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International Conference on Computing and Networking Technology (ICCNT)

作者： Murali Kanthi K. Venkateshwara Rao Sudha Pavani K Nuthanakanti Bhaskar Shankar Nayak Bhukya T. Hitendra Sarma Dept. of Computer Science & Engineering (Data Science) CMR Technical Campus Hyderabad Telangana India Dept. of Computer Science & Engineering CMR College of Engineering & Technology Hyderabad Telangana India Dept. of Computer Science & Engineering CMR Technical Campus Hyderabad Telangana India Dept. of Information Technology Vasavi College of Engineering Hyderabad Telangana India

Hyperspectral image (HSI) classification has made extensive use of deep learning approaches, specifically Convolutional Neural Network (CNN). CNN's ability to fully capture local contextual aspects of HSI makes it an excellent HSI classifier. Although, a significant factor in the poor classification performance is the scarcity of training data. Conventional CNN-based methods don't use the integrate-band relationships of HSI as much as they could because they typically employ 2D-CNNs to extract features. On the other hand, 3D-CNNs use a similar procedure to get both spectral and spatial data. This gets around the problem with 2D-CNNs, but it could mean that not enough features are extracted. As a solution, we offer a multi-scale 3D-CNN with multi branch feature fusion, which we call multi branch fusion CNN (MBF-CNN), for HSI classification. We begin by applying 3D-CNN on multiple scales to extract HSI features. After that, the multiple distinct characteristics are streamlined and combined together. The fused features then go through several fully connected layers to get the classification outcomes. The experiments are executed on both the new data sets and the publicly accessible benchmark data sets to expose the efficiency and durability of the proposed model. Empirically, it has been demonstrated that the presented model succeeds over the remaining state-of-the-art approaches in terms of classification accuracy.

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Reliability and Packet Transfer Efficiency of Sparkle Topologies

Reliability and Packet Transfer Efficiency of Sparkle Topolo...

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Electronics and Sustainable Communication Systems (ICESC), 2020 International Conference on

作者： A Joshi V Subedha Dept. of Artificial Intelligence & Data Science Panimalar Engineering College Poonamallee Chennai India Dept. of CSE Panimalar Engineering College Poonamallee Chennai India

Distributed computing through topology control involves making changes to the underlying network (modeled as a graph) to decrease the cost of distributed algorithms when executed across the modified networks. Topology construction builds reduced topology and topology maintenance adopts the reduced topology when the current topology is no longer optimal. This study makes major contributions to topology control by constructing new topologies named sparkle topologies using a ring topology, structured web topology, sun topology, and star topology. The efficiency of the sparkle topologies is checked by calculating the reliability using Wiener Index and data transfer using Cisco packet simulation. data transmission times, the number of hops required, and the number of potential failure points are all reduced in sparkle topologies compared to ring topologies.

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A Machine Learning and data Analytics Approach to Patient Risk Stratification

A Machine Learning and Data Analytics Approach to Patient Ri...

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data science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Veeramalai Sankaradass V.K Manindra Manish Rajesh Anandhan Ramprakash Velmurugan Sabarinathan Sakthivel Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai India Dhanalakshmi Srinivasan Institute of Medical Sciences and Hospital Perambalur India Dept. of Artificial Intelligence and Data Science Chennai Institute of Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

In this paper, we introduce a robust machine learning (ML) and data analytic solution for risk stratification of patients to promote clinical decision support systems (CDSS) that provide enhanced protection of the patient health interests. Our approach, which we have shown would minimally intrude into peoples' lives, uses predictive models to analyze patient data to identify high-risk patients early in the history of a wide range of medical conditions. Utilizing integrated structured data (e.g., lab results, vital signs) and unstructured data (e.g., clinical notes), our system improves the accuracy of medical decisions made by healthcare providers. We use a combination of supervised learning algorithms and real-time data processing to recalibrate risk scores, which serve as actionable insights for anticipatory interventions. The strategy also comes with extensive validation protocols to verify the efficacy of her model in a healthcare environment. Our results indicate that this practice dramatically increases the accuracy of risk assessments, with important implications for patient care and resource utilization.

