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Relay-Assisted Connectivity Management for Underwater High Speed Communication Networks

Relay-Assisted Connectivity Management for Underwater High S...

Paradigm Shift in Information Technologies with Innovative Applications in Global Scenario (ICPSITIAGS), IEEE International Conference on

作者： Girija Shankar Sahoo G. Renuka Swapnil K. Shinde Krishnan Batri Zeeshan Ahmad Lone Ayaan Faiz Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India Department of Electronics and Communication Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India School of Computer Science and Engineering Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science & Engineering Vivekananda Global University Jaipur India Centre of Interdisciplinary Research in Business and Technology Institute of Engineering and technology Chitkara University Punjab India

ISBN: (数字)9798350329773

ISBN: (纸本)9798350329780

Relay-assisted connectivity control for underwater high-pace verbal exchange networks improves verbal exchange velocity and reliability by connecting Wi-Fi networks to relays. By allowing communication among unique networks, it enables higher insurance, higher air interface, higher interference immunity, and higher network resilience. With underwater excessive pace verbal exchange networks, relays are used to ensure reliable operation to increase the networks' transmission range. Such relay-assisted verbal exchange networks can offer higher records costs than a single factor-to-factor link should provide. They can help to reduce the effect of interference that affects the speed of conversation over lengthy distances. This generation offers some benefits: extended latency, stepped forward bandwidth usage, accelerated community topology, improved fault tolerance and robustness, better statistics synchronization, and quicker handover in multi-radio networks. Additionally, with the help of relay-assisted verbal exchange networks, such networks' deployment, and operation costs can be decreased substantially.

关键词： Costs Interference Spread spectrum communication Routing Robustness Topology Synchronization

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An Energy-Efficient Event Reliability Protocol for Wireless Communication Networks

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Mobile Information Systems 2022年第1期2022卷

作者： Khan, Gulista Kumar Agarwal, Ambuj Khapre, Shailesh Arunkumar, G. Shankar, Achyut Khosravi, Mohammad R. Faculty of Engineering Teerthanker Mahaveer University Moradabad India Department of Computer Science and Engineering School of Engineering and Technology Sharda University Greater Noida India Department of Data Science and Artificial Intelligence IIIT-Naya Raipur Chhattisgarh Raipur India School of Computer Science and Engineering VIT University Vellore India Department of Computer Science and Engineering Amity University Noida India Faculty of Engineering and Built Environment University of Johannesburg Johannesburg South Africa Department of Computer Engineering Persian Gulf University Bushehr Iran

Nowadays, WSN (wireless sensor networks) are used in every aspect of life. It consists of tiny sensor nodes that are capable of sensing, aggregating, dispensation, and sending data for end-user queries. Two major constraints in WSN are power consumption and reliability. As we know, power is limited per sensor node, so efficient utilization of power is needed all the way. Another constraint is reliability. Both constraints are in conflict with each other. If we try to increase reliability by sending the same packet on a different path, then it will increase power consumption. Many algorithms have been defined in the past to reduce power consumption and have reliable transmission. Basically, there are two types of reliability mechanisms: packet reliability and occurrence reliability. Package reliability explains about all packages must be reached at the destination. Event reliability ensures that at least one event should be successful in a specific region. This paper proposes an analysis model to evaluate the reliability of wireless sensor networks. In addition, this paper has proposed a reliability technique for events. The proposed algorithm has been simulated, analyzed, and compared with the already-published algorithms, namely BaseModel, SWIA, and ERP techniques. © 2022 Gulista Khan et al.

关键词： Electric power utilization

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Embedding VLAD in Transformer for Video Question Answering

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Jisuanji Xuebao/Chinese Journal of computers 2023年第4期46卷 671-689页

作者： Guo, Dan Yao, Shen-Tao Wang, Hui Wang, Meng School of Computer and Information Engineering Hefei University of Technology Hefei230601 China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei230094 China Key Laboratory of Knowledge Engineering with Big Data（Hefei University of Technology） Ministry of Education Hefei230601 China Intelligent Interconnected Systems Laboratory of Anhui Province（Hefei University of Technology） Hefei230601 China

Video question answering（VideoQA）is a typical cross-modal understanding task. Its challenge lies in how to learn appropriate multimodal representation and cross-modal correlation for answer inference. Most existing video question answering methods focus on the latter，e. g.，relationship learning between each video frame or clip and word. In this work， we devote to advanced feature embedding of both video and query. We develop a clustering-based VLAD technique for VideoQA. The novelty of our work is the joint exploitation of temporal aggregation and correlation in multimodality. We propose an end-to-end trainable Transformed VLAD embedding network， named TVLAD-Net. TVLAD-Net constructs a differentiable aggregation network module（i. e.， convolutional Residual-less VLAD Block）to generate compact VLAD descriptors（transforming N frames，clips or words to compact K descriptors while K © 2023 science Press. All rights reserved.

