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2nd International Conference on Computing Advancements: Age of Computing and Augmented Life, ICCA 2022

作者： Hasan, Md Imran Mahbub, Nosin Ibna Sarkar, Bappa Dept. of Computer Science and Engineering Islamic University Kushtia Bangladesh Dept. of Information and Communication Technology Islamic University Kushtia Bangladesh

ISBN: (纸本)9781450397346

Black Fungus is a dangerous fungal illness, commonly referred to as 'mucormycosis', usually infecting uncompromising people. Mucormycosis is generally rare, affecting less than two individuals per million each year, but it is currently 80 times more frequent in India. People of all ages, including prematurity infants, may be impacted. To minimize the time and effort required by medical experts to investigate black fungus disease and enhance the consistency of image identification of black fungus infections, we suggested a transfer learning based CNN model as the dataset for the black fungal disease is limited. The experimental outcomes of several different architectures, including VGG16, InceptionV3, and Xception, are compared and analyzed in this study, and it is observed that Xception has the best performance, with 97.92% training accuracy and 95.60% testing accuracy. © 2022 ACM.

关键词： Image enhancement

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Artificial Intelligence for Green Agriculture and Sustainable Resource Management: A Comprehensive Overview

Artificial Intelligence for Green Agriculture and Sustainabl...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Eman Alduweib Waseem Alromema Essa Hezzam Dept. of Computer Science University of Petra Amman Jordan Dept. of Computer Science and Information Applied College Taibah University Medina Saudi Arabia Dept. of Information Systems College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Artificial Intelligence (AI) is a revolutionary technology disrupting the process of finding solutions for various sustainability challenges. Specifically, AI is having a significant influence on many sectors, especially agriculture, energy, waste management, and biodiversity conservation. This paper offers a comprehensive overview of AI's applications in sustainable resource management, with a special focus on green agriculture as a case study, in which the application of AI greatly enhances the efficiency of resource use, crop yield, and environmental conservation efforts. This overview describes the potential application of AI to address performance concerns (ethical value, energy use, and data quality deficiency), and proposes the future research directions such as the explainable AI, circular economies, and the integration of AI with the Internet of Things (IoT) to guide researchers and policymakers in exploiting AI to enhance sustainability in key domains.

关键词： Waste management Ethics Explainable AI Data integrity Transforms Resource management Internet of Things Biodiversity Artificial intelligence Sustainable development

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The ORIS tool: App, library, and toolkit for quantitative evaluation of non-Markovian systems

The ORIS tool: App, library, and toolkit for quantitative ev...

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作者： Carnevali, Laura Paolieri, Marco Vicario, Enrico Information Engineering Dept. Univ. Of Florence Italy Computer Science Dept. Univ. Of Southern California United States

ORIS is a tool for quantitative modeling and evaluation of concurrent systems with non-Markovian durations. It provides a Graphical User Interface (GUI) for model specification as Stochastic Time Petri Nets (STPNs), validation by interactive simulation, and evaluation by several techniques, computing instantaneous and cumulative rewards. It also provides an open-source Java Application Programming Interface (API) to automate the workflow, and it can be used as a toolkit for derivation and evaluation of STPNs in model driven engineering. As distinguishing features, ORIS implements transient and steady-state analysis of STPNs with underlying Markov Regenerative Process (MRP), and transient analysis of STPNs with underlying Generalized Semi-Markov Process (GSMP). It also implements nondeterministic analysis of Time Petri Nets (TPNs), simulation of STPNs, and solution methods for Continuous-Time Markov Chains (CTMCs) and MRPs with at most one non-exponential timer in each state. The well-engineered software architecture of ORIS supports agile implementation of new STPN features, new modeling formalisms, and new analysis methods. © 2022 is held by the owner/author(s).

关键词： Stochastic systems

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Improved Hierarchical M-Net+ for Blind Image Denoising

Improved Hierarchical M-Net+ for Blind Image Denoising

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2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

作者： Fan, Chi-Mao Liu, Tsung-Jung Liu, Kuan-Hsien National Chung Hsing University Dept. of Electrical Engineering and Graduate Institute of Communication Engineering Taiwan National Taichung University of Science and Technology Dept. of Computer Science and Information Engineering Taiwan

ISBN: (数字)9781665470506

ISBN: (纸本)9781665470506

Image denoising is a long standing ill-posed prob-lem. Recently, the convolution neural networks (CNNs) gradually stand in the spotlight and almost dominated the computer vision field and had achieved impressive results in different levels of vision tasks. One of famous hierarchical CNN-backbones is the U-Net which shows awesome performance in both denoising and other areas of computer vision. However, the hierarchical architecture usually suffers from the loss of spatial information due to the repeated sampling. It seriously affects the denoising performance especially the element-wise task like denoising. In this paper, we proposed an improved hierarchical backbone: M-Net+ for image denoising to ameliorate the loss of spatial details. Furthermore, we test on two synthetic Gaussian noise datasets to demonstrate the competitive result of our model. © 2022 IEEE.

关键词： Image denoising

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Position Aware 60 GHz mmWave Beamforming for V2V Communications Utilizing Deep Learning

Position Aware 60 GHz mmWave Beamforming for V2V Communicati...