关键词： Decision support systems data analysis Accuracy Protocols Supervised learning Real-time systems Risk management Resource management Protection Random forests

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New Cloud Service Discovery Mechanisms for Improved Usability

New Cloud Service Discovery Mechanisms for Improved Usabilit...

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2024 OPJU International Technology Conference on Smart Computing for Innovation and Advancement in Industry 4.0, OTCON 2024

作者： Ramu, K. Dasari, Kavitha Chandrashekar, Rakesh Nijhawan, Ginni Kalra, Ravi Al-Hussainawy, Mohammed Kassim Velammal Institute of Technology Dept. of Cse TN Chennai India Institute of Aeronautical Engineering Hyderabad Dundigal India New Horizon College of Engineering Department of Mechanical Engineering Bangalore India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Knowledge Park II Uttar Pradesh Greater Noida201306 India National University of Science and Technology Collage of Dentist Dhi Qar64001 Iraq

ISBN: (纸本)9798350373783

The Adaptive Service Discovery Framework changes cloud service discovery. It uses machine learning and linguistic analysis. ASDF employs three primary algorithms: CSR, SST, and UBM. They collaborate to make service discovery fast, aware, and personalized. UBM tailors service concepts to each person, while SST employs semantics to match correctly. How well a service fits into its environment is considered by CSR. These strategies provide a trustworthy foundation that adapts to users' changing demands and environments. This study reveals that ASDF is cheaper, more efficient, adaptable, and easier to use than conventional approaches. It performs better than DNS-Based Discovery, LDAP Service Discovery, SOAP-Based Service Discovery, UDDI Registry, and RESTful API Discovery, therefore it may improve cloud service discovery. This proves it can improve cloud service discovery. © 2024 IEEE.

关键词： Adaptability Adaptive Service Discovery Framework Cloud Service Discovery Contextual Service Ranking Cost-effectiveness Efficiency Semantic Analysis Semantic Service Tagging Usability User Behavior Modeling

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Internet of Things IoMT data Aggregation Using Machine Learning

Internet of Things IoMT Data Aggregation Using Machine Learn...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： Lakshmi Sagar Pulabaigari C. Sivakumar Dept. of Computer Science and Engineering School of Computing Mohan Babu University Tirupati Andhra Pradesh India Dept. of Data Science School of Computing Mohan Babu University Tirupati Andhra Pradesh India

ISBN: (数字)9798350372847

ISBN: (纸本)9798350372854

The Internet of Things (IoT) has revolutionized the way data is handled and collected, allowing for large amounts of information to be revolutionized quickly and efficiently. This has paved the way for Machine Learning (ML) applications to be used on IoT datasets, such as 10MT data aggregation. This paper discusses the use of a ML approach for aggregation 10MT data in order to better understand trends and classify patterns in various sectors that would otherwise go undetected. It discusses the benefits and challenges of such a system, and how it could be used to improve the efficiency of industrial operations. It stresses the importance of data integrity in this process, as wrong or incomplete datasets can significantly reduce the accuracy and reliability of the results obtained. Furthermore, it discusses the need for suitable ML algorithms and models, such as clustering and random forest, to be applied to the collected data in order to produce robust and meaningful output. It observes the security and privacy challenges associated with such ML-based 10MT data aggregation systems, and the potential solutions available.

