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Enhanced MADM Strategy with Heptapartitioned Neutrosophic Distance Metrics

Neutrosophic Sets and Systems 2025年 78卷 74-96页

作者： Eassa, Mohamed Myvizhi, M. Elkholy, Mohamed Abdelhafeez, Ahmed Elbehiery, Hussam Computer Science Department Faculty of Information System and Computer Science October 6 University Giza12585 Egypt Applied Science Research Center Applied Science Private University Amman Jordan Department of Mathematics KPR Institute of Engineering and Technology Tamilnadu Coimbatore India Faculty of Computer Science& Engineering Alamein International University Egypt

In this work, a novel methodological approach to multi-attribute decision-making problems is developed and the notion of Heptapartitioned Neutrosophic Set Distance Measures (HNSDM) is introduced. By averaging the Penta-Partitioned Neutrosophic Distance Measures under particular conditional criteria, the suggested technique ranks the options. Additionally, we explore the fundamental characteristics of HNSDM and provide several illustrative theorems. The Heptapartitioned Neutrosophic Set Distance Measures are formulated based on the Hepta Neutrosophic Set. Lastly, a practical numerical example is provided to illustrate the efficacy of the suggested methodology. © 2025, University of New Mexico. All rights reserved.

关键词： Heptapartitioned Neutrosophic set Heptapartitioned Neutrosophic set and MADM strategy Heptapartitioned set Neutrosophic sine distance measure

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A Comprehensive Study of Case-Based Reasoning in Information Retrieval: An Artificial Intelligence Approach

A Comprehensive Study of Case-Based Reasoning in Information...

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2025 International Conference on Pervasive Computational Technologies, ICPCT 2025

作者： Solanki, Surabhi Verma, Seema Biswas, Prolay Roy, Pulakesh Singh, Aparna Sharma, Surbhi Computer Science & Engineering Banasthali Vidyapith Rajasthan Tonk India Scset Bennett University Greater Noida India Education National Institute of Technical Teachers' Training and Research Department of Electrical & Electronics Engineering Bhopal India Computer Science & Engineering Dayananda Sagar University Ramanagara India Computer Science& Engg Brainware University Kolkata India

ISBN: (纸本)9798331508685

Information Retrieval (IR) systems which provide accurate and contextually relevant information to healthcare and law etc. where accuracy is very crucial. Traditional information retrieval methods fundamentally based on keyword matching and statistical models usually difficult to achieve the semantic meaning and contextual nuances of queries. Case-Based Reasoning (CBR), as an artificial intelligence technique, provide advance solutions to enhance IR systems based on a set of past cases through adaptive context-supporting retrieval. By taking advantage of past cases data, CBR allows IR systems to interpret complex context-sensitive queries and offer personalized results. The paper provides an overview of traditional IR models, examines the CBR integration into these systems, and evaluates the impact of CBR on retrieval effectiveness and personalization. And finally, we elaborate on the challenges involved with scaling and adapting cases towards real-time retrieval and provide some future directions for research, including integrating machine learning techniques and hybrid models to enhance the relevance, interpretability, and scalability of CBR-based IR systems. © 2025 IEEE.

关键词： Case based reasoning

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Comparative Analysis of an AI-enabled State-of-the-Art Models for the Detection of DDoS Attacks on IoT Devices

Comparative Analysis of an AI-enabled State-of-the-Art Model...

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Disruptive Technologies (ICDT), International Conference on

作者： Rajneesh Kumar Gaurav Gulati Bhupesh Gupta Mohit Chhabra Department of Computer Science & Engineering Maharishi Markandeshwar Engineering College MMDU Mullana Haryana India Department of Computer Science& Engineering IMS Engineering College Ghaziabad UP India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

With technological advancement, Distributed Denial of Service (DDoS) attacks are gaining momentum in IoT networks. DDoS attacks are widely spreading on IoT devices since they have limited resources (memory) and no efficient security models. Different security measures are available to protect IoT devices from attacks. Some of these are firewalls, Intrusion detection systems, etc. Recently, researchers have utilized Artificial Intelligence for early detection of attacks due to its capability of detecting menaced attacks and, hence, boosting security. In this paper, a rigorous survey of different AI-enabled state-of-the-art models for detecting DDoS Attacks was done, and the future direction and scope of improvement for early Detection of DDoS attacks on IoT devices were highlighted.

