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Enhancing Alzheimer's Disease Diagnosis Through Deep Learning-Leveraging VGG-16 Architecture and Adaptive Boosting 5

Enhancing Alzheimer's Disease Diagnosis Through Deep Learnin...

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5th International Conference on Image Processing and Capsule Networks, ICIPCN 2024

作者： Irrigisetty, Harshavelu Madhavi, K. Reddy Avanija, J. Thokala, Rishi Reddy Varma, K. S. S. Rishi Sree Vidyanikethan Engineering College Department of CSE Tirupati India Department of AI&ML Tirupati India School of Computing Tirupati India SRM Institute of Science and Technology Department of CSE Chennai India

ISBN: (纸本)9798350367171

The key to early intervention and patient care with Alzheimer's disease is early detection. The World Health Organization estimates that 50 million people worldwide suffer from dementia, with 60-70 % of cases being Alzheimer's disease (AD). Moreover, the WHO projects that the number of people affected by AD will be nearly tripled by the year 2050, thus, the development of effective detection and management strategies. Our Research suggests a complete method that combines Convolutional Neural Networks (CNNs) with transfer learning using the VGG16 architecture, on the one hand, and Adaptive Boosting on the other, in order to improve the accuracy. The dataset includes NonDemented, VeryMildDemented, MildDemented, and ModerateDemented classes which makes the VGG16 model suitable for extraction of features and classification after the preprocessing steps, such as image resizing, conversion to arrays and pixel value normalization, are performed. From the problems faced in the earlier AD detection methodologies we use CNNs, transfer learning and adaptive boosting to improve the task performance. In conclusion, the combination of automated computational methods and sophisticated algorithms such as CNNs and AdaBoost is a progressive approach in AD detection. It provides services like efficiency, consistency, and improved diagnosis that can be applied to the patient care and the healthcare management. © 2024 IEEE.

关键词： Convolutional neural networks

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Secure Protocol With Mutual Authentication For Telecare Medical Information System Using ECC For IOT Enabled WBAN Devices

Secure Protocol With Mutual Authentication For Telecare Medi...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Ss Anushaa S Naundhini Dharminder Chaudhary Department of Computer Science and Engineering (Amrita School of Computing) Amrita Vishwa Vidyapeetham Chennai

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

Telecare Medical Information System (TMIS) is a technology that has been on rise in the recent times with the onset of the COVID-19 pandemic. TMIS can be implemented in wireless body area networks (WBAN) in the form of devices worn by the patient and is convenient and time saving for both patients and doctors. The data that is generated is transferred through an insecure channel and thus, there is a need to protect confidential patient data. There is a necessity to provide secure authentication and access control to ensure only authorized users can access the data even in a device with limited storage and computational power. In this paper, Elliptic Curve Cryptography and the three-factor authentication method are combined to propose an enhanced protocol that is immune to the flaws in previously proposed ones. It uses a trusted party that helps in the registration process, ensuring that only authorized parties can read the data. A cloud-assisted TMIS is implemented by using the cloud as a storage medium and securing it, as well as the transfer of data. The Automated Validation of Internet Security Protocols and Applications (AVISPA) tool is used to validate the proposed protocol and prove its security.

关键词： Wireless communication Cloud computing Protocols Pandemics Authentication Medical services Medical information systems Body area networks Elliptic curve cryptography Secure storage

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SUSTAINABLE SUPPLIER SELECTION AND ORDER ALLOCATION OF RAW MATERIALS CONSIDERING DEMAND FLUCTUATIONS 51

SUSTAINABLE SUPPLIER SELECTION AND ORDER ALLOCATION OF RAW M...