关键词： Machine learning algorithms data aggregation Market research Security Biomedical monitoring Wearable devices Medical diagnostic imaging

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POSIX: A Prompt Sensitivity Index For Large Language Models

arXiv

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arXiv 2024年

作者： Chatterjee, Anwoy Kowndinya Renduchintala, H.S.V.N.S. Bhatia, Sumit Chakraborty, Tanmoy Dept. of Electrical Engineering Indian Institute of Technology Delhi India Media and Data Science Research Adobe Inc. India

Despite their remarkable capabilities, Large Language Models (LLMs) are found to be surprisingly sensitive to minor variations in prompts, often generating significantly divergent outputs in response to minor variations in the prompts, such as spelling errors, alteration of wording or the prompt template. However, while assessing the quality of an LLM, the focus often tends to be solely on its performance on downstream tasks, while very little to no attention is paid to prompt sensitivity. To fill this gap, we propose POSIX – a novel PrOmpt Sensitivity IndeX as a reliable measure of prompt sensitivity, thereby offering a more comprehensive evaluation of LLM performance. The key idea behind POSIX is to capture the relative change in log-likelihood of a given response upon replacing the corresponding prompt with a different intent-preserving prompt. We provide thorough empirical evidence demonstrating the efficacy of POSIX in capturing prompt sensitivity and subsequently use it to measure and thereby compare prompt sensitivity of various open-source LLMs. We find that merely increasing the parameter count or instruction tuning does not necessarily reduce prompt sensitivity whereas adding some few-shot exemplars, even just one, almost always leads to significant decrease in prompt sensitivity. We also find that alterations to prompt template lead to the highest sensitivity in the case of MCQ-type tasks, whereas paraphrasing results in the highest sensitivity in open-ended generation tasks. The code for reproducing our results is open-sourced at https://***/kowndinyarenduchintala/POSIX. © 2024, CC BY.

关键词： Open systems

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Stochastic Large-scale Machine Learning Algorithms with Distributed Features and Observations

Stochastic Large-scale Machine Learning Algorithms with Dist...

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IEEE International Conference on Big data

作者： Biyi Fang Diego Klabjan Truong Vo Dept. of Engineering and Applied Mathematics Northwestern University Evanston IL USA Dept. of Industrial Engineering and Management Sciences Northwestern University Evanston IL USA Dept. of Machine Learning and Data Science Northwestern University Evanston IL USA

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

As the size of modern datasets exceeds the disk and memory capacities of a single computer, machine learning practitioners have resorted to parallel and distributed computing. Given that optimization is one of the pillars of machine learning and predictive modeling, distributed optimization methods have recently garnered ample attention, in particular when either observations or features are distributed, but not both. We propose a general stochastic algorithm where observations, features, and gradient components can be sampled in a double distributed setting, i.e., with both features and observations distributed. Very technical analyses establish convergence properties of the algorithm under different conditions on the learning rate (diminishing to zero or constant). Computational experiments in Spark demonstrate a superior performance of our algorithm versus a benchmark in early iterations of the algorithm, which is due to the stochastic components of the algorithm.

关键词： Machine learning algorithms Computational modeling Stochastic processes Optimization methods Machine learning Predictive models Prediction algorithms Sparks Distributed computing Convergence

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Comprehensive Analysis of Machine Learning Approaches for Phishing Website Detection

Comprehensive Analysis of Machine Learning Approaches for Ph...

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Cognitive Computing in engineering, Communications, sciences and Biomedical Health Informatics (IC3ECSBHI), International Conference on

作者： Pragna K P Suhani Shetty Ashwini Kodipalli Ushasree. A Shridhar B. Devamane Roshan Joy Martis Dept. of Electronics and Communication Engineering Global Academy Of Technology Bengaluru Karnataka India Dept. of Artificial Intelligence & Data Science Global Academy of Technology Bengaluru Karnataka India Dept. of Computer Science & Engineering Gokaraju Lailavathi Engineering College Hyderabad India Department of Computer Science School of Computing Amrita Vishwavidyapeetam Mysuru Dept. of Electronics & Communication Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India

ISBN: (数字)9798331518523

ISBN: (纸本)9798331518530

Website phishing, a notable cybercrime, involves deceiving individuals by impersonating legitimate websites. URL spoofing, a common technique in website phishing, involves creating website names. The primary objective of this paper is to accurately differentiate between legitimate and phishing sites using relevant URL attributes and different binary classification machine learning algorithms. This study compares simple, bagging, and boosting algorithms across performance analysis metrics. The best-performing models are selected for an ensemble approach. Additionally, various hyperparameter tuning techniques are assessed for the Random Forest Classifier, and Explainable AI methods like LIME and SHAP are used to interpret findings. The experiment yields optimal parameters, achieving 89. % accuracy with an ensemble model with K-Nearest Neighbors, Decision Tree, XG Boost, and Cat Boost. These findings highlight the significance of ensemble models and hyperparameter tuning in machine learning.

