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检索条件"任意字段=5th International Conference on Data Science, Machine Learning and Applications, ICDSMLA 2023"

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A Comparative Analysis of ResNet and MobileNet for Classifying MRI Images 5th

A Comparative Analysis of ResNet and MobileNet for Classifyi...

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5th international conference on data science, machine learning and applications

作者： Padmaja, D. Lakshmi Nikhil, B. Akshaya, Banda Sai Deepak, G. Surya Anurag Univ Hyderabad India North Carolina A&T State Univ Greensboro NC USA Cognizant Technol Solut Chennai India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

this work represents a comprehensive analysis of the performance of two popular deep learning architectures, ResNet and MobileNet, with particular attention to their use in the classification of magnetic resonance imaging (MRI) pictures. Healthcare professionals need to accurately classify medical images in order to make precise diagnosis and develop successful treatment plans. In this paper authors have done thorough comparative research to clarify the quantitative performance indicators while also exploring qualitative elements, such as the subtle differences between each model's strengths and weaknesses. Beyond the technical assessment, the study investigates ResNet's and MobileNet's computational effectiveness and flexibility in response to the various features present in medical imaging data. the project aims to provide a sophisticated understanding of these deep learning systems in order to make a significant addition to the medical image analysis field as a whole. the ultimate goal is to promote improvements in diagnostic accuracy, which will enable healthcare providers to make better judgments and provide better patient care. the results of this study will be crucial in determining the direction of future advancements in this important field as deep learning and medical imaging continue to cross paths. they provide insightful information that goes beyond ResNet and MobileNet to affect the larger field of deep learning applications in medical diagnostics and treatment planning. the aforementioned study highlights the profound potential of deep learning technology to enhance healthcare procedures and further advance medical science.

关键词： ResNet MobileNet Advancements Deep learning Algorithms Computer Vision

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Technical Concepts on Cloud Security and Privacy Using Deep learning Techniques and Hybridized Encryption 5th

Technical Concepts on Cloud Security and Privacy Using Deep ...

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5th international conference on data science, machine learning and applications

作者： Rajchandar, K. Madanan, Mukesh Sastry, R. V. L. S. N. Saxena, Monika SR Univ Sch CS & AI Warangal Telangana India Dhofar Univ Dept Comp Sci Salalah Oman GIET Univ Sch Comp Sci & Engn Gunupur Orisha India Banasthali Vidyapith Dept Comp Sci Banasthali India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the current research questions that are raising concern security in cloud computing. Cloud storage is replacing traditional data storage in numerous companies because it provides instant knowledge of data from any location. However, the main problem stopping businesses from utilizing cloud computing is data security. Cloud computing has been one of the most talked about subjects in technology lately due to its capacity to manage a large number of resources and data. One of the biggest obstacles is scheduling tasks, and inefficient management leads to performance decline. this paper presents a unique method for productive task scheduling with improved security in a cloud computing environment. It is suggested to design the operations using a unique Convolutional neural network optimized modified butterfly optimization (CNN-MBO) approach in order to improve efficiency and shorten construction time. Second, the data is encrypted using a hybridized RSA method, ensuring safe data transfer. the performance of our suggested technique is then evaluated by simulating it using a cloudlet simulator and analyzing the evaluation findings. the suggested method is also contrasted with alternative task scheduling-based methods for a number of performance criteria, including resource usage, reaction time, and energy consumption.

关键词： Deep learning techniques cloud security hybridized encryption privacy

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Automated Segmentation and Classification of Magnetic Resonance Imaging Modalities for Multiple Sclerosis Diagnosis on Employing Deep learning Frameworks: A Critical Review 5th

Automated Segmentation and Classification of Magnetic Resona...