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51st International Conference on Computers and Industrial engineering, CIE 2024

作者： Sarker, Mohiuddin Sakib, Nazmush Ahmed, Sayem Chakrabortty, Ripon K. Department of Mechanical & Production Engineering Ahsanullah University of Science & Technology Dhaka Bangladesh Department of Industrial Engineering Southern Illinois University EdwardsvilleIL United States School of Systems & Computing University of New South Wales CanberraACT2610 Australia

Industries that face a dynamic market always struggle to find the optimal supplier and ordering policy. The rapid changes in demand also pose significant challenges for decision makers. Thus, to assist the decision-makers with supplier selection and optimal ordering policy under demand uncertainties, a multi-criterion, multi-period, and multi-objective mixed integer linear programming (MILP) model is proposed by considering procurement costs, lead times, inventory, shortage and carbon emission costs. Sales data of a pharmaceutical company is collected, and the future demand is forecasted using the Prophet forecasting model. The output of forecasted demand is then used as the input of the MILP model. Weighted objective, preemptive goal programming and non-preemptive goal programming are used to address the challenge of multi-criteria mathematical programming. The decision makers will be able to choose the best supplier among many suppliers and will also be able to get the ordering policy and inventory policy for each time period. A sensitivity analysis is done to study the impact of inventory factors and shortage factors on the objective values. The proposed framework can be used as a decision-making tool by corporate organizations to assist supply chain managers in assessing supplier selection and order allocation by predicting demand while keeping the sustainability of the environment in mind. © 2024 Computers and Industrial engineering. All rights reserved.

关键词： Sensitivity analysis

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Homomorphic Encryption Enabled SVM for Preserving Privacy of P2P Communication

Homomorphic Encryption Enabled SVM for Preserving Privacy of...

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IEEE Students' Conference on Electrical, Electronics and Computer science (SCEECS)

作者： Sourabh Sahu R. Ganeshan V Muneeswaran School of Computing Science And Engineering VIT Bhopal University Sehore Madhya Pradesh India

In recent years, the protection of our data privacy has become crucial, particularly within the realm of peer-to-peer (P2P) communication networks. we propose a novel homomorphic encryption framework a deep dive into the exploration of cutting-edge methods to ensure the preservation of privacy, placing special emphasis on the effective utilization of homomorphic encryption. Our approach strives to uphold the confidentiality of encrypted data while maintaining functionality, achieved through the integration of defensive encryption, a machine-learning classifier algorithm, and homomorphic encryption. We conduct a thorough comparative analysis both before and after incorporating homomorphic encryption. This entails the implementation of sophisticated encryption algorithms on the Support Vector Machine (SVM) model, covering diverse aspects such as data sources, transmission routes, and individual devices, using the p2p communication threats dataset. The study illuminates the inherent link between strengthening endpoint security and the adoption of homomorphic data encryption, underscoring the significance of network-level privacy controls in the realm of peer-to-peer communication. This research elevates the overall privacy architecture by harnessing Support Vector Machines (SVM) for anomaly detection and predictive analytics. In doing so, it clarifies the fundamental relationship between enhancing endpoint security and embracing homomorphic data encryption. Essentially, this work introduces novel perspectives on reinforcing privacy security within decentralized communication networks.

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CAD-IDS: A Cooperative Adaptive Distributed Intrusion Detection System with Fog computing 25

CAD-IDS: A Cooperative Adaptive Distributed Intrusion Detect...

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25th IEEE International Conference on Computer Supported Cooperative Work in Design, CSCWD 2022

作者： Li, Shuren Lu, Yifei Li, Jingqi Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210096 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214215 China

ISBN: (纸本)9781665405270

Nowadays, the IoT (internet of things) botnet has become a huge threat to network security. In response to this threat, we present a cooperative adaptive network intrusion detection system (IDS) framework with fog computing. The core part is cooperation detection architecture based on online adaptive machine learning algorithms. Our CAD-IDS aims to decrease the new attack detection time for all the IoT nodes and reduce the complexity of the cooperative IDS framework. The results of the experiment indicate that our CAD-IDS can detect IoT attacks efficiently and timely. Our ML code is available in https://***/LiQianchang/CAD-IDS-ML-Code. © 2022 IEEE.

