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SupermarQ: A Scalable Quantum Benchmark Suite 28

SupermarQ: A Scalable Quantum Benchmark Suite

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28th Annual IEEE International symposium on high-performance computer architecture (HPCA)

作者： Tomesh, Teague Gokhale, Pranav Omole, Victory Ravi, Gokul Subramanian Smith, Kaitlin N. Viszlai, Joshua Wu, Xin-Chuan Hardavellas, Nikos Martonosi, Margaret R. Chong, Frederic T. Princeton Univ Dept Comp Sci Princeton NJ 08544 USA Supertech Chicago IL 60615 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA Northwestern Univ Dept Comp Sci Evanston IL 60208 USA

ISBN: (纸本)9781665420273

the emergence of quantum computers as a new computational paradigm has been accompanied by speculation concerning the scope and timeline of their anticipated revolutionary changes. While quantum computing is still in its infancy, the variety of different architectures used to implement quantum computations make it difficult to reliably measure and compare performance. this problem motivates our introduction of SupermarQ, a scalable, hardware-agnostic quantum benchmark suite which uses application-level metrics to measure performance. SupermarQ is the first attempt to systematically apply techniques from classical benchmarking methodology to the quantum domain. We define a set of feature vectors to quantify coverage, select applications from a variety of domains to ensure the suite is representative of real workloads, and collect benchmark results from the IBM, IonQ, and AQT@LBNL platforms. Looking forward, we envision that quantum benchmarking will encompass a large cross-community effort built on open source, constantly evolving benchmark suites. We introduce SupermarQ as an important step in this direction.

关键词： Quantum computing Benchmarking Program Characterization

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Automated Virtual Toll Booth System Based on Number Plate Recognition Using YOLOv8 and EasyOCR 15

Automated Virtual Toll Booth System Based on Number Plate Re...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Kumar, Vinod S. Ponmani, S. Anand, K. Ananthajothi, K. Department of Computer Science and Engineering Rajalakshmi Engineering College Chennai India

ISBN: (纸本)9798350370249

In this work, a virtual toll booth system utilizing cutting-edge technologies like EasyOCR with optical character recognition, or OCR, and YOLOv8 for object detection is introduced. By connecting the accounts stored in a database with vehicle number plate recognition, the system is meant to streamline toll collection procedures. By using this technique, the system may efficiently handle toll transactions without the need for actual toll booths or human involvement. the registration of car number plates as well as owner data, which is kept in a database based on CSV files, is the first step in the system's process. the system's camera takes a picture of the license plate when a car gets close to the toll station. After that, the image is processed using the EasyOCR engine to extract the information from the number plate and the YOLOv8 algorithm to determine whether a car is present. An extra toll is charged if the license plate is within a set distance of the toll booth and matches a registered number in the database. the toll amount, the date and time of the transaction, the vehicle's license plate, and other transaction details are all stored in the database by the system. there is no toll if the registration number is not found or is found outside of the predetermined range. Comprehensive testing was used to assess the system's performance, and the results showed that it could recognize license plates and manage toll transactions with high accuracy. the results show that the system can increase the overall efficiency of toll management systems, reduce operational expenses, and speed up toll collection procedures. ©2024 IEEE.

关键词： Optical character recognition

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Enhancing Arrhythmia Classification: A Comparative Study of Filters and Deep Learning Models on ECG Data 15

Enhancing Arrhythmia Classification: A Comparative Study of ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Mallikarjunamallu, K. Syed, Khasim School of Computer Science and Engineering VIT-AP University Andhra Pradesh Amaravati522237 India

ISBN: (纸本)9798350370249

Arrhythmia classification using electrocardiogram (ECG) data is crucial in the treatment of cardiovascular disease. However, ECG data often includes noise, which may conceal essential facts, making categorization difficult. this paper compares a notch filter with Gaussian noise, a Finite Impulse Response (FIR) filter, and an Infinite Impulse Response (IIR) filter using a 1D convolutional neural network (1DCNN) and DenseNet-121. We obtained the ECG signals for this investigation from the MIT-BIH arrhythmia database. the preprocessing pipeline incorporates Gaussian noise into the notch filter at various frequency bands. the proposed IIR filter addresses baseline wander, power-line interference, and muscle artifacts, whereas FIR filters minimize high-frequency noise and baseline wander. then, we train the 1DCNN and DenseNet-121 models on the filtered ECG data. these models classify arrhythmias as normal (N), supraventricular (S), ventricular (V), fusion (F), or unknown (Q). Accuracy, precision, recall, and F1 score are standard metrics used to assess model-filter combinations. Each filtering strategy increases classification performance, and 1DCNN and DenseNet-121 outperform in noisy ECG data. the IIR filter with the DenseNet-121 method achieved the best accuracy of 99.97%, while the Gaussian noise with the notch filter using the 1DCNN method got 99%, contributing to diagnostic capabilities. these discoveries might help detect arrhythmias and improve patient outcomes in cardiovascular diagnostics. ©2024 IEEE.

