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4th International Conference on Smart Systems: Innovations in Computing, SSIC 2023

作者： Jaya Krishna, Raguru Vamshi Krishna, B. Gopalakrishnan, T. Anagha, P. Kumar Sharma, Vijay Prasad Sharma, Devi Department of Computer Science and Engineering Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Manipal India Department of Computer and Communication Engineering and Department of Information Technology Manipal University Jaipur Jaipur India

ISBN: (纸本)9789819736898

Various techniques involved in analyzing social media data have become an important aspect of research. There is a need for efficient techniques to explore huge amounts of data for information prediction like product rating, political election prediction, viral marketing, and information cascading, etc. Nowadays multilayer social networks offer huge platforms for getting opinion reviews and recommendations about any user-interested topic. In multilayer social networks, multiple users are connected and the relationship between them is represented in different layers indicating different connections and interactions. Network structure is more complex and the social interactions between the individuals are dynamic in nature. This study reviews a thorough overview of multilayer social network predictive modeling methodologies and examines contemporary multilayer social network algorithms that have been published in the literature. In addition, we also discussed various key issues and algorithms to address the issues. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Prediction models

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A stacking ensemble approach for identification of informative tweets on twitter data

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International Journal of Information technology (Singapore) 2023年第5期15卷 2651-2662页

作者： Dasari, Siva Krishna Gorla, Srinivas Prasad Reddy, P.V.G.D. Department of Computer Science and Engineering GMR Institute of Technology GMRIT Rajam and Research Scholar Andhra University Visakhapatnam India Department of Computer Science and Engineering GITAM Deemed to Be University Visakhapatnam India Department of Computer Science and Systems Engineering Andhra University Visakhapatnam India

In the Present era, social media information plays an impact on our daily activities. Accurate media information identification is also challenging because of fake or spam information. Social media may receive this information via Twitter, Facebook, Youtube, LinkedIn, etc. In the context of natural disasters, people share their opinions via social media. Identifying such tweets which are informative or non-informative via these social media is a challenging issue. This work focuses on when a natural disaster happens and how social media information has been categorized, such as informative or non-informative. In this paper, we collected seven datasets from various disaster events from crisisNLP and categorized the tweet information using the stacking ensemble approach. In the proposed work, we considered ensemble stacking approach, in which for base learner we chosen the top five best-performed machine learning models where as for meta-learner we chosen the logistic regression. We tested our stacking model performance with matrices, namely accuracy, precision, recall, and F1 score. We also compared our approach results with other state-of-the-art models. © 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's institute of computer Applications and Management.

关键词： CrisisNLP embedding Machine Learning Stacking Tweets Vectorization

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An Effective Method for Distributing Workloads in Smart City using Sensor Networks Enabled by Fog

An Effective Method for Distributing Workloads in Smart City...

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2024 IEEE International Conference on Electronic Systems and Intelligent Computing, ICESIC 2024

作者： Nithiyanandam Velvizhi, R. Priyadharshini, S.P. Poongavanam, N. SRM Institute of Science and Technology Kattankulathur Campus Department of Computing Technologies Chengalpattu Kattankulathur India Sathyabama Institute of Science and Technology OMR Department of Computer Science and Engineering Chennai India St' Joseph College of Engineering OMR Department of Artificial Intelligence and Machine Learning Chennai India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Chennai India

ISBN: (纸本)9798331522988

By linking sensor nodes to fog nodes in critical locations, the Distributing Workloads in Fog Enabled Smart City (DW-FESC) technique efficiently handles the problem of distribution of workloads in smart cities. To get over the processing limits of IoT devices, this setup makes sure that complicated tasks are sent off to neighbouring fog nodes. The plan uses a load balancer and an updated greedy algorithm to evenly distribute work, which speeds up execution and keeps loads from becoming too heavy. Additionally, it has a deadline-based task execution mechanism that makes sure the most important jobs are done on time. The simulation results show that DW-FESC keeps the variance from the mean execution times to a minimum while considerably improving task execution times in comparison to other methods. With the use of fog computing, DW-FESC improves the dependability, efficiency, and responsiveness of smart city applications, leading to greener city life. © 2024 IEEE.