关键词： Internet of things

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Actual Facial Mask Recognition Utilizing YOLOv3 and Regions with Convolutional Neural Networks 1

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2nd International Conference on Emerging Trends in Expert Applications and Security, ICETEAS 2023

作者： Thilagavathy, A. Naveen Raju, D. Priyanka, S. RamBalaji, G. Gopirajan, P.V. Sureshkumar, K. Department of Computer Science and Engineering R.M.K. Engineering College Kavaraipettai India Department of Computational Intelligence School of Computing SRM Institute of Science and Technology Kattankulathur Campus Chennai603203 India Department of Information Technology Saveetha Engineering College Chennai India

ISBN: (数字)9789819919093

ISBN: (纸本)9789819919086

With the Coronavirus Disease (COVID-19) outbreak in 2019, it appears as though time has stopped. The World Health Organization (WHO) determined that wearing a face mask was essential for limiting virus transmission to combat pathogen transmission. Nevertheless, physically choosing how often individuals wear face masks in public is a momentary operation. The need to track people using respirators required creating an automated process. Nowadays, different deep and machine learning techniques can be used productively. All of the requirements for this type of model have been addressed in this work. The necessity and architectural layout of the suggested method have been thoroughly examined, followed by a rigorous inspection of several methodologies and their related comparison analyses. We investigate optimum settings for the sequential convolutional neural network model to accurately identify the existence of masks avoiding overfitting. Face identification categorically deals with distinguishing a specific group of entities, i.e., face. It has numerous applications, such as autonomous driving, education, surveillance. The prevalent practice reconfigures the images before fitting them into the network to surmount the inhibition. Some face collections are head turn, tilt, and slant with multiple faces in the frame and different types of masks with different colors. The proposed method consists of a cascade classifier and a pre-trained Convolutional Neural Network (CNN), which contains two 2D convolution layers connected to layers of dense neurons. As grayscale rationalizes the algorithm and diminishes the computational requisites, it is utilized for extracting descriptors instead of working on color images instantaneously. It has categorical representation, and the data is converted to categorical labels. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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Multimodal Deep Learning with XAI for Transparent and Accurate Doctor Prescription Recognition

Multimodal Deep Learning with XAI for Transparent and Accura...

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Emerging Research in Computational science (ICERCS), International Conference on

作者： Sunil Reddy J Tenzin Sungrab Norbu Pavan Kumar Praveen Vitthal Ajjanakatt Reddyam Harsha U Ananthanagu Department of Computer Science and Engineering Alliance School of Advanced Computing Alliance University India

ISBN: (数字)9798331534967

ISBN: (纸本)9798331534974

This paper presents a deep learning framework to automate recognition of medical prescriptions by using handwritten content, feature extraction from CNNs like VGG16, Alex Net, and MobileNetV2, and sequential data utilizing LSTMs. A multi-modal approach captures even the spatial and temporal details in a prescription, which assists in an accurate interpretation of complex handwriting. Specifically, key pre- processing like grayscale conversion, normalization and edge detection improving the quality of the image, reducing noise and bringing forth the prominent features that aid in the improvement of recognition accuracy and also implementation of XAI concepts to further enhance the interpretation. This model reduces transcriptions, making it easier for the patients as well as the practitioners to understand the handwriting through the computer screen. The method therefore assists in safer patient-centered healthcare by automating prescription processing thus triggering clearer communication towards more informed healthcare decisions.

关键词： Deep learning Handwriting recognition Accuracy Text recognition Computational modeling Medical services Predictive models Feature extraction Transformers Biomedical imaging

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Predicting Cancer Types from Gene Expression Data Using Machine Learning

Predicting Cancer Types from Gene Expression Data Using Mach...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Gayathri B Nair Nanditha Nandakumar Remya S Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Genomic analysis has emerged as an essential step in biological and medical research which has accelerated with the incorporation of ML techniques allowing so much more scope in the analysis of complicated genetic information. With the vast amount and the high complexity of genomic information, it is easy to see how ML can be of assistance. Over algorithms being able to identify the structure of genetic polymorphisms, categorize them, and model phenotype features, ML provides an avenue to unlock the workings of genes, their related diseases and individual differences. This paper incorporates various supervised learning algorithms, including Logistic Regression, Support Vector Machine (SVM) and Random Forest, for genomic analysis on the Gene Expression Cancer RNA-Seq dataset, which includes approximately 1000 samples. The highest accuracy of 98.22 percentage was attained by logistic regression.