关键词： IIR filters

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Beyond the Black Box: Explainable AI for Glaucoma Detection and Future Improvements 15

Beyond the Black Box: Explainable AI for Glaucoma Detection ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Bindu, Avadhani thangavel, Senthil Kumar Somasundaram, K. Parthasaradhi, Sathyan Pulgurthi, Ram Gopal Dhar, Meenakshi Y Amrita Vishwa Vidyapeetham Amrita School of Computing Coimbatore Department of Computer Science and Engineering India Amrita Vishwa Vidyapeetham Amrita School of Physical Sciences Coimbatore Department of Mathematics India Sathyan Eye Hospital and Coimbatore Glaucoma Foundation Ramanathapuram Coimbatore India Medivista Eye Centre Safilguda Secunderabad India Amrita Institute of Medical Sciences Department of Ophthalmology Faridabad India

ISBN: (纸本)9798350370249

Timely and precise identification of glaucoma is essential to avert irreversible vision loss. Automating glaucoma detection is essential whilst the disease worsens over time and manual screening methods possess limitations. It facilitates early identification and treatment in order to prevent the vision impairment. Machine learning and deep learning algorithms are efficient, nevertheless, they suffer from a lack of interpretability and require a substantial amount of data and computational resources. Transfer learning proves efficient by improving scarcity of data with enhanced performance. this study seeks to examine the operational characteristics of transfer learning approaches and employ XAI (eXplainable Artificial Intelligence) method LIME to achieve clear interpretability of the features obtained by the model for prediction, so as to address the challenges related to interpretability. this study has used a combined dataset consisting of RIMONE, PAPILA, CHAKSU, and AKSHI (a dataset created by the authors) to develop ResNet-50, ResNet50-V2, VGG-16, VGG-19, and EfficientNet-V2S models. Among the developed five models, ResNet-50 and EfficientNet-V2S provide better accuracy as demonstrated in this study with an analysis using metrics and feature visualization. Both models attain a maximum test accuracy of 91.78% and the minimum test loss of, with ResNet-50 at 0.24 and EfficientNet-V2S at 0.23. In addition, these models demonstrate superior precision, specificity, sensitivity, and F1-score, all around 92. Further, feature maps and class activation maps for ResNet-50 and EfficientNet-V2S show distinct and concentrated zones of activation, indicating accurate interpretation. thus, in this comparative study, ResNet-50 and EfficientNet-V2S are choosen as the best models for this classification task owing to their high accuracy and reliable interpretability via visual activation patterns. Consequently, the mobile app ANSAN has been integrated with the ideal clinical deci

关键词： Deep learning

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Priority-Aware Task Offloading and Resource Allocation in Satellite and HAP Assisted Edge-Cloud Collaborative Networks 15

Priority-Aware Task Offloading and Resource Allocation in Sa...

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15th International Conference on Communication Software and Networks, ICCSN 2023

作者： Dai, Xin Chen, Xin Jiao, Libo Wang, Yijie Du, Shougang Min, Geyong School of Computer Science Beijing Information Science & Technology University Beijing China College of Engineering Mathematics and Physical Sciences University of Exeter Exeter United Kingdom

ISBN: (纸本)9798350329360

Mobile edge computing (MEC) has been regarded as an effective technique to handle computing intensive tasks of IoT devices (IoTDs). In this paper, we present a priority-aware edge computing model for space-air-ground integrated networks (SAGIN), which contain a satellite, a high altitude platform (HAP) and numerous IoTDs. In such networks, the HAP provides edge computing services close to the IoTDs and the cloud servers connected to satellite provide cloud computing services. the network considers different services for IoTDs with various Quality-of-Service (QoS) requirements. First, we design a media access control MAC protocol named priority-aware time division multiple access (PTDMA) for controlling the large-scale access of IoTDs. Second, for tasks with different priorities, we propose a priority-aware deep deterministic policy gradient (DDPG) based task offloading and resource allocation scheme (PDOA). Simulation experimental results validate the performance of the PTDMA and PDOA scheme, which can effectively improve the average system utility in a stochastic time-varying environment. © 2023 IEEE.