关键词： Fog computing

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Modelling and Impact Analysis of Antipode Attack in Bufferless On-Chip Networks

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SN computer science 2023年第3期4卷 1-9页

作者： Kunthara, Rose George Josna, V.R. Neethu, K. James, Rekha K. Jose, John Division of Electronics Engineering School of Engineering Cochin University of Science and Technology Kerala Cochin 682022 India Department of Computer Science Cochin University of Science and Technology Kerala Cochin 682022 India Department of Computer Science and Engineering Indian Institute of Technology Guwahati Assam Guwahati 781039 India

With advancements in VLSI technology, Tiled Chip Multicore Processors (TCMP) with packet switching Network-on-Chip (NoC) have evolved as the backbone of modern data intensive parallel systems. Manufacturers are looking at the prospect of using several third-party Intellectual Property (IP) cores in their TCMP designs due to strict time-to-market restrictions. Outsourcing IP from vendors across the world exposes System-on-Chip (SoC) designs to malicious implants such as Hardware Trojans (HTs). The performance of entire system is adversely affected by the presence of malicious HT in NoC routers, which can negatively disrupt communication between tiles. Generally, in buffered NoC, hardware trojans affect flits when they are in input buffers. Hardware trojans in bufferless NoC is a less explored area. In this paper, we model an HT that leads to antipode attack, which can occur on Permutation Deflection Network (PDN) of a bufferless router. In a bufferless router such as CHIPPER architecture, only the highest priority flit gets productive port, while other flits may or may not get productive port depending on port availability, leading to deflections. The modelled trojan misroutes all flits of HT-infected router to non-productive ports without modifying the flit control field. We investigate the effects of such an intermittent HT and analyse its effects at NoC level in terms of performance metrics such as average flit latency, deflection rate, throughput and router link utilisation. Experimental evaluations conducted on an 8 × 8 bufferless mesh NoC indicate that the modelled HT degrades network performance due to increased flit deflections and traffic across central routers which impacts system reliability. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Antipode attack Bufferless router Deflection routing Hardware trojan NoC security

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QoS Aware Energy Efficient Call Admission Control for 5G Networks 21

QoS Aware Energy Efficient Call Admission Control for 5G Net...

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21st IEEE India Council International Conference, INDICON 2024

作者： Poddar, Varsha Bit, Sipra Das Indian Institute of Engineering Science & Technology Department of Computer Science & Technology Shibpur India

ISBN: (纸本)9798350391282

The rapid evolution of communication technologies has led to the emergence of 5G networks, promising unprecedented levels of connectivity, speed, and efficiency. Radio resource management in such a network is one of the important tasks. Call admission control is one of the mechanisms towards realization of such resource management. In this context, maintaining quality of service especially for bandwidth-hungry services (e.g. video gaming) is of utmost importance. Further, 5G network being an ultra-dense one and requiring to admit huge number of services, a large number of cells including macro and small are deployed to cater the need of increasing bandwidth for maintaining QoS. This results in sharp increase of energy consumption. Thus, in this work we propose a call admission control scheme for 5G network targeting to improve QoS while making the scheme energy efficient. Unlike many existing works, we address QoS and energy efficiency together. We target to enhance QoS by reducing blocking probability and end-to-end-delay, and enhancing throughput. This is attained by serving the request by either BS of the MC alone or in association with a suitable BS of the co-located SCs depending on the bandwidth requirement of the services. The suitability of the SC is judged by certain parameters including distance of the SC-BS from the requesting UE, which makes it energy efficient. Finally, the performance of the proposed scheme is evaluated through Python based simulator. Our scheme records on average 79% and 58.8% improved performance over its competitor [7] for blocking probability and end-to-end delay respectively while keeping the overall improvement in throughput by 63.92% in the network. Further, we claim that our scheme saves energy on average by 60% and 20% over one state-of-the-art [10] for macro and small cell respectively. © 2024 IEEE.

关键词： Blocking probability Call admission control End-to-end delay Load balancing Macro/small cell QoS

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Quantum Generative Adversarial Network for Autonomous Drone Navigation in Urban Wind Zones 6

Quantum Generative Adversarial Network for Autonomous Drone ...

