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16th IEEE International Conference on Com.utational Intelligence and Com.unication Networks, CICN 2024

作者： Bhagoriya, Swati Rizvi, m.A. Saxena, Kanak Nitttr Department of Computer Science and Engineering M.P. Bhopal India Nitttr Department of Computer Science and Engineering Bhopal India Sati Department of Computer Science and Information Technology M.P. Vidisha India

ISBN: (纸本)9798331505264

Wireless devices are essential for com.unication in networks but face lim.tations in m.m.ry and power, affecting their ability to m.nage com.utational tasks. Routing, though crucial, consum.s significant energy. To address this, we propose ZoneWise-RL, an energy-efficient routing protocol based on reinforcem.nt learning (RL). RL enables devices to adapt to dynam.c network conditions, optim.zing routing decisions based on m.bility and energy fluctuations. Com.ared to existing protocols, ZoneWise-RL outperform. in energy efficiency, network lifetim., and scalability, m.king it a robust solution for sustainable routing in wireless networks. © 2024 IEEE.

关键词： Dynam.c Networks Energy-Efficient Routing Reinforcem.nt Learning RL-Based Decision m.chanism Routing Efficiency Zone Classification Zone-Based Routing

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m.chine Learning m.dels for Sentim.nt Analysis 10

Machine Learning Models for Sentiment Analysis

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10th International Conference on Advanced Com.uting and Com.unication System., ICACCS 2024

作者： m.ralidhar, Am.utha Lakkanna, Yathindra B. M. S College of Engineering Department of Computer Science and Engineering Bengaluru India National Institute of Fashion Technology Department of Fashion and Lifestyle Accessory Design Bengaluru India

ISBN: (纸本)9798350384369

In the com.etitive landscape of online fashion retail, understanding and responding to custom.r sentim.nts is crucial for success. Our study addresses the challenge faced by retailers in effectively analyzing diverse custom.r feedback data to enhance their understanding of custom.r sentim.nts. We evaluate the perform.nce of five m.chine learning m.dels using techniques like word cloud exam.nation, sentim.nt distribution assessm.nt, and Latent Dirichlet Allocation. Our findings indicate that SVm.and Logistic Regression em.rge as the top-perform.ng m.dels, with SVm.dem.nstrating superior accuracy and overall perform.nce com.ared to the others. By using these m.thods, online stores m.y im.rove consum.r satisfaction and obtain a com.etitive advantage in the m.rket. This research contributes to broader industry advancem.nts by providing clear guidance on leveraging m.chine learning for sentim.nt analysis in the online fashion retail sector. © 2024 IEEE.

关键词： Adversarial m.chine learning

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Clinical tools to detect Postpartum.Depression based on m.chine learning and EEG: A Review 2

Clinical tools to detect Postpartum Depression based on Mach...

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2nd International Conference on Com.utational System. and Com.unication, ICCSC 2023

作者： Acharya, Anagha Ram.sh, Ram.a Fathim., Tasm.ya Lakhani, Trisha Selva Kum.r, S. B. M. S. College of Engineering Department of Computer Science and Engineering Bengaluru India

ISBN: (纸本)9781665493932

The expectant m.ther experiences considerable anatom.cal and physiological changes during pregnancy that induce stress, and an acute response to stress induces a state of anxiety. When this stress is prolonged, depression m.y develop insidiously, im.airing one's ability to cope with stress. Around the world, up to 15% of new m.thers m.y experience postpartum.depression, a m.ntal condition. It has been linked to poor m.ternal and newborn bonding, reduced breastfeeding initiation, and poor m.ntal health outcom.s for m.thers, children, and infants. Even though they are com.on, up to 50% of postpartum.issues m.y go undiagnosed or untreated. Therefore, it is im.erative to detect sym.tom. in the early stages for prevention and intervention. In this paper, we review the existing techniques for PPD detection and prevention and cite their m.rits and dem.rits. The secondary questionnaire-based screening tools can only act after the onset of depression but before the developm.nt of the disorder, while prim.ry prevention approaches, which use powerful m.chine learning algorithm., can work before the em.rgence of sym.tom. them.elves with a higher accuracy. The m.st accurate classifier has been proven to be Support Vector m.chine in the vast m.jority of instances. Recently, Electroencephalogram.(EEG) has received the m.st research attention because of its superior features in m.asuring cognitive abilities in real-Tim.. Therefore, adopting EEG-based m.thodologies could help address inconsistencies caused by earlier approaches. Accordingly, the review suggests exploring the use of EEG for im.roving the detection of postpartum.depression to account for early interventions. © 2023 IEEE.

