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A Likelihood Ratio Test of Genetic Relationship among Languages

A Likelihood Ratio Test of Genetic Relationship among Langua...

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2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL 2024

作者： Akavarapu, V.S.D.S. Mahesh Bhattacharya, Arnab Dept. of Computer Science and Engineering Indian Institute of Technology Kanpur India

ISBN: (纸本)9798891761148

Lexical resemblances among a group of languages indicate that the languages could be genetically related, i.e., they could have descended from a common ancestral language. However, such resemblances can arise by chance and, hence, need not always imply an underlying genetic relationship. Many tests of significance based on permutation of wordlists and word similarity measures appeared in the past to determine the statistical significance of such relationships. We demonstrate that although existing tests may work well for bilateral comparisons, i.e., on pairs of languages, they are either infeasible by design or are prone to yield false positives when applied to groups of languages or language families. To this end, inspired by molecular phylogenetics, we propose a likelihood ratio test to determine if given languages are related based on the proportion of invariant character sites in the aligned wordlists applied during tree inference. Further, we evaluate some language families and show that the proposed test solves the problem of false positives. Finally, we demonstrate that the test supports the existence of macro language families such as Nostratic and Macro-Mayan. © 2024 Association for Computational Linguistics.

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Enhancing Supply Chain Efficiency through IoT Integration: A Detailed Examination of the Chances and Difficulties 2

Enhancing Supply Chain Efficiency through IoT Integration: A...

引用

2nd International Conference on Advances in Computation, Communication and Information Technology, ICAICCIT 2024

作者： Mehra, Chaitanya Sharma, Shaifali Arora, Jayant Sharma, Aakanksha Mahajan, Pranaya Manhotra, Aryan Dept. of Computer Science Engineering Chandigarh University Punjab Mohali India

ISBN: (纸本)9798331541217

The adoption of Internet of Things (IoT) technology in supply chain management has brought about noteworthy transformations, boosting productivity, visibility, and evidence- based decision-making. This research investigates the primary reasons for implementing IoT in contemporary supply chains, tackling issues such as limited real-time monitoring, suboptimal inventory control, and the necessity for preemptive risk manage- ment. The investigation delves into the capabilities of IoT-driven solutions, including sensor technology, RFID systems, and data analysis tools, to confront these obstacles by enabling constant surveillance and anticipatory maintenance, taking into account the impact of innovative technologies such as blockchain and artificial intelligence. The results underscore the considerable effect of IoT in establishing robust, flexible, and eco-friendly supply chains, providing valuable insights for businesses aiming to improve their operational effectiveness in an increasingly digital landscape. © 2024 IEEE.

关键词： Inventory control

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Classification of Foods based on Bijective Soft Set 15

Classification of Foods based on Bijective Soft Set

引用

15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Das, Priyanka Bhattacharya, Rupam Dept. of Computer Application and Science Institute of Engineering & Management Kolkata India

ISBN: (纸本)9798350370249

Cuisine data is typically classified using images of the cuisine, cooking methods, cooking utensils, and, most importantly, ingredients. The vast amount of recipe data available on the internet necessitates the creation of specialised systems for storing, retrieving, and effectively utilising stored knowledge and data. Neural networks based expert systems are steadily being used to classify the cuisine data. But it lacks in dealing the vagueness present in the categorical cuisine data. Also, most of the popular recipes available online are mostly unhealthy. As a result, the Bijective soft set theory has been introduced into this study since it is an intelligent method for developing classification rules from food data and then recognising ambiguity and data reliance. The classification guidelines also address the necessity to select healthy/nutritious meals. The investigation outcomes from using bijective soft set analysis to a set of data samples are presented and appraised in this work. Furthermore, the classification rules derived from this approach provides valuable help for developing food recommendation systems. The accuracy of this classification scheme has got really influenced by application of the bijective soft set when compared to other state-of-the-art classifiers. ©2024 IEEE.

关键词： bijective soft set classification classification rule generation cuisine ingredients

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An Intensive Review on Deep Learning Approaches in Non-Invasive Fetal Healthcare Monitoring 4

An Intensive Review on Deep Learning Approaches in Non-Invas...

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4th International Conference on Sustainable Expert Systems, ICSES 2024

作者： Harismithaa, L.R. Sadasivam, G. Sudha Psg College of Technology Dept. of Computer Science of Engineering Coimbatore India

ISBN: (纸本)9798331540364

Fetal health care is one of the most crucial concerns in today's world. Evaluating fetal well-being has always been challenging. Proper development and monitoring of the fetus are indispensable for its growth and healthy life. Monitoring and diagnosing abnormalities at the right gestational age is very significant. There are remarkable technical advancements in fetal well-being evaluation, bridging the gap between prenatal medicine and Artificial intelligence. This study provides an intensive review of various modes of non-invasive data that can be used for fetal health monitoring and decision support. Also, existing studies on fetal health care that use deep learning techniques for diagnosis and monitoring are explored. Also, a generic framework designed for an Automated Fetal healthcare Monitoring system using AI approaches is presented. © 2024 IEEE.

