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4th IEEE Asian Conference on Innovation in technology, ASIANCON 2024

作者： Patil, Harshal Swati Kamala, L. Chaturvedi, Arpana Veeranjaneyulu, Rayavarapu Vekariya, Vipul Maharashtra Pune India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Gitam School of Technology Department of Computer Science and Engineering Karnataka Bengaluru561203 India New Delhi Institute of Management Department of IT/AI/ML New Delhi110062 India Pace Institute of Technology & Sciences Department of Computer Science and Engineering Andhra Pradesh Ongole523272 India Parul University Parul Institute of Engineering and Technology Department of Computer Science and Engineering Post Limda Gujarat 391760 India

ISBN: (纸本)9798350354218

This study aims to design a CAD tool for Alzheimer's disease (AD) detection using linear SVM classifiers on MRI scans. Existing approaches often face challenges with high-dimensional feature spaces and limited data accessibility from repositories like ADNI. To overcome these, three SVM-based classifier proposals were explored. Results demonstrated promising accuracy, sensitivity (Se), and specificity (Sp), particularly with cross-validation techniques. Notably, classifiers trained with image histograms showed superior performance in differentiating between AD, cognitively normal (CN), and mild cognitive impairment (MCI). Despite successes, initial approaches encountered difficulties, necessitating alternative methods. Challenges in data access and processing highlight the need for improved data management tools. Moving forward, refinements such as feature selection and testing with larger datasets could enhance classifier performance. Exploring deep neural networks as an extension may offer further insights into AD detection methodologies for early diagnosis and intervention. © 2024 IEEE.

关键词： Deep neural networks

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Exploratory Analysis of Methods, Techniques, and Metrics to Handle Class Imbalance Problem 2

Exploratory Analysis of Methods, Techniques, and Metrics to ...

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2nd International Conference on Machine Learning and Data engineering, ICMLDE 2023

作者： Sneha, H.R. Annappa, B. Department of Information Science & Engineering Nitte Meenakshi Institute of Technology Bengaluru560064 India Department of Computer Science & Engineering National Institute of Technology Karnataka Surathkal575025 India

Class imbalance a common challenge in machine learning, often results in skewed predictions and misrepresentative model assessments, highlighting the need for effective countermeasures. Our detailed survey dives into three primary techniques for addressing this issue: data-level interventions, algorithmic modifications, and integrated hybrid solutions. We thoroughly dissect each approach, delineating its merits, drawbacks, and ideal use cases. Data-level methods aim to restructure the dataset for class balance, while algorithmic techniques recalibrate the learning process to better detect the minority class. The hybrid strategies merge the benefits of both for a holistic remedy. The study further emphasizes the importance of precise evaluation metrics, elaborating on both conventional metrics and those tailored for imbalanced scenarios. Our objective is to arm professionals with a deep insight into tackling class imbalance, especially within the big data framework. The insights shared aspire to inspire the creation of resilient, equitable machine learning models adapt at navigating imbalanced data, ensuring enhanced prediction fidelity and consistency. © 2024 Elsevier B.V.. All rights reserved.

关键词： Machine learning

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Stage Wise Classification and prediction of Alzheimer disease using Hybrid Appraoch 4

Stage Wise Classification and prediction of Alzheimer diseas...

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4th International Conference on Intelligent Technologies, CONIT 2024

作者： Prabagaran, A. Dwivedi, Vinay Neelam Rani, Jyoti Angelin Blessy, J. Padmanabhan, P. Gupshup Technology India Pvt Ltd Bengaluru India Galgotias University School of Computer Science and Engineering Uttar Pradesh Greater Noida India Noida Institute of Engineering & Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India Galgotias Univeristy School of Computing Science and Engineering Dept of Computer Application India Greater Noida Institute of Technology Department of Computer Science and Engineering Uttar Pradesh Greater Noida India

ISBN: (纸本)9798350349900

Alzheimer's, the most prevalent cause of dementia, leads to a continuous decline in cognitive functions, social skills, and behavior, affecting individuals' independent functioning. As per survey 2010 in India around 3.7 million affected by dementia, and this data is expected to twofold by 2030. The associated cost of care is projected to triple, necessitating ongoing support. This issue is a significant societal concern that warrants careful consideration. Prediction and classification of Alzheimer's disease (AD) are crucial for identifying and addressing it at earlier stages. The disease is typically classified into three stages: early-stage dementia, middle-stage, and late-stage. However, several challenges exist, including limited awareness, diagnostic difficulties, insufficient technologies and treatments, unpredictable progression across stages, and a lack of comprehensive stage-by-stage predictions. This work proposes a hybrid model for predicting, classifying, and stage-wise classifying Alzheimer's disease. The hybrid model aims to address the challenges associated with the disease, utilizing different features for prediction and classification. The hybrid model used a Random forest, convolutional neural network (CNN), kernel density algorithm (KDA) with hyperparameter tuning for accurate prediction and classification. The proposed hybrid method used Random forest used for basic classification, CNN and kernel density for stage wise classification. The proposed hybrid method achieved an impressive 95.6% accuracy in stage wise classification and prediction of AD. © 2024 IEEE.