关键词： Resource allocation

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Optimization of BERT Algorithms for Deep Contextual Analysis and Automation in Legal Document Processing 15

Optimization of BERT Algorithms for Deep Contextual Analysis...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Reddy, B. Srikanth Balavivekanandhan, A. Lakhera, Pallavi Changala, Ravindra Sabareesh, R. Balakumar, A. Department of CSE Potti Sreeramulu Chalavadhi Mallikarjuna Rao College of Engineering & Technology Andra Pradesh. Vijayawada India Department of Computer Science Prof. Dhanapalan College of Science and Management Chennai India Department of Law University of Allahabad Uttar Pradesh Paryagraj India Department of Information Technology Guru Nanak Institutions Technical Campus Hyderabad India Coimbatore India Department of ECE K.Ramakrishnan College of Engineering Trichy India

ISBN: (纸本)9798350370249

Legal document processing is one of the main areas where technology has advanced significantly, thanks to the introduction of deep contextual analysis and automation. the goal of this work is to improve the performance of Bidirectional Encoder Representations from Transformers (BERTs) algorithms in deep contextual analysis, with a particular focus on legal document processing. the effort aims to increase the accuracy and efficiency of BERT-based models in capturing complex legal situations, enabling automation in the reading and analysis of documents. By means of meticulous tuning, attention mechanism modifications, and investigation of ideal hyperparameters, our refined BERT algorithms demonstrate exceptional contextual comprehension and document processing skills. the acquired results show considerable improvements over the baseline BERT models in terms of speed and accuracy. In legal document analysis tasks, the enhanced algorithms produce a significant decrease in processing time while retaining or exceeding the high accuracy and recall of the original model. the study suggests optimizing BERT algorithms for improved efficiency in legal document processing, leading to increased productivity and automated contextual analysis in the legal sector the Python implementation of the BERT-based legal document processing system achieves an accuracy of 92%. ©2024 IEEE.

关键词： Efficiency

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Indian French Bean Varieties: CNN vs. Decision Trees Model Analysis 15

Indian French Bean Varieties: CNN vs. Decision Trees Model A...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Verma, Pooja Vats, Satvik Sharma, Rishabh Swami Vivekanand Institute of Engineering and Technology Department of Computer Science and Engineering Punjab Ramnagar India Computer Science and Engineering Graphic Era Hill University Dehradun248002 India Graphic Era Deemed to be University Dehradun248002 India Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9798350370249

there is no doubt that crop variety improvement and the use of efficient analytical methods that contribute to high agricultural production and food security are crucial. It is research focused on the Comparison of Indian French bean (Phaseolus vulgaris) varieties: Selection-9, Pusa Parvati, and Arka Suvidha using a hybrid Convolutional Neural Network (CNN) and Decision Tree (DT) model. the major aim, therefore, is to assess the effectiveness of this integrated model in correctly sorting and forecasting the yields of these bean types under various agroecological circumstances. this dataset includes observational field data along with morphological and phenological characters of plants collected from various experimental sites. thus, the proposed CNN-DT model embeds the feature extraction capacity of CNNs with the interpretability and decision-making effectiveness of DT. It is this integration that would endeavor to utilize the inherent advantages of the two approaches to improve the classification of the crops and profitability of crop selection and management. this suggests that the hybrid model improves the classification accuracy and reliability better as compared to the traditional isolated models. the assessment of the model in terms of accuracy, precision, recall, and F1 score highlights a performance enhancement when differentiating the three types of French beans. Moreover, the present work suggests the use of state-of-the-art methods in machine learning for agricultural studies and can be useful for the improvement of decision-making mechanisms used by farmers and agronomists. From the findings of this study, it is clear that this is a need to combine modern learning techniques with ordinary farming practices when it comes to picking the right crops and improving production. Altogether, the study provides significant recommendations for AI-domain application in agriculture to increase the effectiveness of crop cultivation more efficiently. © 2024 IEEE.

关键词： Convolutional Neural Network Decision Tree French Bean Variety Classification India

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VANET Congestion Control and Stability performance Analysis using Seagull Optimization Deep Learning 15

VANET Congestion Control and Stability Performance Analysis ...

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15th International Conference on computing Communication and Networking Technologies, ICCCNT 2024

作者： Niaz Ahamed, V.M. Sivaraman, K. Bharath Institute of Higher Education and Research Department of Computer Science and Engineering Tamil nadu Chennai73 India

ISBN: (纸本)9798350370249

Significant matters in VANET environment include high vehicle versatility, changing vehicle thickness and dynamics between vehicle separating. therefore, improved routing protocol enhances overall performance of VANET through intermittent service availability. In this regard, a group-based AI technique is implemented to predict VANET mobility. Effective routing based on a hybrid metaheuristic algorithm combined with Ensemble Learning produces significantly improved results. Vehicle Ad Hoc Networks (VANETs) are currently used in various applications such as transportation using vehicle-to-vehicle multi hop wireless communication since they offer scalable and cost-effective solutions for it. On the other side, multi-hop communication development is regarded as a challenging task in VANET conditions due to rapid alterations of periodic and topological network breakdowns. In general, mobile ad hoc routing protocols, this leads to failure of routing or its invalidation. In order to solve the problem of routing and get multi-constrained QoS (quality of service) measures in VANETs, numerous research works have been conducted on it. Another aspect for application is traffic collision control. Unfortunately, there are hardly any sensors anywhere which make it basically impossible to obtain continuous information about traffic states. Accurate predictions about traffic flow are difficult because road accidents and public events cannot be predicted in advance and both these can essentially affect the traffic flow. this article reports the framework of Seagull Optimization Deep Learning for predicting accuracy and experiments various nodes. this framework exhibition focuses on different vehicle flows, position rates, and road organization typologies. Our framework effectively reduces the general waiting time of vehicles during aggregation and shortens the vehicle travel season even when the location rate is low. © 2024 IEEE.