关键词： Fetal monitoring

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Rough Set Based Recipe Clustering Approach 10

Rough Set Based Recipe Clustering Approach

引用

10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Das, Priyanka Mukherjee, Chandrani Gupta, Samiksha Roy, Sayan Shah, Khushi Singh, Shreya Institute of Engineering & Management Dept. of Computer Application and Science Kolkata India

ISBN: (纸本)9798350384369

Online cooking instructions including ingredient lists and cooking methods are now widely available due to the proliferation of recipe sharing websites. Finding novel and complex patterns in such vast, ambiguous categorical data sets is a crucial task. Conventional clustering analysis algorithms frequently fail to address the uncertainty surrounding the exact value of features found in these categorical data sets. Thus to aid in decision-making, rough set based clustering is frequently used. This study aims to identify the optimal clustering feature from the recipe data that will result in the grouping of related food products by applying the rough purity notion. The recipe data has been regarded as a categorical valued information system in this instance, and information theoretic attribute purity is taken into account using the rough purity technique. © 2024 IEEE.

关键词： Food chemistry

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An algorithm frequently mines a set of items for big data to enhance efficiency

An algorithm frequently mines a set of items for big data to...

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2024 IEEE International Conference on Automation and Computation, AUTOCOM 2024

作者： Sonkar, Sanjay Kumar Singh, Balkar SunRise University Dept. of Computer Science and Engineering Rajasthan Alwar India

ISBN: (纸本)9798350382723

In the landscape of rapidly expanding data streams, deriving meaningful insights, particularly frequent itemsets, within massive datasets at a swift pace poses a significant challenge. Addressing this challenge, various methodologies for Frequent Itemset Mining have been proposed, yet they struggle with low support counts and efficiency concerns in handling large datasets. In response, our research introduces Jagged Itemset Counting (JIC) methodologies, aiming to effectively mine Frequent Itemsets from extensive data. The core objective revolves around devising a robust algorithm capable of identifying all Frequent Itemsets, irrespective of database size or the nature of the itemset. Central to this approach is the introduction of a straightforward label representation, GPLN (Geometric Progression Label Number), assigned to each frequent item. Utilizing CGPLN (Cumulative Geometric Progression Label Number), derived from the arithmetic sum of GPLNs within transaction subsets, forms the CGPLN-Label representation for each transaction subset (itemset). Comparative analysis reveals superior performance of JIC over Apriori and Eclat algorithms for small and medium-sized datasets, exhibiting efficiency even at minimal support thresholds. In the realm of Big Data, where FP-Growth and Eclat falter, the proposed technique shines with faster execution times, optimized main memory utilization, and efficient disc memory usage. © 2024 IEEE.

关键词： Data mining

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A High Throughput Bloom Filter Based TCAM Architecture for Fast NIDS

A High Throughput Bloom Filter Based TCAM Architecture for F...

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2024 International Conference on Smart Electronics and Communication Systems, ISENSE 2024

作者： Shaik, Sameera Noor Mahammad, S.K. IIITDM Kancheepuram Dept. of Computer Science and Engineering Tamilnadu Chennai India

ISBN: (纸本)9798331528126

One of the computationally most intensive tasks of an Network Intrusion Detection System (NIDS) is string/pattern matching where the payload of incoming packet is compared against predefined rule-set in order to detect any intrusion or threat. Although, various software algorithms and hardware architectures have been proposed to accelerate the performance of NIDS, wire-speed matching of these patterns still remains as a challenge. TCAMs are popularly used in many networking applications as they can perform the pattern matching in single clock cycle, but they always suffer with high power dissipation and delay due to their complex circuitry. Hence, most of the research focuses on efficient utilization of TCAM to cope up with high-speed links. In this paper, a high-throughput TCAM based architecture employed with Bloom filters is proposed. When a Bloom filter is deployed with TCAM, the number of patterns to be matched on TCAM reduces significantly since most of the patterns are eliminated in the prefiltering stage. There exist various Bloom filters such as Standard Bloom filter [1], Cuckoo filters [2], Counting Bloom filters [3] and Compressed filters [4]. Our proposed variant of Bloom filter outperforms all the existing filters in terms of speed while using less memory. We performed experiments on widely used open-source Snort NIDS and the proposed approach is able to achieve a throughput of 128Gbps for version 3.0c and 77.5Gbps for version 2.9.17. © 2024 IEEE.