关键词： Convolutional neural networks

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A Novel Deep Learning Framework for Enhanced Acute Lymphoblastic Leukemia Detection

A Novel Deep Learning Framework for Enhanced Acute Lymphobla...

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2024 Frontiers in Optics, FiO 2024

作者： Ahmadsaidulu, Shaik Kanase, Akash Suresh Jain, Puneet Kumar Banoth, Earu Department of Biotechnology & Medical Engineering National Institute of Technology Odisha Rourkela769008 India Department of Computer Science & Engineering National Institute of Technology Odisha Rourkela769008 India

In this work a deep-learning model using enhanced YOLOv8(You Only Look Once) for classifying Acute Lymphoblastic Leukemia (ALL) and other normal *** 98% accuracy for ALL and 91% for combined (ALL & Normal) classif... 详细信息

关键词： Contrastive Learning

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Optimizing Resource Allocation for Energy Efficiency in Fog Cloud Computing Environments 13

Optimizing Resource Allocation for Energy Efficiency in Fog ...

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13th IEEE International Conference on Communication Systems and Network Technologies, CSNT 2024

作者： Kaur, Mandeep Aron, Rajni Seth, Shriya Chitkara University Punjab Department of Computer Science and Engineering India Nmims University Mpstme Mumbai India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Department of Computer Science and Engineering India

ISBN: (纸本)9798350305463

Fog computing has become the primary computing paradigm for IoT applications as it meets the low-latency needs of the growing number of IoT applications. However, the servers can get overwhelmed due to the high demand for fog resources in several IoT applications. Fog computing complements cloud computing, as it only processes user requests near them. Distributing tasks evenly across all nodes in the fog layer helps achieve optimal task processing. Load balancing in the fog-cloud computing environment aids in diminishing energy use. In this article, fog computing architecture named 'EcoFogLoad Architecture' has been proposed to balance the workload among the fog layer. Along with this, the 'Energy-Efficient Workload Optimization (EEWO)' algorithm has been proposed to optimize the use of resources at fog layer in terms of cost, time delay and energy consumption. iFogSim has been used to execute the proposed algorithm and obtain the experimental results. The results of the proposed approach are compared with those of other existing algorithms. Load balancing at the fog layer facilitates optimal resource utilization, reducing latency and improving service quality. The article concludes by presenting potential avenues for future research. © 2024 IEEE.

关键词： Fog

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Design & Development of Multipliers & Square Circuits Using the Concepts of Quantum-Dot Cellular Automata (QCA) technology 3

Design & Development of Multipliers & Square Circuits Using ...

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3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025

作者： Manjunath, T.C. Venugopal, Mohankumar Rajarajeswari College of Engineering Department of Computer Science and Engineering Bangalore India Dr. Ambedkar Institute of Technology Department of Electronics and Communication Engineering Bangalore India

ISBN: (纸本)9798331508456

Quantum dots Cellular Automatas [QCA] represents an groundbreaking advancement in nanoelectronics, characterizeed by their lower power consumptions, higher-speed operations, & enhanced integration capabilities. These attributes position QCA as a formidable contender to the prevalent Complementery Metal-Oxides-Semiconductors (CMOS) technology, which are currently the industry standard. The focus of these researches is on the innovative design of a QCA-based Multiplier and Square circuit. Multipliers are integral to various digital signal processing applications, where they significantly influence system throughput and power efficiency. Square circuits, on the other hand, are vital for performing mathematical computations. The design of the QCA-based multiplier incorporates binary adders and AND gates, optimizing it for efficient performance. To thoroughly assess the viability of QCA over traditional CMOS, a comparative analysis focusing on key metrics in the areas utilization & thier power's dissipations is undertaken. Their QCA Designer tool plays a critical role in this evaluation, allowing for the validation of the proposed model by examining parameters like cell count, area, and energy dissipation. This approach not only highlights the potential enhancements QCA offers over CMOS but, it further gives the router for a no. of future scope in innovations w.r.t. nanoelectronics. © 2025 IEEE.

关键词： CMOS integrated circuits

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Security System for Services Access in Smart City System Using Internet of Everything and Cloud 2

Security System for Services Access in Smart City System Usi...