关键词： Congestion control (communication)

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Not All SWAPs Have the Same Cost: A Case for Optimization-Aware Qubit Routing 28

Not All SWAPs Have the Same Cost: A Case for Optimization-Aw...

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28th Annual IEEE International symposium on high-performance computer architecture (HPCA)

作者： Liu, Ji Li, Peiyi Zhou, Huiyang North Carolina State Univ Raleigh NC 27695 USA

ISBN: (纸本)9781665420273

Despite rapid advances in quantum computing technologies, the qubit connectivity limitation remains to be a critical challenge. Both near-term NISQ quantum computers and relatively long-term scalable quantum architectures do not offer full connectivity. As a result, quantum circuits may not be directly executed on quantum hardware, and a quantum compiler needs to perform qubit routing to make the circuit compatible with the device layout. During the qubit routing step, the compiler inserts SWAP gates and performs circuit transformations. Given the connectivity topology of the target hardware, there are typically multiple qubit routing candidates. the state-of-the-art compilers use a cost function to evaluate the number of SWAP gates for different routes and then select the one with the minimum number of SWAP gates. After qubit routing, the quantum compiler performs gate optimizations upon the circuit with the newly inserted SWAP gates. In this paper, we observe that the aforementioned qubit routing is not optimal, and qubit routing should not be independent on subsequent gate optimizations. We find that with the consideration of gate optimizations, not all of the SWAP gates have the same basis-gate cost. these insights lead to the development of our qubit routing algorithm, NASSC (Not All Swaps have the Same Cost). NASSC is the first algorithm that considers the subsequent optimizations during the routing step. Our optimization-aware qubit routing leads to better routing decisions and benefits subsequent optimizations. We also propose a new optimization-aware decomposition for the inserted SWAP gates. Our experiments show that the routing overhead compiled with our routing algorithm is reduced by up to 69.30% (21.30% on average) in the number of CNOT gates and up to 43.50% (7.61% on average) in the circuit depth compared with the state-of-the-art scheme, SABRE.

关键词： quantum computing compiler optimization qubit routing

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An Unified Approach for Traffic Sign Classificationand Flow Prediction using Deep Learning Technique 15

An Unified Approach for Traffic Sign Classificationand Flow ...

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15th International Conference on Advances in computing, Control, and Telecommunication Technologies, ACT 2024

作者： Kavitha, D. Lekhana, Kommareddy Rajeshri, Vigrahala Rushitha, Chennareddygari School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (纸本)9798331300579

Efficient and safe traffic management is an important concern in modern urban environments. To address this challenge, we propose an integrated deep learning based solution that unifies traffic sign classification and traffic flow prediction using the LeNet architecture. the seamless fusion of these two critical tasks enables a holistic approach to traffic management, benefiting autonomous vehicles, traffic control systems, and road safety. In this unified approach, LeNet, a seminal convolutional neural network (CNN), serves as the backbone for traffic sign classification. Lenet Network is a very famous kind of configuration of convolutional neural networks that can be used to classify *** this paper, we have used Lenet to classify traffic signs, mainly used for selfdriving cars. Leveraging LeNet's capability to recognize traffic signs with high accuracy, we train it on a comprehensive dataset comprising 43 different classes of traffic signs. this dataset encompasses a wide range of shapes, colors, and conditions, allowing the LeNetbased classifier not only to identify individual traffic signs but also to provide valuable context for downstream traffic flow prediction. Extensive experiments on diverse datasets validate the effectiveness of our unified approach. We demonstrate superior traffic sign classification accuracy using LeNet, surpassing previous state-of- art methods. Additionally, our traffic flow prediction capabilities exhibit impressive accuracy and robustness across various traffic scenarios. Traffic flow prediction helps people for effective route planning so that the people can chose their routes to save and fuel and it helps to reduce traffic congestion. this research represents a significant step toward enhancing traffic management systems efficiency and safety by leveraging deep learning techniques. Our unified approach, combining LeNet- based traffic sign classification with traffic flow prediction, holds great promise for smarter and more r

关键词： Convolutional neural networks

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