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5th international conference on data science, machine learning and applications

作者： Ramya, Palaniappan Siva, R. SRM Inst Sci & Technol Dept Computat Intelligence Chennai Tamil Nadu India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Multiple Sclerosis (MS) is a long-term autoimmune disease that disrupts the brain and central nervous system of people resulting in vision, sensory, and motor problems, that eventually ends up in nerve deterioration or permanent physical inability if untreated. To detect MS, several screening procedures have been recommended, the most popular amongst them is Magnetic Resonance Imaging (MRI). MRI modalities assist physicians in getting accurate details of the structure and operational activity of the brain, which will be crucial for detecting and treating MS in an early stage. Manual diagnosis of MS utilizing MRI modalities is a tedious, time consuming process and is susceptible to misconception. Deep learning Based Diagnosis Systems (DBDS) employs Artificial Intelligence to diagnose MS by employing several Deep learning techniques. In Deep learning (DL) frameworks, feature selection and extraction are done automatically by the designed layers which can automatically learn and extract the required features from the MRI modality. In this paper, a critical study of previous research works for diagnosing MS by employing several DBDS is discussed. the several types of pre-processing methods for MRI neuroimaging modalities were discussed. the various steps involved in the processing of MRI modalities by several DBDS models for MRI segmentation and classification are analyzed. Furthermore, the major challenges and future research opportunities in engaging DL architectures for MS diagnosis from MRI modalities are discussed.

关键词： Multiple Sclerosis Deep learning Healthcare MRI Neuroimaging

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A Comprehensive Review on Bio-Inspired Optimization Techniques 5th

A Comprehensive Review on Bio-Inspired Optimization Techniqu...

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5th international conference on data science, machine learning and applications

作者： Nimmala, Satyanarayana Samya, Banoth Reddy, Jakkala Ravichandra Ramana, T. Venkata Ch, Ravikumar CVR Coll Engn Dept CSE DS Hyderabad Telangana India CVR Coll Engn Dept Comp Sci & Engn Hyderabad Telangana India Vijay Rural Engn Coll Dept CSE Nizamabad Telangana India CVR Coll Engn Dept Comp Sci & Engn AIML Hyderabad Telangana India Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the concept of optimization has gained widespread recognition in numerous industries worldwide. To maximize resources like time, money, distance, options, and searches, people and organizations are actively looking for the best answers to particular problems. the traditional methods of tackling optimization problems are no longer considered adequate since they frequently produce suboptimal results or take too long. Using heuristic and meta-heuristic methods is now necessary to solve optimization problems. To avoid becoming trapped in locally optimal solutions, meta-heuristic approaches are made to thoroughly investigate and utilize the entire search space. Meta-heuristic algorithms are remarkably adept at solving optimization issues;they take their cues from the behavior of real organisms such as fish, birds, insects, and bees. In almost every modern field, optimization is a crucial factor to take into account while attempting to resolve real-world issues. An in-depth analysis of bio-inspired optimization methods and their real-world applications for a variety of optimization problems is presented in this paper.

关键词： Optimization Heuristic methods Meta-heuristic methods Trade-off Bio-inspired optimization

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E2M: Emerge Energy Management Framework for Multimedia data Transmission in WMSNs 5th

E2M: Emerge Energy Management Framework for Multimedia Data ...

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5th international conference on data science, machine learning and applications

作者： Janakamma, Ch. Hegde, Nagaratna P. Osmania Univ Hyderabad India Vasavi Coll Engn Hyderabad India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Wireless Multimedia Sensor Networks (WMSN) are autonomous, limited energy and distributed multimedia sensor which can transmit the multimedia data through communication networks. In WMSN, sensor nodes sense data and capture multimedia data. the WMSN deployed in many applications such as surveillance, healthcare, military applications and etc., the enhancement of high speed communication in WMSN is increasing demand for multimedia services and applications. the WMSN mostly used for transmission of multimedia services such as audio and video. the volume of multimedia data is increase vastly as well as utilization of bandwidth and energy also increase. the limited energy WMSN exhaust rapidly and its leads to network failure. Previous extensive research carried out to maximize throughput by the optimal utilization of network bandwidth. Also improved QoS by the novel routing algorithm in WMSN. To address the limitation of existing frameworks, in this research paper proposed an emerging energy management framework to reduce energy utilization and improve network life time. the proposed E2M framework implementation is made using NS2. the empirical results shown that proposed framework outperforms with comparison of existing frameworks. the performance evaluated in terms of PDR, Delay, throughput and Energy. the proposed E2M Framework reduce 5.74 ms of network delay, improved 5.3% of PDR, throughput improved to 77KB and reduce energy consumption of 0.39 kJ.