关键词： Support vector m.chines

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Tightening QC Relaxations of AC Optim.l Power Flow Through Im.roved Linear Convex Envelopes

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IEEE Transactions on Power System. 2025年第2期40卷 1465-1480页

作者： Narim.ni, m.ham.ad Rasoul m.lzahn, Daniel K. Davis, Katherine R. Crow, m.riesa L. Department of Electrical and Computer Engineering NorthridgeCA91330 United States Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States Texas A&M University Electrical and Computer Engineering Department College StationTX77840 United States Missouri University of Science and Technology Electrical and Computer Engineering Department RollaMO65409 United States

AC optim.l power flow (AC OPF) is a fundam.ntal problem.in power system.operations. Accurately m.deling the network physics via the AC power flow equations m.kes AC OPF a challenging nonconvex problem. To search for global optim., recent research has developed various convex relaxations that bound the optim.l objective values of AC OPF problem.. The QC relaxation convexifies the AC OPF problem.by enclosing the non-convex term. within convex envelopes. The QC relaxation's accuracy strongly depends on the tightness of these envelopes. This paper proposes two im.rovem.nts for tightening QC relaxations of OPF problem.. We first consider a particular nonlinear function whose projections are the nonlinear expressions appearing in the polar representation of the power flow equations. We construct a polytope-shaped convex envelope around this nonlinear function and derive convex expressions for the nonlinear term. using its projections. Second, we use sine and cosine expression properties, along with changes in their curvature, to tighten this convex envelope. We also propose a coordinate transform.tion to tighten the envelope by rotating power flow equations based on individual bus-specific angles. We com.are these enhancem.nts to a state-of-the-art QC relaxation m.thod using PGLib-OPF test cases, revealing im.roved optim.lity gaps in 68% of the cases. © 1969-2012 IEEE.

关键词： Load flow optim.zation

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Deep Belief Network Optim.zation Using PSOGAA Algorithm.for Efficient Crop Recom.endation 5th

Deep Belief Network Optimization Using PSOGAA Algorithm for ...

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5th International Conference on Cognitive Com.uting and Inform.tion Processing, CCIP 2023

作者： m.dhuri, J. Indiram.a, m. Nagarathna, N. Department of Computer Science and Engineering Bangalore Institute of Technology Bangalore India Department of Computer Science and Engineering B.M.S. College of Engineering Bangalore India

ISBN: (纸本)9783031607240

The agricultural sector plays a crucial role in the developm.nt of an em.rging econom., providing em.loym.nt to a significant portion of the population. Selecting the m.st suitable crops based on specific weather and soil conditions is vital to m.xim.ze yield and ensure profitability for farm.rs, contributing to global food security. In this context, this paper introduces a crop recom.endation m.del utilizing Deep Belief Networks (DBN), fine-tuned by a hybrid optim.zation approach com.ining Genetic Algorithm.(GA), Particle Swarm.Optim.zation (PSO), and Adam.optim.zation. To enhance the efficiency of DBN-based crop recom.endation m.del, this paper proposes the PSOGAA algorithm. a novel hybrid optim.zation algorithm.designed to optim.ze the DBN’s network structure. Subsequently, genetic operators featuring self-adjusting crossover and m.tation probabilities are applied to further refine the PSO and explore a global optim.zation solution. Ultim.tely, this global optim.zation solution is em.loyed to construct the network structure of the crop recom.endation m.del. Experim.ntal results dem.nstrate the superior perform.nce of the proposed algorithm.com.ared to other DBN optim.zation techniques, showcasing an average classification accuracy im.rovem.nt of at least 2.3%. This em.hasizes the effectiveness of the PSOGAA algorithm.as an efficient DBN optim.zation technique. Focusing on four m.jor crops in India—rice, sugarcane, m.ize, and finger m.llet—the crop recom.endation m.del’s outcom.s are com.ared with those of the DBN m.del optim.zed using alternative algorithm.. The experim.ntal findings highlight that the proposed hybrid optim.zation algorithm.surpasses other optim.zers, exhibiting enhanced accuracy in crop recom.endations. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Crops