关键词： Support vector machines Logistic regression Accuracy Supervised learning Genomics Ensemble learning Gene expression Bioinformatics Random forests Cancer

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BOBBLE: A Mixed Routing-Granularity Distributed Load Balancing for Data Center Networks 23

BOBBLE: A Mixed Routing-Granularity Distributed Load Balanci...

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23rd IEEE International Conference on High Performance computing and Communications, 7th IEEE International Conference on Data science and Systems, 19th IEEE International Conference on Smart City and 7th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Xu, Zhengzhi Lu, Yifei Ma, Xu School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing210096 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214215 China

ISBN: (纸本)9781665494571

In current Datacenter Networks (DCNs), Equal-Cost Multipath (ECMP) is a default load balancing scheme. However, using ECMP may result in rapid growth of Flow Completion Time (FCT) due to its well-known drawbacks. In order to solve the problem of ECMP, load balancing schemes with per-packet granularity (such as RPS) and per-flowlet granularity (such as LetFlow) are proposed. These two different granularities have their own features, but there's no existing load balancing considering both their advantages. In this paper, we mix per-flowlet and per-packet in scheduling decisions and introduce the potential performance bottleneck in mixed granularities load balancing. Based on this idea, we propose a hybrid scheme for load balancing, referred to as BOBBLE. The key idea of BOBBLE is to distinguish elephant and mouse flows and schedule them with different granularity. We evaluate BOBBLE in simulations with NS-3. Experiment shows that BOBBLE slightly sacrifices elephant flows FCT but it is worthy because our results demonstrate that BOBBLE is a good trade-off which outperforms RPS and LetFlow in mouse flows FCT in both symmetric and asymmetric topology. © 2021 IEEE.

关键词： Data centers Network topology Smart cities Load management Routing Mice Computer networks

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ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-OBJECTIVE LEARNING 12

ADAPTIVE STOCHASTIC GRADIENT ALGORITHM FOR BLACK-BOX MULTI-O...

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12th International Conference on Learning Representations, ICLR 2024

作者： Ye, Feiyang Lyu, Yueming Wang, Xuehao Zhang, Yu Tsang, Ivor W. Department of Computer Science and Engineering Southern University of Science and Technology China Australian Artificial Intelligence Institute University of Technology Sydney Australia Centre for Frontier AI Research Agency for Science Technology and Research Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Shanghai Artificial Intelligence Laboratory China School of Computer Science and Engineering Nanyang Technological University Singapore

Multi-objective optimization (MOO) has become an influential framework for various machine learning problems, including reinforcement learning and multitask learning. In this paper, we study the black-box multi-objective optimization problem, where we aim to optimize multiple potentially conflicting objectives with function queries only. To address this challenging problem and find a Pareto optimal solution or the Pareto stationary solution, we propose a novel adaptive stochastic gradient algorithm for black-box MOO, called ASMG. Specifically, we use the stochastic gradient approximation method to obtain the gradient for the distribution parameters of the Gaussian smoothed MOO with function queries only. Subsequently, an adaptive weight is employed to aggregate all stochastic gradients to optimize all objective functions effectively. Theoretically, we explicitly provide the connection between the original MOO problem and the corresponding Gaussian smoothed MOO problem and prove the convergence rate for the proposed ASMG algorithm in both convex and non-convex scenarios. Empirically, the proposed ASMG method achieves competitive performance on multiple numerical benchmark problems. Additionally, the state-of-the-art performance on the black-box multi-task learning problem demonstrates the effectiveness of the proposed ASMG method. © 2024 12th International Conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Multiobjective optimization

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