affect the dynamics of learners and learning can be gleaned through the use of social network analysis (sNA). Even a single time-slice of the social network of a class, which shows the social ties between the students, can reveal much about a student's position in the network, which may affect what and how a student learns and his/her problem solving ability. This paper presents a study of the levels of the social network of students in an engineering project course. The analysis is done in the context of a design task given to small teams of students. The quality of the final design is evaluated using a rubric that yields a quantifiable result. We relate the team members' perceptions of their network with the problem solving ability of the team. We found significant correlations between a team's project score and a team's balance as well as with the individual student's perception of their team balance, although the sample size was small. In this context, the balance of a team is the degree to which feelings are reciprocated, as discussed in section 3. The perception of team balance, or the levels of the social network, is discussed in section 6. The levels of social network analysis uncovers whether team members correctly perceive the relationships among their teammates. These initial findings open opportunities for future work on the role social network analysis can play in the analysis of collaborative learning. © American society for engineering Education, 2015.

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simulator for swarm Feature selection Algorithms for Classification Problem

Simulator for Swarm Feature Selection Algorithms for Classif...

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Interdisciplinary Approaches in Technology and Management for social Innovation (IATMsI), IEEE Conference on

作者： Mayuri Diwakar Kulkarni Makarand shahade shailesh Deore Nilesh Khandekar Ishwari bhavsar Jayesh Garud Kunal Ahirrao Dept. of Computer Engineering SVKM’s Institute of Technology Dhule India Dept. of Computer Engineering SSVPS’s B.S. Deore CoE Dhule India

swarm Intelligence (sI) algorithms address difficult optimization and decision-making issues using cues from collective behavior. These behaviors are inspired by social creatures like ants, bees, and birds. Using these creatures' behaviors algorithms have demonstrated amazing promise in several fields, such as distributed computing, robotics, and optimization. Assessing sI algorithms' performance under various conditions and scenarios is still a significant difficulty, though. We introduce a new swarm Intelligence Algorithm Performance Analysis simulator (sIAPAs) to tackle this issue. This proposed simulator introduces a novel approach to automating the process of selection for swarm Intelligence (sI) algorithms on datasets. The system empowers users to upload datasets into a specialized simulator, which then orchestrates a comparative analysis using a suite of sI algorithms. The simulator evaluates and selects the most suitable algorithm b.s.d on the dataset's characteristics and problem domain. This provides the optimal selected features by the respective algorithms b.s.d on measured parameters.

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