关键词： Semantics

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Leveraging CNN-Transformer Hybrid Architecture for Accurate Breast Tumor Segmentation in Mammograms

Leveraging CNN-Transformer Hybrid Architecture for Accurate ...

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Women in data science at Prince Sultan University (WiDS PSU), International Conference of

作者： Mudassar Ali Muhammad Zeeshan Tahir Iqra Mariam Khadija Nawaz Tariq Mahmood Samia Allaoua Chelloug College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China School of Electronics and Information Engineering Liaoning University of Technology Jinzhou China Faculty of Information Sciences University of Education Vehari Pakistan Artificial Intelligence and Data Analytics Lab CCIS PSU Riyadh SA Department of Information Technology CCIS PNU Riyadh SA

ISBN: (数字)9798331520922

ISBN: (纸本)9798331520939

Breast cancer detection through mammograms is very crucial for early diagnosis, and tumor segmentation is an important step in treatment planning. This paper proposes a novel approach by combining CNNs with transformers, which we call the Hybrid CNN + Transformer model, for the task of breast tumor segmentation. It leverages the strengths of CNN s in local feature extraction to capture fine details, such as tumor boundaries, and combines them with transformers that model long-range dependencies and contextual relationships across images for the exact delineation of tumor regions in complex tissue structures. Our methodology will be tested on the high-quality mammog-raphy dataset annotated with both benign and malignant lesions, the so-called INbreast dataset. Herein, the CNN backbone-first for instance, ResNet or EfficientNet-extracts the input mammo-gram hierarchically; features are fed into a transformer layer in order to capture global dependencies, after which the final segmentation mask is generated by using a U-Net style decoder for a pixel-wise tumor classification. The segmentation accuracies of our models demonstrated the significant improvement achieved in tumor segmentation, using Hybrid CNN + Transformer when compared with tra-ditional CNN-based models on challenging tumor boundaries and complex tissue patterns. An accurate, robust, completely automatic method for tumor segmentation will thus enable improved diagnostic feasibility on clinical grounds. Furthermore, the combination of learning at both local and global features improved generalizability to previously unseen mammogram images and therefore strengthened clinical applicability.

关键词： Image segmentation Adaptation models Accuracy Breast tumors Computational modeling Transformers Mammography Breast cancer Convolutional neural networks Tumors

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Prediction of Startup Performance Using Bidirectional Long Short-Term Memory with Parametric Switch Activation Function

Prediction of Startup Performance Using Bidirectional Long S...

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Ambient intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Putta Srivani Gopu Sowjanya Bura Vijay Kumar Kassem AL-Attabi Ibraheem Hatem Mohammed Department of CSE (Cyber Security) Malla Reddy Engineering college for women Hyderabad India Department of CSE(Data Science) CMR Technical Campus Hyderabad India School of Computer Science and Artificial Intelligence SR University Warangal India The Islamic University Najaf Iraq Department of Computer Communications Engineering Al Rafidain University College Bagdad Iraq

Predicting start-up success has garnered increasing attention in recent years. Numerous predictive models and methodologies have been devised to ascertain the probability of a start-up's triumph contingent on diverse aspects. Large datasets have been analysed using machine learning techniques like logistic regression, decision trees, and neural networks to find patterns that can help predict startup success. Predicting the success of a start-up, however, is a difficult undertaking because the business environment is unpredictable and changeable. Efficient resource utilization is crucial for economic development and has a direct impact on reducing unemployment rates. In order to minimize resource wastage and mitigate the risk of failure, the research identified and examined specific factors that influence the success or failure of small-scale companies. In the current research, the Crunch Base dataset is utilized to predict the failure or success of start-ups by employing the Bidirectional Long Short-Term Memory (Bi-LSTM) with Parametric Switch Activation (PSA) function model. The Bi-LSTM unit, a type of Recurrent Neural Network (RNN), incorporates the Feed Forward Neural (FFN) Network for classification purposes. The suggested Bi-LSTM model achieved a higher accuracy rate of 72.26% compared to existing methods. The suggested Bi-LSTM model achieved a higher accuracy rate of 72.26% compared to existing methods.