关键词： Surveys Analytical models Firewalls (computing) Memory management Intrusion detection Denial-of-service attack Internet of Things Security Artificial intelligence Disruptive technologies

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A Comprehensive Study of Case-Based Reasoning in Information Retrieval: An Artificial Intelligence Approach

A Comprehensive Study of Case-Based Reasoning in Information...

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Pervasive Computational Technologies (ICPCT), International Conference on

作者： Surabhi Solanki Seema Verma Prolay Biswas Pulakesh Roy Aparna Singh Surbhi Sharma Computer Science & Engineering Banasthali Vidyapith Tonk Rajasthan SCSET Bennett University Greater Noida India Department of Electrical & Electronics Engineering Education National Institute of Technical Teachers' Training and Research Bhopal India Computer Science & Engineering Dayananda Sagar University Ramanagara India Computer Science& Engg Brainware University Kolkata India

ISBN: (数字)9798331508685

ISBN: (纸本)9798331519476

关键词： Adaptation models Accuracy Scalability Semantics Medical services Machine learning Information retrieval Cognition Real-time systems Context modeling

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Multi-stage system for preprocessing mammograms

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IEIE Transactions on Smart Processing and Computing 2020年第2期9卷 119-126页

作者： Shrivastava, Neeraj Bharti, Jyoti Department of Computer Science& Engineering Maulana Azad National Institute of Technology Bhopal M.P India Department of Computer Science & Engineering IPS IES Academy Indore MP India

Breast image analysis is important for the detection and diagnosis of various diseases, but more specifically, breast cancer. The most popular and widely used screening tool is mammography. It gives a better result than other screening tools for breast image analysis. Mammograms are difficult to analyze because of noise, poor quality, artifacts, and the inclusion of pectoral muscle. The technique proposed in this paper has several stages for preprocessing mammograms. First, artifacts and the background are removed by changing the original image into a binary image using Otsu s thresholding. The medio-lateral view of mammography images has two orientations: left or right. Orientation detection in the next step is important for pectoral muscle removal, which is done using the line segment method. Contrast is enhanced using contrast limited adaptive histogram equalization, and noise is reduced using a median filter. The proposed technique was applied to a publicly available dataset from the Mammographic Image Analysis Society, from which 322 images were analyzed, and performance parameters calculated. After calculations for accuracy, sensitivity, and specificity, the accuracy achieved was 94.62%, sensitivity achieved was 90.21%, and specificity was 97.20%.Copyrights © 2020 The Institute of Electronics and Information Engineers.

关键词： Mammography

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Mining of Frequent and High Utility Fuzzy Sequential Patterns 1

Mining of Frequent and High Utility Fuzzy Sequential Pattern...

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1st International Conference on Applied Artificial Intelligence and Computing, ICAAIC 2022

作者： Ritika Gupta, Sunil Kumar I K Gujral Punjab Technical University Department of Computer Science& Engineering India Sardar Beant Singh State University Department of Computer Science& Engineering India

ISBN: (纸本)9781665497107

Sequential pattern mining is one of the very popular data mining techniques to find patterns from databases. The variation in technique is based on the threshold used for mining which can be frequency, utility, or both. The use of fuzzy theory helps to provide natural representation by expressing parameters in linguistic terms. This paper proposes a hybrid fuzzy sequential pattern mining algorithm that expresses quantity in linguistic terms. The proposed method generates less number of patterns than the existing algorithms, according to the results of the experiments. © 2022 IEEE.

关键词： Linguistics

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Comparative Analysis of Machine Learning based Plant Leaf Disease Detection Systems 14

Comparative Analysis of Machine Learning based Plant Leaf Di...