关键词： Open source software

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A Random Forest Framework for Predicting Cardiovascular Disease in Diverse Populations' 2

A Random Forest Framework for Predicting Cardiovascular Dise...

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2nd International Conference on Advanced Computing and Communication Technologies, ICACCTech 2024

作者： Rahi, Parvez Kang, Sandeep Singh Singh, Ajay Pal Singh, Inderjeet Chandigarh University Dept. of Computer Science & Engineering Punjab Mohali India

ISBN: (纸本)9798331519056

According to the latest information from WHO, the World Health Organization, heart disease remains a major global health challenge, resulting in millions of deaths each year. Heart disease presents in various forms, including Hypertensive Heart Disease, Congestive Heart Failure (CHF), Coronary Artery Disease, Cardiomyopathy, and Arrhythmia. Accurate classification of these conditions is crucial for reducing mortality rates and improving patient outcomes. This study utilizes an enhanced Random Forest classifier to improve the accuracy of heart disease classification. The Synthetic Minority Oversampling Technique (SMOTE) was utilized to rectify the disparities in class. and Polynomial Feature engineering was employed to capture complex interactions within the data. The dataset underwent comprehensive preprocessing, including feature normalization and label encoding, followed by extensive hyperparameter tuning using GridSearchCV. The optimized model achieved a high accuracy of 92% and a Mean Squared Error (MSE) of 0.05. Key performance indicators, including an F1 score of 0.87, recall of 0.88, and accuracy of 0.92, highlight the model's efficacy in differentiating between various forms of heart disease. Future research could explore incorporating additional clinical data to further enhance the model's accuracy and applicability across diverse patient populations. © 2024 IEEE.

关键词： Clinical research

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Multiple language Discovery Abuse of Cyberspace with Advanced Transformer Architecture 2

Multiple language Discovery Abuse of Cyberspace with Advance...

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2nd IEEE International Conference on IoT, Communication and Automation Technology, ICICAT 2024

作者： Singh, Arvind Tripathi, Abhinandan Buddha Institute of Technology Dept. of Computer Science & Engineering Gorakhpur India

ISBN: (纸本)9798350368109

The number of reported instances of cyber abuse has also increased in tandem with the growth in the quantity of people that are regularly using the internet. Users' online freedom and privacy are jeopardized by such occurrences. Traditionally, the removal of such content from the internet has been accomplished through manual moderation and reporting systems. This strategy has, however, certain drawbacks, such as a reliance on people, longer delays, and less privacy for data. Traditional recurrent sequence models and supervised machine learning have been used in previous attempts to automate this procedure, but they have a tendency to perform badly on non-English material. A versatile solution that can handle multilingual text is urgently needed, given the increasing diversity of consumers using the internet. Moreover, translations frequently lose important context and emotion from content written in vernacular languages. In this work, we offer a generative deep learning based method for the detection of cyber abuse throughout Hindi, English with mixed codes, and Hindi text. The method uses bidirectional transformer-based BERT *** the TRAC-1 standard aggressiveness identification task, the suggested architecture can get extremely positive outcomes on the task leaderboard for English and accomplish cutting-edge performance on the Hindi dataset with mixed codes. The outcomes show that the new strategy is superior to the current ones because they were obtained without the need of numerous models or ensemble-based methods. Effective models that utilize deep learning techniques to analyze multilingual text can process a wider variety of inputs, making them indispensable in combating these types of social ills. © 2024 IEEE.

关键词： Generative adversarial networks

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The Tree Autoencoder Model, with Application to Hierarchical Data Visualization 38

The Tree Autoencoder Model, with Application to Hierarchical...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Carreira-Perpiñán, Miguel Á. Gazizov, Kuat Dept. of Computer Science and Engineering University of California Merced United States

We propose a new model for dimensionality reduction, the PCA tree, which works like a regular autoencoder, having explicit projection and reconstruction mappings. The projection is effected by a sparse oblique tree, having hard, hyperplane splits using few features and linear leaves. The reconstruction mapping is a set of local linear mappings. Thus, rather than producing a global map as in t-SNE and other methods, which often leads to distortions, it produces a hierarchical set of local PCAs. The use of a sparse oblique tree and of PCA in its leaves makes the overall model interpretable and very fast to project or reconstruct new points. Joint optimization of all the parameters in the tree is a nonconvex nondifferentiable problem. We propose an algorithm that is guaranteed to decrease the error monotonically and which scales to large datasets without any approximation. In experiments, we show PCA trees are able to identify a wealth of low-dimensional and cluster structure in image and document datasets. © 2024 Neural information processing systems foundation. All rights reserved.

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