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6th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2025

作者： Swarnalatha, Anumula Archana, Nalluri Mukkapati, Naveen Selvakumar, Subramanian Tamir, Eneyachew Manikandaprabu, P. Department of Aeronautical and Aerospace Engineering KCG College of Technology Tamil Nadu Chennai600097 India Department of Computer Science and Engineering Vignan's Foundation for Science Technology and Research Andhra Pradesh 522213 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh 522502 India Department of Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Electrical and Computer Engineering Bahir Dar Institute of Technology Bahir Dar University Bahir Dar Ethiopia Department of Mechanical Engineering Sona College of Technology Tamil Nadu Salem636005 India

ISBN: (纸本)9798331522667

Major challenges are observed when autonomous drones fly in the complex, changeable environment such as wind zones regarding accurate path planning, avoiding the object, and completing tasks. Lack of flexibility in different situations reduces the reliability of conventional models in real time. To overcome these difficulties the Savitzky-Golay Denoising CSWin Quantum Generative Adversarial Network with Emperor Penguin Optimizer known as SGDCQGANNet-EPO was developed. The RGB data and GPS-estimated wind forces in this model undergo Savitzky-Golay Denoising;this model does not rely on global data, only RGB camera data and GPS data. Recently a CSWin transformer (CSWin-T) makes feature extraction and obstacles detection as well as wind angle, wind speed and wind position identification easier. Quantum Generative Adversarial Networks (QGANet) are then used for optimized path planning, and the model is enhanced by the Emperor Penguin Optimizer (EPO) to fine tune the generator and discriminator loss functions. More accurate compared to current approaches in accuracy and efficiency, the model achieves an accuracy of 99.28%, a low RMSE of 0.38, a recall of 99.25%, and EVA of 0.95. In Confidence using this new technique, the free roaming and self-organizing drone maneuvers in complex environments achieved remarkable results. © 2025 IEEE.

关键词： Generative adversarial networks

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Image Processing: Unlocking the Secrets of Visual Information 2

Image Processing: Unlocking the Secrets of Visual Informatio...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Lavanya, D. Bhushan, Kavi Kant, Gur Sharan Sountharraj, A.C. Srivastav, Akanksha Begum, M. Sharmila Sri Venkateswara College of Engineering Computer Science and Engineering Department Andhra Pradesh Tirupati India Ccs University Sir Chhotu Ram Institute of Engg. & Technology Department of Computer Science U.P Meerut India Dr. Ngp Art's and Science College Department of Computer Science with Data Analytics Coimbatore-48 India Tula's Institute Department of Computer Science and Engineering Dehradun India Maharaja Institute of Technology Department of Computer Science Mysore India

ISBN: (纸本)9798350384277

In the age of image processing, the hidden ability can be encoded with visual information that is not only observed but also painstakingly carried out;in this way, the complex secrets that are encoded in each pixel have been made clear. Image processing is the magic that has increased the amount of clarity, significance, and utility while transforming stationary arrays of images into dynamic narratives. The image's quality is typically influenced by several variables, such as temperature, light, and noise. Due to its persistent presence in various digital photographs, noise plays a crucial function in the image. Throughout the image coding, transmission, and development procedure, noise consequences could arise. The field of noise removal in image processing has grown to be quite interesting and vibrant. Recently, there has been a notable advancement regarding the filtering of various noise types for image processing in digital format. This research paper aims to discuss image processing and denoising various noises in images to improve overall quality. In terms of methodology systematic qualitative research has been conducted that helps the research gather information from a wide range of sources. Moreover, this paper finds that image processing has significant potential in this digital age. By denoising images, this phenomenon can be applied in different applications in every sector. © 2024 IEEE.

关键词： Image denoising

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A novel reformed normaliser free network with U-Net architecture for semantic segmentation

A novel reformed normaliser free network with U-Net architec...

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作者： Kalvapalli, Sai Prabanjan Kumar Mala, C. Punitha, V. Department of Computer Science and Engineering National Institute of Technology Tamil Nadu Tiruchirappalli India Department of Computer Science and Engineering Saranathan College of Engineering Tamil Nadu Tiruchirappalli India

Recently developed semantic segmentation network architectures include BatchNorm layer and skip connections. They are outperforming with latest training techniques, but the BatchNorm has implicit limitations such as gradients calculation and memory overhead. Hence this paper proposes a novel architecture named as NF-Unet, that combines the simple, flexible and general framework of NF-Nets and the unique architecture of encoder decoder format of U-Net network that can train with huge batch sizes. The backbone of the contracting path consists of NF-net UNet for encoding the image, for identifying the objects in the image. The proposed architecture achieved 87.37 and 70.12 mean intersection over union (mIoU) on train and test dataset and outperforms the other approaches in the literature in terms of number of parameters. © 2023 Inderscience Enterprises Ltd.

