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2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Nallusamy, C. Krishnan, M. Adith Farook, S. Gopinath, M. Ravikumar, Divya Department of Information Technology Tiruchengode637 215 India K. S. Rangasamy College of Technology Department of Information Technology Tiruchengode637 215 India Sri Sai Ram Engineering College Computer Science and Business Systems Chennai600044 India

ISBN: (纸本)9798350359688

Efficient path planning for on-road vehicle breakdown assistance in Vehicular Ad Hoc Networks (VANETs) is crucial for ensuring timely and effective response to incidents. This paper proposes a novel approach that harnesses the adaptive and resilient characteristics of the Honey Badger optimization algorithm along with the deceptive and adaptive behaviors of the Cuttlefish optimization algorithm to optimize path planning in VANETs. The hybridization of these two distinct optimization techniques offers a unique synergy, allowing for robust and agile response strategies in dynamic vehicular environments. Through a comprehensive methodology, we formulate the breakdown assistance problem as an optimization task and integrate the Honey Badger and Cuttlefish algorithms to generate and update paths in real-time. We implement the proposed algorithm in a VANET simulation environment and evaluate its performance using various metrics, including path length, time to reach the stranded vehicle, and resource utilization. Experimental results demonstrate the effectiveness of the hybrid algorithm, outperforming traditional approaches and showcasing its potential for enhancing vehicle breakdown assistance in VANETs. © 2024 IEEE.

关键词： Vehicular ad hoc networks

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An Ensemble Machine Learning Technique for Stroke Prognosis

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computer systems science & engineering 2023年第10期47卷 413-429页

作者： Mesfer Al Duhayyim Sidra Abbas Abdullah Al Hejaili Natalia Kryvinska Ahmad Almadhor Uzma Ghulam Mohammad Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz University Al-Kharj16273Saudi Arabia Department of Computer Science COMSATS UniversityIslamabad53000Pakistan Computer Science Department Faculty of Computers&Information TechnologyUniversity of TabukTabuk71491Saudi Arabia Information Systems Department Faculty of ManagementComenius University in BratislavaOdbojárovBratislava440Slovakia Department of Computer Engineering and Networks College of Computer and Information SciencesJouf UniversitySakaka72388Saudi Arabia Department of Computer Science and Software Engineering International Islamic UniversityIslamabad44000Pakistan

Stroke is a life-threatening disease usually due to blockage of blood or insufficient blood flow to the *** has a tremendous impact on every aspect of life since it is the leading global factor of disability and *** can range from minor to severe(extensive).Thus,early stroke assessment and treatment can enhance survival *** prediction is extremely time and resource *** prediction methods such as Modern Information and Communication Technologies(ICTs),particularly those inMachine Learning(ML)area,are crucial for the early diagnosis and prognosis of ***,this research proposed an ensemble voting model based on three Machine Learning(ML)algorithms:Random Forest(RF),Extreme Gradient Boosting(XGBoost),and Light Gradient Boosting Machine(LGBM).We apply data preprocessing to manage the outliers and useless instances in the ***,to address the problem of imbalanced data,we enhance the minority class’s representation using the Synthetic Minority Over-Sampling Technique(SMOTE),allowing it to engage in the learning process *** reveal that the suggested model outperforms existing studies and other classifiers with 0.96%accuracy,0.97%precision,0.97%recall,and 0.96%*** experiment demonstrates that the proposed ensemble voting model outperforms state-of-the-art and other traditional approaches.

关键词： Stroke prediction machine learning ensemble model data analysis Synthetic Minority Over-Sampling

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Quantum Cryptography in Secure IoT Communications

Quantum Cryptography in Secure IoT Communications

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Frontier Technologies and Solutions (ICFTS), International Conference on

作者： P. Ramesh N. Saranya Shantha Shalini K S. Leela Eric Howard BalaSundara Ganapathy N Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Business Systems Bannari Amman Institute of Technology Sathyamangalam Tamil Nadu India Department of Computer Science and Engineering Aarupadai Veedu Institute of Technology Paiyanoor Tamil Nadu India Department of Computer Science and Business Systems Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Physics and Astronomy Macquarie University Sydney Australia Department of Information Technology Panimalar Engineering College Chennai Tamil Nadu India

ISBN: (数字)9798331502768

ISBN: (纸本)9798331502775

QRNGs and post-Quantum VPNs are employed in this study to examine the ability of quantum cryptography in securing IoT interactions. QRNGs generate the keys based on quantum mechanics that are almost impossible to guess. This makes them safer compared to the traditional approaches to undertaking the exercise. These keys ensure that IoT networks can be used to send and receive data securely eradicating issues that relate to quantum computing. The application of post-Quantum VPNs helps to defend communication channels from being attacked with the help of quantum algorithms. The use of used of QRNGs and post-Quantum VPNs in the IoT devices gives a robust protection system against various cyber threats. This paper considers several performance indicators, including the degree of encryption, response time, multinomiality, and power consumption abilities. That is to say, it demonstrates that this cryptographic method significantly enhances the IoT communications' resilience to quantum threats. The findings reveal that quantum cryptography is effective in securing IoT networks in the future.