关键词： Uniform resource locators Machine learning algorithms Explainable AI Phishing Computational modeling Biological system modeling Nearest neighbor methods Performance analysis Tuning Random forests

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Design and Evaluation of Reward Functions for Load Balancing in Cloud datacenters

Design and Evaluation of Reward Functions for Load Balancing...

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Innovative Trends in Information Technology (ICITIIT), International Conference on

作者： Samriddhee Ghosh Karthick Seshadri Chidambaran Kollengode Dept. of Computer Science and Engineering National Institute of Technology Andhra Pradesh India AI and Data Platforms LinkedIn Bangalore India

ISBN: (数字)9798350386813

ISBN: (纸本)9798350386820

Task scheduling and virtual machine (VM) consolidation techniques optimize resource utilization and distribute the load evenly across physical machines and virtual machines in a virtualized data center. Users’ requests to applications hosted on the virtual machines are dynamic. Though schedulers assign tasks to VMs based on the current state of the VMs and the expected resource requirements of the tasks, the requirements of the tasks typically tend to deviate from the expected requirements. This eventually leads to load imbalance and skewed energy consumption across virtual machines. Load imbalance and high energy consumption occur due to dynamic nature of workloads across the data center. An overloaded VM experiences degradation in performance thereby violating Quality of Service (QoS) and Service Level Agreement (SLA) requirements. In this work, a reinforcement learning (RL)-based task scheduling approach is utilized to minimize load imbalance, wherein we compare the effectiveness of using different reward functions. The RL agent learns a policy to minimize the number of overloaded VMs and to reduce deviation from a load-balanced condition by efficiently scheduling tasks using Q-learning algorithm. It takes scheduling decisions with the help of previous experiences when it encounters similar workload patterns. Experiments have been performed on the real time Google cluster traces and the proposed reward functions were assessed for their performance.

关键词： Energy consumption data centers Q-learning Quality of service Virtual machining Scheduling Real-time systems

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Effectiveness and Influence of Parts of Speech like adjectives and interjections for examining the feelings in sentences containing sarcasm: A study

Effectiveness and Influence of Parts of Speech like adjectiv...

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International Conference on Advances in Information Technology (ICAIT)

作者： Adarsh M J Pushpa Ravikumar Dept. of CS&E (Data Science) Adichunchanagiri Institute of Technology (Affiliated to VTU Belagavi) Chikkamagaluru Karnataka India Dept. of Computer Science and Engineering Adichunchanagiri Institute of Technology Chikkamagaluru Karnataka India

ISBN: (数字)9798350383867

ISBN: (纸本)9798350383874

In contemporary text analytics, examining the feelings in customer evaluations has taken centre stage. It is difficult to identify emotions in sentences written with irony. Social media portals and e-commerce portals are offering users to come up with comments and reviews with realistic funny emotions. As a way to identify the feelings expressed in sentences written in a mocking tone purpose, we have attempted to highlight the Adjectives function in this study by taking into account positive and negative adjectives as well as an interjection, both good and bad words. We propose the Adjective-based Sarcasm Detection Algorithm (ABSA) and the Interjection-based Sarcasm Detection Algorithm (IBSA) based on lexicon approach as machine learning approaches won't yield better results in detection of irony and have compared it with the existing approach with more sarcastic sentences. Our F score results for ABSA and IBSA are 0.86 and 0.91, respectively. This paper sheds light on investigating humour or irony in context of parts of speech (POS) words.

关键词： Social networking (online) Reviews Machine learning Electronic commerce Detection algorithms Information technology Portals

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