关键词： Multimedia Application Energy Efficient Network Lifetime Energy Management Quality of Service (QoS)

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Skin Disease Detection 5th

Skin Disease Detection

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5th international conference on data science, machine learning and applications

作者： Janokar, Sagar Joshi, Kalpesh Raikwar, Rajesh Kumbhar, Sahil Kumbhar, Akshay Kumbhar, Shashank Vishwakarma Inst Technol Elect & Telecommun Engn Pune Maharashtra India Vishwakarma Inst Technol Dept Engn Sci & Humanities Pune Maharashtra India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Dermatology is a specialized field of biology focused on diagnosing and treating skin conditions. the skin's diverse range of conditions can be influenced by factors like temperature, humidity, and climatic conditions, leading to regional and seasonal variations. However, the unique characteristics of human skin, such as unevenness, tone, and hairiness, make it challenging to mechanically synthesize and assess accurately. While some studies have used computer vision techniques to identify and simulate skin diseases, only a few have addressed the medical paradigm of the problem. In many remote areas, limited access to medical services causes patients to disregard early symptoms, potentially exacerbating their condition over time. As a result, an elevated demand is emerging for precise automated techniques to identify skin conditions. We developed a deep learning model capable of differentiating between normal skin and skin with ailments to address this requirement. Furthermore, our model is capable of categorizing significant varieties of skin conditions, including melanocytic nevi, melanoma, benign keratoses-like lesions, basal cell carcinoma, actinic keratoses, vascular lesions, and dermatofibroma. A subclass of machine learning a deep learning, played a crucial role in the development of our model. By utilizing large datasets, deep learning significantly reduced the number of classifiers compared to traditional machine learning techniques. Convolutional neural network (CNN) algorithm, commonly employed for image categorization, was the backbone of our model. through this research, we aim to promote and support the expansion of dermatology by providing a fast and accurate tool for skin disease diagnosis and classification.

关键词： Convolutional Neural Network (CNN) Skin Disease Deep learning Training Testing and Evaluation Dermatoscopic pictures

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Design and Development of Traffic VANETs for Safety and Accident Avoidance 5th

Design and Development of Traffic VANETs for Safety and Acci...

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5th international conference on data science, machine learning and applications

作者： Kadiayla, Ramana Tanam, Aparna Manvita, K. Chaitanya Bharathi Inst Technol Dept Artificial Intelligence & Data Sci Hyderabad 500075 Telangana India G Narayanamma Inst Technol & Sci Dept Informat Technol Hyderabad 500104 Telangana India G Narayanamma Inst Technol & Sci Dept Comp Sci & Technol Hyderabad 500104 Telangana India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Vehicular ad-hoc networks (VANETs) are one of the recent interests in research industry that have advances in wireless communication and networking models. the ultimate goal of the VANETS is to enable the authorized messaging services towards the vehicles and public networks. To reduce the complexity of the service authorization, it uses proxy vehicles to reduce the complex computational problems. During the authentication of proxy vehicles, it sends multiple messages to frequently get response from the road side units in a more effective way. In the proposed system, proxy based unique authentication scheme is discussed where it cannot provide message authenticity and also it will not resist the mediate attacks. Sometimes, the system accepts the false signatures. Secondly, we proposed a unique identity-based authentication scheme using latest proxy vehicles (ID-MAP). To reconfirm the authentication assurance, the signature is adaptively verified against the chosen message. A robust authentication model is evaluated using the Elliptic Curve discrete Logarithm problem represented as (ECDLP) in the system oracle model.

关键词： Vehicular Ad-hoc Networks Secure Network Wireless Network technologies Safety Accident Avoidance

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An Overview of Automatic Speech Recognition Based on Deep learning and Bio-Signal Sensors 5th

An Overview of Automatic Speech Recognition Based on Deep Le...

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5th international conference on data science, machine learning and applications

作者： Venkatesh, N. Krishna, K. Sai Geetha, M. P. Dave, Megha R. Kapila, Dhiraj SR Univ Sch CS & AI Warangal Telangana India Anurag Univ Elect & Commun Engn Hyderabad 500072 Telangana India Sri Eshwar Coll Engn Artificial Intelligence & Data Sci Coimbatore Tamil Nadu India Sankalchand Patel Univ Comp Engn Swami Sachchidanand Polytech Coll Visnagar 38431 Gujarat India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