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Detection and classification of breast lesions using multiple information on contrast-enhanced mammography by a multiprocess deep-learning system: A multicenter study

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Chinese Journal of Cancer Research 2023年第4期35卷 408-423页

作者： Yuqian Chen Zhen Hua Fan Lin Tiantian Zheng Heng Zhou Shijie Zhang Jing Gao Zhongyi Wang Huafei Shao Wenjuan Li Fengjie Liu Simin Wang Yan Zhang Feng Zhao Hao Liu Haizhu Xie Heng Ma Haicheng Zhang Ning Mao School of Information and Electronic Engineering Shandong Technology and Business UniversityYantai 264005China Department of Radiology Yantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China School of Medical Imaging Binzhou Medical UniversityYantai 264003China Department of Radiology Fudan University Cancer CenterShanghai 200433China Department of Radiology Guangdong Maternal and Child Health HospitalGuangzhou 510010China School of Computer Science and Technology Shandong Technology and Business UniversityYantai 264005China Yizhun Medical AI Co.Ltd. Beijing 100080China Big Data and Artificial Intelligence Laboratory Yantai Yuhuangding HospitalAffiliated Hospital of Qingdao UniversityYantai 264000China

Objective: Accurate detection and classification of breast lesions in early stage is crucial to timely formulate effective treatments for patients. We aim to develop a fully automatic system to detect and classify breast lesions using multiple contrast-enhanced mammography(CEM) ***: In this study, a total of 1,903 females who underwent CEM examination from three hospitals were enrolled as the training set, internal testing set, pooled external testing set and prospective testing set. Here we developed a CEM-based multiprocess detection and classification system(MDCS) to perform the task of detection and classification of breast lesions. In this system, we introduced an innovative auxiliary feature fusion(AFF)algorithm that could intelligently incorporates multiple types of information from CEM images. The average freeresponse receiver operating characteristic score(AFROC-Score) was presented to validate system’s detection performance, and the performance of classification was evaluated by area under the receiver operating characteristic curve(AUC). Furthermore, we assessed the diagnostic value of MDCS through visual analysis of disputed cases,comparing its performance and efficiency with that of radiologists and exploring whether it could augment radiologists’ ***: On the pooled external and prospective testing sets, MDCS always maintained a high standalone performance, with AFROC-Scores of 0.953 and 0.963 for detection task, and AUCs for classification were 0.909[95% confidence interval(95% CI): 0.822-0.996] and 0.912(95% CI: 0.840-0.985), respectively. It also achieved higher sensitivity than all senior radiologists and higher specificity than all junior radiologists on pooled external and prospective testing sets. Moreover, MDCS performed superior diagnostic efficiency with an average reading time of 5 seconds, compared to the radiologists’ average reading time of 3.2 min. The average performance of all radiologists was also improved to

关键词： Deep learning contrast-enhanced mammography breast lesions detection classification

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Automated Injury Detection and Alert Systems in Public Transportation Integrating IoT with Convolutional Neural Networks

Automated Injury Detection and Alert Systems in Public Trans...

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Self Sustainable artificial intelligence Systems (ICSSAS), International Conference on

作者： S P Vimal John Benito Jesudasan Peter U. Kavitha G. Bhuvaneswari G. Manikandan R Thamizhamuthu Department of Electronics and Communication Engineering Sri Ramakrishna Engineering College Coimbatore Tamil Nadu India Cyber | Data Science & Engineering Capital One Services LLC Richmond Virginia USA School of Commerce Vel Tech RangarajanDr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Computer Science and Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Artificial Intelligence and Data Science RMK Engineering College Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Campus-Chengalpattu Chennai Tamil Nadu India

ISBN: (数字)9798350368413

ISBN: (纸本)9798350368420

Innovative methods to improve public transportation safety and response mechanisms have emerged due to Internet of Things (IoT) and artificial intelligence (AI) technologies. IoT devices and Convolutional Neural Networks (CNNs), a class of deep learning algorithms analyzing visual imagery, are used to create an innovative Automated Injury Detection and Alert System (AIDAS) for public transportation. The proposed AIDAS architecture employs IoT sensors and cameras in buses and trains to monitor and evaluate passengers’ physical health in real time. These sensors can detect unexpected impacts, odd motions, and distressing noises. A strong CNN model trained to detect passenger injury or bodily damage receives live images from the onboard cameras. An automatic alarm process is activated immediately upon injury detection. The closest emergency response teams and transportation system operators get complete event reports, including time, position (by GPS coordinates), and injury type. A quick reaction to accidents with this real-time alarm system might save lives and reduce public transportation injuries. AIDAS’ use of IoT and CNN technology is innovative in public transportation safety. The technology significantly improved emergency response times and reduced unattended injuries in public transport networks. The system’s excellent damage detection accuracy is due to CNNs’ advanced image processing and pattern recognition skills and IoT devices’ sensory inputs. It also addresses privacy issues and the necessity for strong data encryption and proposes solutions. Finally, it analyzes how AIDAS affects public transportation safety and future research and development in this critical field.

关键词： Visualization Emergency services Real-time systems Convolutional neural networks Internet of Things Reliability artificial intelligence Injuries Public transportation Monitoring

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Enhanced Prediction of Early-Stage Autism Using Supervised Learning with Optimized Feature engineering

Enhanced Prediction of Early-Stage Autism Using Supervised L...