关键词： Semantic Segmentation

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Revolutionizing Culinary Quality: An Intelligent Food Grading System Employing Advanced Machine Learning Algorithms 10

Revolutionizing Culinary Quality: An Intelligent Food Gradin...

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10th International Conference on Advanced Computing and Communication Systems, ICACCS 2024

作者： Pratap, C Benin D'Souza, Aren Amblish, Athulya Mathew, Jane Mary Srivenu, K. Babu, Leno Varghese Karunya Institute of Technology and Sciences Department of Electrical and Electronics Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Department of Food Processing Technology Coimbatore India

ISBN: (纸本)9798350384369

In this pioneering research, a transformative paradigm in culinary quality assessment through the implementation of an Intelligent Food Grading System (IFGS) powered by state-of-the-art machine learning algorithms is introduced. The primary objective is to revolutionize conventional food quality evaluation methods by introducing a dynamic, precise, and automated approach. The IFGS employs advanced machine learning techniques to comprehensively analyze diverse food attributes, including taste, texture, freshness, and nutritional content. This system provides a standardized and objective framework for evaluating food quality. Moreover, it caters to consumer expectations by ensuring transparency and reliability in the assessment of food products. The study delves into the intricacies of the machine learning algorithms incorporated in the IFGS, emphasizing their adaptability and continuous learning capabilities. The synergy between technological innovation and culinary expertise presented in this work encourages a broader conversation within the culinary community, inviting chefs, researchers, and enthusiasts to embrace a future where cutting-edge technology enhances and refines the culinary experience. © 2024 IEEE.

关键词： Food chemistry

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GENTEEL-NEGOTIATOR: LLM-Enhanced Mixture-of-Expert-Based Reinforcement Learning Approach for Polite Negotiation Dialogue 39

GENTEEL-NEGOTIATOR: LLM-Enhanced Mixture-of-Expert-Based Rei...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Priya, Priyanshu Chigrupaatii, Rishikant Firdaus, Mauajama Ekbal, Asif Department of Computer Science and Engineering Indian Institute of Technology Patna India Dhanbad India School of Artificial Intelligence and Data Science Indian Institute of Technology Jodhpur India

ISBN: (纸本)157735897X

Developing intelligent negotiation dialogue systems that resolve conflicts and promote equitable, inclusive, and sustainable outcomes is at the forefront of advancing automated negotiation technology for social good. Negotiation involves balancing cooperation and competition to maximize value without causing offense. Using polite language fosters mutual understanding and creates a respectful and collaborative environment essential for successful negotiations in various domains. Considering this, in this paper, we propose a polite negotiation dialogue system, GENTEEL-NEGOTIATOR for social good applications to boost the overall quality of negotiation outcomes. We focus on developing a negotiation dialogue system for two key application areas, namely tourism and e-commerce. We begin by curating a unique negotiation dialogue dataset, NEGOCHAT for tourism. We further enrich the NEGOCHAT and Integrative Negotiation Dataset (IND) for e-commerce with various negotiation strategies. These datasets are then used to develop the GENTEEL-NEGOTIATOR, leveraging the Large Language Model (LLM) and mixture-of-expert (MoE)-based reinforcement learning approach. The proposed MoE-based method employs heuristic experts dedicated to negotiation, politeness, and dialogue coherence to facilitate the learning of diverse semantics by analyzing the dialogue context. A novel reward function with negotiation strategy congruence, politeness, dialogue coherence, and engagingness rewards is designed to guide the policy's learning for generating responses. Automatic and human evaluations on NEGOCHAT and IND datasets validate the effectiveness of GENTEEL-NEGOTIATOR in generating polite responses during negotiation while maintaining conversation goals, including coherence and engagingness. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Reinforcement learning

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