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2nd International Conference on Disruptive Technologies, ICDT 2024

作者： Wadhwa, Amit Arora, Neerja G. L. Bajaj Institute of Technology and Management Department of Computer Science and Engineering Greater Noida India Ajay Kumar Garg Engineering College Department of Computer Science and Engineering Ghaziabad India

ISBN: (纸本)9798350371055

With the advancement of technologies like cloud computing and Internet of things, there are enormous areas in which these can be utilized. One such domain is Smart City concept utilizing or based on the architecture of Internet of everything. In a smart city the distinguish concept is the utilization of internet of everything architecture to effectively manage the services provided to users. Moreover, integration of internet of everything with cloud computing architecture would provide a better ecosystem in the area of smart cities. Providing services to smart city users could pose certain challenges and security issues with the integration of cloud computing. As per previous studied literature there is scope for improving access and authentication-based security of services imparted to smart city users. In this work, initially the consolidation of internet of everything (IoE) and cloud computing in smart cities is put forward. Further, this work focusses upon issues and challenges encountered in leveraging IoE in smart city environments with analysis of existing techniques for authentication and access control of cloud-based services and later provides a proposed system focusing upon imparting access control and authentication-oriented security for services delivered over a cloud and IoE followed by its analysis. © 2024 IEEE.

关键词： Access Control Authentication Cloud Computing Services IOE (Internet of Everything) Smart Cities

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Judicial nested named entity recognition method with MRC framework

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International Journal of Cognitive Computing in engineering 2023年第1期4卷 118-126页

作者： Zhang, Hu Guo, Jiayu Wang, Yujie Zhang, Zhen Zhao, Hansen School of Computer and Information Technology Shanxi University Shanxi Taiyuan030006 China Department of Computation Science and Engineering George Institute of Technology Atlanta United States

Judicial named entity recognition (JNER) is a basic task of judicial intelligence and judicial service informatization. At present, the research of JNER has attracted extensive attention. However, the existing JNER methods usually can only assign a single label to a token in the input sequence, which is not applicable to nested entities where a token may be assigned two or more different labels at the same time. Therefore, this paper introduces the machine reading comprehension (MRC) framework into JNER, and proposes a judicial nested NER method based on the MRC. Firstly, we design the question template according to the characteristics of judicial nested named entities, and construct the legal text named entity dataset in MRC format. Next, we introduce the span extraction MRC model based on the pre-trained to encode the question and text, and learn the context knowledge of the entity in the question. Finally, we extract the starting and end positions of the matching span respectively through two classifiers, to get the corresponding entities. The experimental results on the information extraction dataset in "CAIL2021" show, compared with the existing baseline models, the proposed method effectively improves the recognition effect of nested entities commonly existing in the judicial field. © 2023

关键词： Informatization

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A Review on GIF-Based Steganography Techniques 5th

A Review on GIF-Based Steganography Techniques

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5th International Conference on Recent Innovations in Computing, ICRIC 2022

作者： Chola, Abhinav Awasthi, Lalit Kumar Singh, Samayveer Department of Computer Science and Engineering Dr B R Ambedkar National Institute of Technology Punjab Jalandhar India Department of Computer Science and Engineering National Institute of Technology Uttarakhand Srinagar India

ISBN: (纸本)9789811998751

Data privacy has been a significant concern since the beginning of the Internet, and in the current scenario, the need and importance of data privacy are increasing exponentially. Today, a multitude of media formats including text, images, videos, Graphics Interchange Format (GIFs), etc., are available for exchanging data, and techniques of cryptography and steganography play a key role in maintaining data secrecy during such transmissions. This paper elaborates on the GIF-based techniques of steganography. It provides a detailed comparison of the working, advantages and limitations of the major approaches developed in this field in the last three decades. It provides a clear and chronological understanding regarding the evolution of GIF-based steganography methods and discusses future directions to enhance the existing techniques. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Steganography

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CKGLD: Curating Knowledge Graph for Linked Open Data Generation for Cyber Law and Ethics 4th

CKGLD: Curating Knowledge Graph for Linked Open Data Generat...

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4th International Conference on Emerging Trends and Technologies on Intelligent Systems, ETTIS 2024

作者： Aravind Krishnan, A. Deepak, Gerard Santhanavijayan, A. Venugopal, K.R. Department of Computer Science and Engineering BMS Institute of Technology and Management Bengaluru India National Institute of Technology Tiruchirappalli India Bangalore University Bengaluru India

ISBN: (纸本)9789819757022

This paper introduces a knowledge graph curation framework for the generation of linked open data in the domain of cyber law and ethics called the CKGLD. This framework utilizes both static metadata and dynamically acquired knowledge from Web 3.0. With advanced methods of term and category extraction, classification using both lightweight machine learning and strong deep learning methods, and clustering, the framework can exceed all similar frameworks in terms of performance. Initial terms are processed from the documents using the TF-IDF strategy. Static metadata stack is established from e-books, query logs, and glossaries. Renyi entropy is used to guide informative term selection for a decision tree classification;in this manner, the scale of the generated knowledge is managed by lightweight computing requirements. Dynamic knowledge from Web 3.0 is classified using GANs and is subjected to DBSCAN clustering. A multi-agent setup with parallelization enhances the efficiency of the clustering process. The knowledge graph is finally created using the Co-SIM rank and is evaluated for specificity using Hellinger's distance and volume of intersection index. These paradigms contribute to this framework significantly outperforming the baseline frameworks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Knowledge graph

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