关键词： Resistance Quantum algorithm Scalability Virtual private networks Encryption Internet of Things Time factors Quantum cryptography Protection Resilience

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Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Learning 22

Depth-Aware Multi-Modal Fusion for Generalized Zero-Shot Lea...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Cao, Weipeng Yao, Xuyang Xu, Zhiwu Pan, Yinghui Sun, Yixuan Li, Dachuan Qiu, Bohua Wei, Muheng Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Stony Brook University New York United States Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China ZhenDui Industry Artificial Intelligence Co. Ltd Shenzhen China Department of Automation Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798331527471

Realizing Generalized Zero-Shot Learning (GZSL) based on large models is emerging as a prevailing trend. However, most existing methods merely regard large models as black boxes, solely leveraging the features output by the final layer while disregarding potential performance enhancements from other layers. Indeed, numerous researchers have visually depicted variations in the features learned across different layers of neural networks. Motivated by this observation, we propose a Vision Transformer (ViT)-based GZSL method named Depth-Aware Multi-Modal ViT (DAM2ViT), which exploits multi-level features of ViT. DAM2ViT incorporates a multi-modal interaction block to align semantic information of categories across multiple layers, thereby augmenting the model’s capacity to learn associations between visual and semantic spaces. Extensive experiments conducted on three benchmark datasets (i.e., CUB, SUN, AWA2) have showcased that DAM2ViT achieves competitive results compared to state-of-the-art methods. © 2024 IEEE.

关键词： Zero-shot learning

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ACO-Based Hyperparameter Tuning of a DL Model for Lung Cancer Prediction

ACO-Based Hyperparameter Tuning of a DL Model for Lung Cance...

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Computing, Power and Communication Technologies (IC2PCT), IEEE International Conference on

作者： Kumar Dilip G S Pradeep Ghantasala D. Nageswara Rao Manisha Rathee Priyanka Bathla Department of Computer Science and Engineering Alliance University Bengaluru India School of Computer & Systems Sciences Jawaharlal Nehru University New Delhi Chandigarh University Gharuan Mohali Punjab India

Machine learning models could provide human intelligence to perform complex tasks effectively. Among them, deep learning models have successfully been applied in several domains. However, that efficacy can be achieved only through effective design and training of deep learning models, which is a complex task to achieve and requires a lot of expertise in the domain. Finding the optimal hyperparameter values is crucial for high-performance machine learning models, especially tree-based models and neural network-based models. Estimation of the parameters such as the weight of neural networks can be done using data sets and these are internal model parameters, however the hyperparameters such as number of layers, size of layers, learning rates etc. are external to the model. The values of hyperparameters can either be determined intuitively by domain experts or by manually and randomly trying various possible configurations until we arrive at some acceptable values. Both have severe limitations for machine learning models with a lot of hyperparameters. Several attempts have been made in the direction of automatic hyperparameter optimization to save human efforts in designing machine learning models. Nature-inspired computing has gained a lot of popularity in this direction in recent years. In this paper, we have applied ant colony optimization, a nature-inspired approach for hyperparameter optimization for deep learning models for the prediction of lung cancer.

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Challenges and Emerging Issues for Generative AI and Hyper Intelligence

Challenges and Emerging Issues for Generative AI and Hyper I...

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Cyber science and Technology Congress (CyberSciTech), IEEE

作者： Jianhua Ma Qun Jin Hui-Huang Hsu John Paul C. Vergara Antonio Guerrieri Claudio Miceli Ao Guo Faculty of Computer and Information Sciences Hosei University Japan Department of Human Informatics and Cognitive Sciences Waseda University Japan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Information Systems and Computer Science Ateneo de Manila University Philippines ICAR-CNR Italy Graduate Program in Systems Engineering and Computer Science Federal University of Rio de Janeiro Brazil Graduate School of Informatics Nagoya University Japan

ISBN: (数字)9798331532093

ISBN: (纸本)9798331532109

Generative Artificial Intelligence (GenAI) represents a significant milestone in the development of artificial intelligence, bringing sophisticated AI capabilities into daily life and work. As we approach the era of Hyper Intelligence (Hyper-I), a variety of critical challenges and emerging issues have come to light, ranging from computational complexity to ethical concerns. This paper explores the evolution of AI from the perspective of human learning, comparing machine and human intelligence, and identifying key considerations for the development of future AI systems. It also highlights the growing importance of regulating advanced AI models, such as Reinforcement Learning-based Long-Term Planning Agents, to ensure that Hyper-I remains under human control. Additionally, the paper discusses the computational complexity of transformer-based models, their applicability to intractable problems, and their role in cognitive building systems and resource-constrained environments through TinyML. By analyzing these pressing challenges, this work provides insights into the future of AI and the path toward responsible innovation in generative and hyper-intelligent systems.