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IEEE International Conference on Communications (ICC)

作者： Muhammad Baqer Mollah Honggang Wang Hua Fang Dept. of Electrical and Computer Engineering University of Massachusetts Dartmouth MA Dept. of Graduate Computer Science and Engineering Yeshiva University NY Dept. of Computer and Information Science University of Massachusetts Dartmouth MA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

Beamforming techniques are essential to compensate for severe path loss in millimeter-wave (mmWave) communications. These techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over such narrow beams for efficient link configuration by traditional beam selection approaches, mainly relied on channel state information and exhaustive search, typically impose significant latency and computing overheads, which is often infeasible in vehicle-to-vehicle (V2V) communications like highly dynamic scenarios. In contrast, utilizing out-of-band contextual information, such as vehicular position information, is a potential alternative to reduce such overheads. This paper proposes a solution that utilizes deep learning to predict the optimal beams for vehicular communication at 60 GHz. By analyzing vehicular position information, the solution can identify the beams that provide sufficient mmWave received powers, ensuring the best line-of-sight links for vehicle-to-vehicle (V2V) communications. The proposed solution was tested on real-world measured mmWave sensing and communication datasets, and the results show that it can achieve an average of 84.58% of received power of link status, making it a promising solution for beamforming in mmWave enabled V2V communications.

关键词： Deep learning Power measurement Array signal processing Vehicular ad hoc networks Receiving antennas Predictive models Sensors

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Analysis of Agricultural Toolset based on Artificial Intelligence 11

Analysis of Agricultural Toolset based on Artificial Intelli...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Bajpai, Yashi Srivastva, Madhavi Singh, Trilok Chauhan, Vineet Kumar Upadhyay, Diwakar Dixit, Abhishek Invertis University Dept. of Computer Science and Engg. Bareilly India Ritm Dept. of Computer Science and Engg. Lucknow India Tmu Dept. of Computer Science and Engg. Moradabad India Vardhaman College of Engineering Dept. of Information Technology Hyderabad India

ISBN: (纸本)9781665487320

One of the industries that are most crucial to humanity is agriculture. Agriculture mechanization is the major issue facing all countries today. As the world's population is expanding at an incredibly fast rate, there is an increasing demand for food. To fulfill the expanding demand, farmers will need to apply chemical pesticides more often than they already do. The soil is harmed by this. The land continues to be unproductive and barren as a result of this having a substantial influence on agricultural activities. Several mechanization strategies, including deep learning, machine learning, and artificial intelligence, are covered in this article. It is crucial to use new technologies at various stages of the agro-based supply chain due to several long-term challenges for the agricultural industry and various factors, such as population growth, global warming, technological advancement, and the condition of environmental assets (water, etc.). Examples include automated farm equipment processes, the use of sensing devices and satellite data for distant locations, artificial intelligence, and machine learning for forecasting weather patterns. Crop diseases, inadequate storage management, chemical usage, weed control, insufficient irrigation, and poor water management are just a few problems the agricultural sector is facing. Using the range of strategies covered, each of these problems might be handled. It has been demonstrated that automating farming procedures increases soil productivity and improves soil fertility. To get a quick overview of how automation is currently being used in agriculture, this paper examines the work of numerous researchers. In the current study, we highlight the key uses of AI and Ml techniques in farming and highlight the undeniably rising trend in the implementation of these techniques to advance the agriculture sector. © 2022 IEEE.

关键词： Farms

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Empowering Learning: An Integrated Approach to Enhance Education

Empowering Learning: An Integrated Approach to Enhance Educa...

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Sharma, Kunal Puneet Deepika Jindal, Pardeep Kumar Sharma, Preeti Indraprastha Institute of Information Technology Dept. of Computer Science and Design Delhi India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Dept. of CSE Mohali140307 India Chandigarh University Dept. of CSE Mohali India Chandigarh Engineering College Chandigarh Group of Colleges Jhanjeri Dept. of ECE Mohali140307 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India

ISBN: (纸本)9798350378092

Programming has become one of the most essential skills in virtually every industry. However, we have access to numerous resources and platforms for learning and development, but may still find ourselves inadequately prepared in this field, not reaching the level of proficiency as aspire to achieve. This has increased the demand for a dedicated phase that is allotted uncommonly for the students to support a coding society among them, right from the beginning. The initiative that is planned to settle this specific issue will also help students to overcome their limitations through continuous data-driven learning. The inevitable outcome will be a platform ALGAR which can be utilized by the students and faculty members to do any kind of code and estimation by using different programming languages like C, C++, Java, Python, etc. Moreover, they can also run their code independently inside an integrated compiler, eliminating the need for an external compiler. Along with this, the comparison of the suggested platform features with the various other existing education platforms like GitHub, Leet Code, Code Chef etc., is also discussed to empower the learning. © 2024 IEEE.