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International Conference on ICT in Business Industry & Government (ICTBIG)

作者： Amritpal Sidhu Kiran K S P.D. More K.S. Bhuvaneshwari Prateek Aggarwal M. Sindhu Chitkara Centre for Research and Development Chitkara University Himachal Pradesh India School of Engineering and Technology Physics JAIN (Deemed to be University) Bangalore Karnataka India Computer Engineering Vishwakarma Institute of Technology Pune India Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of CSE Prince Shri Venkateshwara Padmavathy Engineering College Chennai India

ISBN: (数字)9798331518981

ISBN: (纸本)9798331518998

An autism spectrum disease (ASD) affects a person's brain development. Autism spectrum disorder is detected in children and young adults. For an early diagnosis of the disease, parental observation is crucial. That is why it is possible to provide children with effective therapy or treatment when they are young. Misdiagnosis is common due to the wide range of symptoms and levels of autism range syndrome. The datasets on children and toddlers with ASD that were identified at an early age were subjected to a limited number of Feature Transformation methods. The next step was to determine which classification methods performed best by applying them to various ASD datasets. Log, Power-Box Cox, and Yeo-Johnson Transformations for toddler datasets and Log transformations for children’s datasets were the FTs that followed the best prediction. The next step in using these altered datasets for early autism prediction is to use characteristic Selection Techniques in order to identify the most important ASD risk characteristic. In this comparison, the proposed method is the top machine learning technique with an incredible accuracy. Decision Tree, Random Forest (RF), K-Nearest Neighbours(KNN), Support Vector Machine(SVM), and Gradient Descent(GD) are some of the other models that perform a decent job, but they can't compare to the outstanding results that proposed method obtained. With an accuracy rate of 98.99%, proposed method stands out as the most reliable and efficient alternative among these methods.

关键词： Radio frequency Support vector machines Autism Pediatrics Accuracy Supervised learning Vectors Reliability Random forests Diseases

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An Investigation into Spectral Efficiency Enhancement of Fiber-Optic Communication via High-Speed Spectrum Management

An Investigation into Spectral Efficiency Enhancement of Fib...

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International Conference on Futuristic Technologies (INCOFT)

作者： Khushboo Singh Sai Lalitha G. Ravivarman Sasirekha R Swapnil K. Shinde Akhilendra Pratap Singh Department of Computer Science & Engineering Vivekananda Global University Jaipur India Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Electrical and Electronics Engineering Karpagam Academy of Higher Education Coimbatore Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Artificial Intelligence & Data Science Vishwakarma Institute of Information Technology Pune India Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh India

This research appears into the effectiveness of high-pace spectrum management as a way to beautify the spectral performance of fiber-optic conversation. that is finished by means of forecasting a dynamic optical visitors arrival pattern to reduce carrier-degree blocking off opportunity, and with the aid of allocating the maximum green bandwidth to every request based totally on a non-blocking priority-based totally scheduling algorithm. This study assesses the overall performance of modern-day systems, analyzes their shortcomings, and uses experimental consequences to evaluate their effectiveness for improving spectral efficiency. The findings endorse that there's ability for improvement in spectral efficiency with excessive-speed spectrum control.

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DeWave: Discrete EEG Waves Encoding for Brain Dynamics to Text Translation

arXiv

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arXiv 2023年

作者： Duan, Yiqun Zhou, Jinzhao Wang, Zhen Wang, Yu-Kai Lin, Chin-Teng GrapheneX-UTS HAI Centre Australian Artificial Intelligence Institute Faculty of Engineering and Information Technology University of Technology Sydney UltimoNSW2007 Australia School of Computer Science The University of Sydney CamperdownNSW2050 Australia

The translation of brain dynamics into natural language is pivotal for brain-computer interfaces (BCIs). With the swift advancement of large language models, such as ChatGPT, the need to bridge the gap between the brain and languages becomes increasingly pressing. Current methods, however, require eye-tracking fixations or event markers to segment brain dynamics into word-level features, which can restrict the practical application of these systems. To tackle these issues, we introduce a novel framework, DeWave, that integrates discrete encoding sequences into open-vocabulary EEG-to-text translation tasks. DeWave uses a quantized variational encoder to derive discrete codex encoding and align it with pre-trained language models. This discrete codex representation brings forth two advantages: 1) it realizes translation on raw waves without marker by introducing text-EEG contrastive alignment training, and 2) it alleviates the interference caused by individual differences in EEG waves through an invariant discrete codex with or without markers. Our model surpasses the previous baseline (40.1 and 31.7) by 3.06% and 6.34%, respectively, achieving 41.35 BLEU-1 and 33.71 Rouge-F on the ZuCo dataset. This work is the first to facilitate the translation of entire EEG signal periods without word-level order markers (e.g., eye fixations), scoring 20.5 BLEU-1 and 29.5 Rouge-1 on the ZuCo dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal encoding

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