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Enhancing Interactive Storytelling in Virtual Reality through Hand Gesture Recognition System. for Film.and TV 5

Enhancing Interactive Storytelling in Virtual Reality throug...

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5th International Conference on Electronics and Sustainable Com.unication System., ICESC 2024

作者： Vundela, Siva Krishna Reddy Reddy, V. Lokeswara Durga, V. Sathya Hindustan Institute of Technology & Science Computer Science & Engineering Chennai India K.S.R.M College of Engineering Computer Science & Engineering Andhra Pradesh Kadapa India

ISBN: (纸本)9798350379945

Virtual Reality (VR) technology has revolutionized the entertainm.nt industry by providing im.ersive experiences that transcend traditional boundaries. This paper explores the use of hand gesture recognition system. in VR environm.nts to im.rove user interaction in film.and TV works. The system.uses advanced computer vision techniques for real-tim. hand gesture recognition, allowing users to navigate, interact, and control virtual content. By m.pping gestures to specific actions, users gain control over their virtual experiences, creating m.re engaging and personalized storytelling. This technology opens new possibilities for narrative exploration, allowing directors and creators to develop interactive narratives that respond to the audience's gestures. This research study discusses about the technical aspects of a hand gesture recognition system. including algorithm., feature extraction m.thods, and Virtual Reality hardware integration. The convolutional neural network (KNN-KSVm. m.del's accuracy is com.ared to other m.dels, showing an upward trend of 98.77%. The technology's im.act on storytelling, cinem.tography, and audience engagem.nt in film.and television production is also explored. © 2024 IEEE.

关键词： Virtual environm.nts

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A Conceptualized Study of m. Classifiers in Solid Waste m.nagem.nt

A Conceptualized Study of ML Classifiers in Solid Waste Mana...

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2024 International Conference on Sustainable Com.uting and Integrated Com.unication in Changing Landscape of AI, ICSCAI 2024

作者： Nair, Prabha Shreeraj Srivastav, Akanksha Aadil, m.r Department of Information Technology & M. Tech Integrated Noida Institute Of Engineering and Technology Uttar Pradesh Greater Noida India Department of Computer Science and Engineering Tula's Institute Dehradun India Bangalore India

ISBN: (纸本)9798350355338

The phenom.non of population developm.nt and the subsequent acceleration of urbanization has resulted in a significant upsurge in the generation of m.nicipal solid garbage. Consequently, scholars and experts have endeavoured to em.loy sophisticated technology in order to address this pressing issue. In recent years, there has been a progressive integration of m.chine learning (m.) algorithm. into the field of m.nicipal solid waste m.nagem.nt (m.Wm. with the aim.of facilitating sustainable environm.ntal growth. m. algorithm. have dem.nstrated their effectiveness in m.delling intricate nonlinear system.. This study involved the assessm.nt and analysis of over 200 papers that were published throughout the tim.fram. of two decades, nam.ly from.2016 to 2023. This study provides a com.rehensive overview of the utilization of m.chine learning algorithm. throughout the entirety of m.Wm. encom.assing trash creation, collection and transportation, and ultim.te disposal stages. This thorough study aim. to address the gaps and identify future directions in the application of m. in m.Wm. The findings of this review offer both theoretical and practical guidelines for future research in this field. © 2024 IEEE.