关键词： Students

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Effective Disease Recognition in Cucumbers: A Web-Based Application Using Transfer Learning Models 3

Effective Disease Recognition in Cucumbers: A Web-Based Appl...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Rahman, Md Shahidur Ahamed, Asif Pranto, Md Nuruzzaman Islam, Md Ariful Al Masum, Abdullah Al-Sakib, Abdullah Rahman, S.M. Arafat Haque, Rezaul Rahman, Shafiur Westcliff University Dept. of Engineering Management CA92614 United States Westcliff University College of Technology and Engineering CA92614 United States International American University Dept. of Business Administration CA90010 United States Westcliff University Dept. of Information Technology CA92614 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Cucumber farming plays a crucial role in Bangladesh's agricultural economy, significantly contributing to vegetable production. However, diseases like Downy Mildew, Bacterial Wilt, Anthracnose, and Belly Rot threaten crop quality and lead to financial losses. Effective management of these diseases is crucial for maintaining high crop yields and ensuring economic stability. Current disease identification methods are labor-intensive, costly, and dependent on expert knowledge. To address these challenges, this study presents an automated disease categorization system using advanced transfer learning models, including MobileNet, ShuffleNet, InceptionResNet-v2, AlexNet, and Ensemble Transfer Learning (ETL). Utilizing a dataset of 8,000 well-annotated images, enhanced through preprocessing techniques like GLRM feature extraction and InfoMAX-GAN feature selection, the ETL model achieved a remarkable accuracy of 99.79% and an F1-score of 99.78%. We also developed a user-friendly web tool for real-time diagnostics, providing invaluable support to farmers and agricultural specialists. This tool enables rapid disease detection, leading to increased productivity and a transformative approach to cucumber disease management. By reducing the reliance on chemical interventions, it promotes sustainable farming practices and safeguards human and animal health. © 2024 IEEE.

关键词： Feature Selection

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Enhancing Health Product Traceability on the Blockchain: A Novel Approach for Supply Chain Management inspection to AI

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EAI Endorsed Transactions on Pervasive Health and Technology 2024年 10卷

作者： Prashanth, Mallellu Sai Uma Maheswari, V. Aluvalu, Rajinikanth Prasad Kantipudi, M.V.V. Dept. of Computer Science and Engineering Vardhaman College of Engineering Hyderabad India Dept. of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Dept. of Information Technology Chaitanya Bharathi Institute of Technology Hyderabad India Pune India

INTRODUCTION: Blockchain technology is being investigated as a viable solution due to the industry's growing requirement for accountability and traceability. This study describes a fresh method for tracking down medical products that makes use of a decentralised smart contract network set up on the Ethereum blockchain. In order to enable secure and auditable tracking of health products throughout their lifecycle, the suggested system, named "HealthProductTraceability," makes use of the transparency and immutability of blockchain. OBJECTIVES: The system uses a "Product" struct to hold pertinent data such the product name, batch number, temperature, producer, and distributors. To quickly get product information depending on the batch number, a mapping is used. The use of tools to manufacture items, send them to distributors, and market them is one significant contribution of this *** demanding validation tests, such as verifying that batch numbers are unique and exist before carrying out certain activities, these functions protect the integrity of the traceability system. METHODS: In order to enable interested parties to track the product's travel and temperature changes, the system additionally emits events for product manufacture, distribution, and temperature adjustments. The suggested system is innovative because it can track the temperature of health items from beginning to end on a decentralised, open platform. RESULTS: By utilising blockchain technology, the system lessens reliance on centralised authorities, fosters stakeholder trust, and minimises the likelihood of fraud, forgery, and tampering in the supply chain for health products. The contract's architecture recognises some of the issues with blockchain technology, including scalability and privacy. By investigating solutions like sidechains, off-chain transactions, and enhancements to consensus methods, scalability issues are solved. CONCLUSION: In summary, the suggested HealthProductTraceability

关键词： Smart contract

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A Construction of Three Party Post Quantum Secure Authenticated Key Exchange for Mobile Users 14

A Construction of Three Party Post Quantum Secure Authentica...

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14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023

作者： Kumar, Uddeshaya Pal, Yogesh Nikhil, Perla Sai Garg, Manish Kumar, M. Ranjith Chaudhary, Dharminder The Lnm Institute of Information Technology Dept. of Mathematics Jaipur India Noida India Amrita Vishwa Vidyapeetham Amrita School of Computing Dept. of Computer Science & Engineering Chennai India Amrita Vishwa Vidyapeetham Amrita School of Engineering Dept. of Mathematics Chennai India

ISBN: (纸本)9798350335095

A three-party authenticated key exchange protocol enables two entities to agree on a session key with the help of a dedicated server through an insecure channel. Lattice based cryptography plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. However, it is not easy to design quantum resistant password based three-party protocol due to the high demand for security requirements and the limited resources nature of mobile devices. In this article, we have proposed a new post quantum three party key exchange based on a variant of lattice assumption, the ring learning errors. The protocol ensures security against impersonation attack, stolen smartcard attack, password guessing attack, and other existing attacks. In authentication phase, the protocol have used lattice based cryptography that plays a very important role in authentication and key exchanges that protects against the threat of quantum attacks. The proposed protocol ensures both securities against quantum attacks and efficiency due to simple algebraic operations that are polynomial addition and multiplications. © 2023 IEEE.

关键词： Authentication

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