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14th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2023

作者： Jamadar, Riyazahemed A. Sharma, Anoop Wagh, Kishor Department of Computer Science& Engineering Singhania University Rajasthan India Department of Computer Engineering AISSMS IOIT Pune India

Recently the availability of Machine Learning(ML) algorithms through state of the art programming interfaces & tools has lead the research community to employ ML based object detection/classification algorithms & techniques for the research work with improved accuracy & performance. There is a significant amount of research work that employs ML based approaches for plant leaf disease detection & classification. A systematic study and comparative analysis of these ML algorithms provide a good insight to researchers and guide them to select & blend these ML algorithms for higher accuracy and performance. In this paper an effort is made to establish comparative analysis of the most recent work done in this domain, The ML algorithms have been analyzed against parameters like the data set, preprocessing techniques, feature extraction methods and accuracy, and the type of diseases that could be detected. Every analysis concisely discusses the characteristics and scope for further extensions of the proposed technique/method. © Grenze Scientific Society, 2023.

关键词： Object detection

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Improving the performance of aspect based sentiment analysis using fine-tuned Bert Base Uncased model

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International Journal of Intelligent Networks 2021年 2卷 64-69页

作者： Geetha, M.P. Karthika Renuka, D. Department of Computer Science& Engineering Sri Ramakrishna Institute of Technology Tamil Nadu India Department of Information Technology PSG College of Technology Tamil Nadu India

Nowadays, digital reviews and ratings of E-commerce platforms provide a better way for consumers to buy the products. E-commerce giants like Amazon, Flipkart, etc provide customers with a forum to share their experience and provide potential consumers with true evidence of the product's outcomes. To obtain useful insights from a broad collection of reviews, it is important to separate reviews into positive and negative feelings. In the proposed work, Sentiment Analysis is to be done on the consumer review data and categorize into positive and negative feelings. Naïve Bayes Classification, LSTM and Support Vector Machine (SVM) were employed for the classification of reviews from the various classification models. Many of the current SA techniques for these customer online product review text data have low accuracy and often takes longer time in the course of training. In this research work, BERT Base Uncased model which is a powerful Deep Learning Model is presented to elucidate the issue of Sentiment Analysis. The BERT model gave an improved performance with good prediction and high accuracy compared to the other methods of Machine Learning in the experimental evaluation. © 2021 The Author(s)

关键词： Sentiment analysis

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Wormhole attack in wireless sensor networks: A critical review 11th

Wormhole attack in wireless sensor networks: A critical revi...

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11th International Conference on Advanced Computing and Communication Technologies, ICACCT 2018

作者： Dutta, Nishigandha Singh, Moirangthem Marjit Department of Computer Science& Engineering North Eastern Regional Institute of Science & Technology Nirjuli791109 India

ISBN: (纸本)9789811306792

A Wireless sensor network (WSN) consisting of spatially distributed autonomous devices using sensors is prone to numerous kinds of threats and attacks due to various reasons such as unattended nature of deployment in an un-trusted environment, limited network resources, easy network access as well as range of radio transmission. Wormhole attack is one such attack where an intruder establishes a low latency link between two sensor nodes so as to misguide the nodes and exhaust network resources by gaining access to sensitive information. This paper outlines the wormhole attack in WSN and provides a critical review on the techniques to deal with it. Various wormhole detection techniques and prevention mechanisms that can be used to counter the wormhole attack in WSN are compared and analyzed briefly in this paper. © 2019, Springer Nature Singapore Pte Ltd.

关键词： Sensor nodes

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Optimization Technique to Reduce Resource Utilization of Software Key Loggers Using PSO 3

Optimization Technique to Reduce Resource Utilization of Sof...

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3rd International Conference on Pervasive Computing and Social Networking, ICPCSN 2023

作者： Sharma, Yashika Rana, Muskaan Apex Institute of Technology Chandigarh University Mohali India Chandigarh University Department of Computer Science& Engineering Mohali India

ISBN: (纸本)9798350322842

Software key loggers are the leading type of keylogging. Both cheap and consumer-specific and specially created keylogging programs are easily accessible online. These key loggers should be revised to each target operating system to guarantee the appropriate handing of I/O devices. Therefore, system differences inevitably initiate operating system-based devices of software key loggers: keyboard state tables, system routine hooks, and the adoption of kernel-mode layered drivers. It is analyzed that software key logger has high execution time and resource utilization. In this research work, the PSO technique is applied which will optimize the software key loggers. The proposed model is implemented in MATLAB and results are analyzed in terms of execution time, resource utilization. © 2023 IEEE.

关键词： MATLAB

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