the process of producing speech is complex and includes a number of biosignals in addition to acoustics. In order to overcome the limitations of conventional speech processing in particular and to gain a better understanding of the process of speech creation in general, these biosignals can be used. they originate from the articulators, the movement of the articulator muscles, the connections within the brain, and the brain itself. With an emphasis on speech production, recognition, and volitional control, we discuss artificial mouth techniques in this review that make use of a variety of sensors, including gyros, images, 3-axial magnetic sensors, electromyography as EMG, electroencephalography as EEG, electropalatography as EPG, electromagnetic articulography as EMA, permanent magnet articulography as PMA, and articulator electromyography. Before classifying them into taxonomy, we evaluate the flow of several voice recognitionrelated deep learning technologies, including visual speech recognition and silent speech interface. We conclude by talking about ways to address the communication issues that persons with speech impairments face as well as upcoming deep learning research.

关键词： Bio signals deep learning speech recognition EMG EMA

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Development of Enhance-Net Deep learning Approach for Performance Boosting on Medical Images 5th

Development of Enhance-Net Deep Learning Approach for Perfor...

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5th international conference on data science, machine learning and applications

作者： Manoharan, Geetha Solanke, D. R. Acharjee, Purnendu Bikash Nayak, Chinmaya Kumar Sharma, Mukesh Kumar Sahu, Dillip Narayan SR Univ Sch Business Warangal Telangana India SGB Amravati Univ Dept Appl Elect Amravati 444602 Maharashtra India CHRIST Univ Pune Maharashtra India Sri Sri Univ Fac Emerging Technol Cuttack Odisha India Rungta Coll Pharmaceut Sci & Res Bhilai 490024 Chhattisgarh India Gangadhar Meher Univ Dept MCA Sambalpur Odisha India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

Only a few clinical procedures include the use of clinical methods for the early detection, observing, evaluation, and treatment evaluation of a range of medical illnesses. Knowing the analysis of medical images in computer vision necessitates being acquainted with the core concepts and uses of deep learning and artificial neural networks. the A rapidly expanding area of study is the Deep learning Approach (DLA) in medical image processing. DLA is often used in medical imaging to determine if an ailment is present or not. By producing speedier, more accurate results in real time, deep learning algorithms may make the jobs of radiologists and orthopaedic surgeons easier. But the standard deep learning approach has reached its efficiencies. While offering an ideal solution known as boost-Net, we study numerous optimization strategies to increase the effectiveness of deep neural networks in this research. From a selection of well-known deep learning models, Champion-Net was selected as the deep learning model. the musculoskeletal radiograph-bone classification (MURA-BC) dataset is used in this investigation. Utilizing the train and test datasets, Enhance-Net's classification precision was evaluated.

关键词： Deep learning Medical image Enhance- net dataset

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Implementation of Recent Advancements in Cyber Security Practices and Laws in India 5th

Implementation of Recent Advancements in Cyber Security Prac...

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5th international conference on data science, machine learning and applications

作者： Kumar, Bura Vijay Singh, Manvendra Arora, Vaishali Malik, Khushboo Nomani, M. Z. M. Kapila, Dhiraj SR Univ Sch Comp Sci & Artificial Intelligence Warangal Telangana India Sharda Univ Sharda Sch Law Greater Noida India CHRIST Dept Law Delhi NCR Campus Ghaziabad India Aligarh Muslim Univ Fac Law Aligarh 202001 Uttar Pradesh India Lovely Profess Univ Dept Comp Sci & Engn Phagwara Punjab India

ISBN: (纸本)9789819780334;9789819780310;9789819780303

In the past few decades, a large number of scholars and experts have found that wireless connectivity technologies and systems are susceptible to many kinds of cyber attacks. Both governmental organizations and private firms are harmed by these attacks. Cybersecurity law is a complex and fascinating area of law in the age of information technology. this essay aims to outline numerous cyber hazards as well as ways to safeguard against them. In both local and international economic contexts, it is critical to establish robust regulatory and legal structures that address the growing concerns about fraud on the internet, security of information, and intellectual property protection. Additionally, it covers cybercrime's different manifestations and security in a global perspective. Due to recent technical breakthroughs and a growth in access to the internet, cyber security is now utilized to safeguard not just a person's workstation but also their own mobile devices, including tablets and mobile phones, that have grown into crucial tools for data transmission. the community of security researchers, which includes members from government, academia, and industry, must collaborate in order to comprehend the new risks facing the computer industry.

关键词： Cyber security law cyber crime data protection information technology

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