关键词： Hyper-intelligent systems Ethics Technological innovation Generative AI Computational modeling Tiny machine learning Pressing Transformers Regulation Computational complexity

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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

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computer systems science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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Multibeam Multi-Frequency Characterization of Water Column Litter

Multibeam Multi-Frequency Characterization of Water Column L...

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OCEANS

作者： Pedro Alves Guedes Hugo Silva Sen Wang Alfredo Martins José Miguel Almeida Eduardo Silva Technology and Science INESCTEC - Institute for Systems and Computer Engineering Technology and Medicine Imperial College London - Imperial College of Science ISEP - School of Engineering Polytechnic Institute of Porto

ISBN: (数字)9798350362077

ISBN: (纸本)9798350362084

This paper explores the potential use of acoustic imaging and the use of a multi-frequency multibeam-echosounder (MBES) for monitoring marine litter in the water column. The main goal is to perform a test and validation setup using a simulation and actual experimental setup to determine if the MBES data can detect marine litter in a water column image (WCI) and if using multi-frequency MBES data will allow to better distinguish and characterize marine litter debris in detection applications. Results using simulated HoloOcean Environment and actual marine litter data revealed the successful detection of objects commonly found in ocean litter hotspots at various ranges and frequencies, enablingthe pursue of novel means of automatic detection and classification in MBES WCI data while using multi-frequency capabilities.

关键词： Oceans Acoustic imaging Plastics Monitoring

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A Review Analysis on Smart Blind Stick Using IoT 4

A Review Analysis on Smart Blind Stick Using IoT

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Asha, P.K. Shanmugha Priya, R.K. Nagaraj, D. Aswini, S. Wilson, G Preethi Vinoth Kumar, K. New Horizon College of Engineering Department of Chemistry Bengaluru India Jai Shriram Engineering College Avinashipalayam Department of Information Technology Tirupur India Dhanalakshmi College of Engineering Department of Cse Chennai India Bangalore Technological Institute Department of Cse Bengaluru India Jerusalem College of Engineering Department of Computer Science & Business Systems Chennai India New Horizon College of Engineering Department of Eee Bengaluru India

ISBN: (纸本)9798331523923

Millions of individuals worldwide struggle with blindness and other forms of visual impairment. 940 million individuals will suffer some degree of vision loss by 2015, according to the WHO (World Health Organization). 246 million people experience fleeting apparitions, while 39 million people are blind. Most people with bad vision are older than 50 and live in industrialized cultures. The main challenge that blind individuals encounter is mobility. They are cut off from their environment. Since they are oblivious to what is going on around them, they require assistance. Technological advancements have been crucial in increasing the mobility and freedom of those who are blind. One such innovation is the Smart Blind Stick, a high-tech assistance gadget made to improve the safety and navigation of the blind. The goal of this review article is to examine the many facets of Smart Blind Stick technology, including its functionality, benefits, drawbacks, and overall influence on users' life. This review aims to shed light on the current status of Smart Blind Stick technology and its potential future advances by reviewing existing literature and advancements. © 2025 IEEE.

关键词： Obstacle detectors

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NeuroMorse: A Temporally Structured Dataset For Neuromorphic Computing

arXiv

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arXiv 2025年

作者： Walters, Ben Bethi, Yeshwanth Kergan, Taylor Nguyen, Binh Amirsoleimani, Amirali Eshraghian, Jason K. Afshar, Saeed Azghadi, Mostafa Rahimi College of Science and Engineering James Cook University Townsville Australia International Centre for Neuromorphic Systems Western Sydney University Australia Department of Electrical Engineering and Computer Science York University Toronto Canada Department of Electrical and Computer Engineering University of California Santa Cruz United States

Neuromorphic engineering aims to advance computing by mimicking the brain’s efficient processing, where data is encoded as asynchronous temporal events. This eliminates the need for a synchronisation clock and minimises power consumption when no data is present. However, many benchmarks for neuromorphic algorithms primarily focus on spatial features, neglecting the temporal dynamics that are inherent to most sequence-based tasks. This gap may lead to evaluations that fail to fully capture the unique strengths and characteristics of neuromorphic systems. In this paper, we present NeuroMorse, a temporally structured dataset designed for benchmarking neuromorphic learning systems. NeuroMorse converts the top 50 words in the English language into temporal Morse code spike sequences. Despite using only two input spike channels for Morse dots and dashes, complex information is encoded through temporal patterns in the data. The proposed benchmark contains feature hierarchy at multiple temporal scales that test the capacity of neuromorphic algorithms to decompose input patterns into spatial and temporal hierarchies. We demonstrate that our training set is challenging to categorise using a linear classifier and that identifying keywords in the test set is difficult using conventional methods. The NeuroMorse dataset is available at 10.5281/zenodo.12702379, with our accompanying code at https://***/jc427648/NeuroMorse. © 2025, CC BY.

关键词： Spatio-temporal data

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