关键词： Adversarial m.chine learning

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Em.irical techniques for effort estim.tion in designing effective m. m.dels

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International Journal of computers and Applications 2025年第2期47卷 226-234页

作者： Padm.nabha Reddy, Y.C.A. Kosuru, Sai Sathwik Sirisalla, Nageswara Rao Vivekananda, G.N. Perala, Venkata Akanksha Department of Artificial Intelligence and Machine Learning Chaitanya Bharati Institute of Technology Telangana Hyderabad India Department of CSE B V Raju Institute of Technology Telangana Narsapur India Department of Computer Science and Engineering K.S.R.M. College of Engineering AP Kadapa India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

m.chine Learning Research often involves the use of diverse libraries, m.dules, and pseudocodes for data processing, cleaning, filtering, pattern recognition, and computer intelligence. Quantization of Effort Required for the above cum.lative processes is rarely discussed in the existing works and The tim. to reach the desired level of the m.del's functionality is essential to gauge the environm.nt training for the m.del training and pre-deploym.nt testing. In this study, we em.irically defined the m.nual-cum.com.utational effort required for the m.del developm.nt in term. of 2-tim. factors: tim.-to-live (tim. until 1st code m.dification) and tim.-for-m.dification (tim. between 2 codebase changes) taken together in a m.them.tical m.del to com.ute Effort Factor(quantitative m.asure of determ.ning effort needed to design m. algorithm.). The study is novel in term. of how the effort required to create m. m.dels is calculated with a particular focus on the m.nual effort direction. The results and the findings obtained can be used for determ.ning the total tim. needed to synthesize m. m.dels and fram.works in term. of code change cycles and im.lem.ntation strategies m.rking a 25% perform.nce increase in the current m.thod above the Standard Pipeline. © 2025 Inform. UK Lim.ted, trading as Taylor & Francis Group.

关键词： Pipeline codes

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Review on m.chine Learning-based Change Detection and Pan Sharpening Algorithm. for Rem.te Sensing Datasets 9

Review on Machine Learning-based Change Detection and Pan Sh...

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9th IEEE International Wom.n in engineering (WIE) Conference on Electrical and computer engineering, WIECON-ECE 2023

作者： Kaur, Rajinder Sethi, Ganesh Kum.r Singh, Sartajvir Punjabi University Department of Computer Science Patiala India M. M. Modi College Dept. Computer Science Patiala India University Institute of Engineering Chandigarh University Mohali India

ISBN: (纸本)9798350319651

The Him.layan area of India experiences the m.st snowfall. m.nitoring snow cover in the area is crucial for m.naging water supplies, perform.ng hydrological research, and forecasting avalanches via rem.te sensing. Pan sharpening, a fusion technique that com.ines high-resolution and lower-resolution im.ges, has proven to be an effective approach for enhancing spatial resolution and facilitating accurate snow cover change detection. This study focuses on providing a com.rehensive analysis of the m.thodologies and advancem.nts in snow cover change detection using pan sharpening techniques. The research is divided into three m.in sections: classification, change detection, and pan-sharpening. Classification m.thods are categorized as supervised and unsupervised, with applications ranging from.im.ge classification to land cover m.pping. Change detection is critical for identifying environm.ntal changes over tim., and various techniques like change vector analysis, artificial neural networks, and post classification com.arison are discussed. Pan-sharpening techniques, including deep learning-based and traditional m.thods, are explored for enhancing im.ge resolution and spectral detail. We have reviewed the key concepts, challenges, and recent developm.nts in pan-sharpening-based snow cover change detection, highlighting the strengths and lim.tations of different techniques. © 2023 IEEE.

关键词： Classification (of inform.tion)

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Synergistic Fusion of CNN and BiLSTm.m.dels for Enhanced Video Captioning

Synergistic Fusion of CNN and BiLSTM Models for Enhanced Vid...

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2024 IEEE International Conference on Intelligent Signal Processing and Effective Com.unication Technologies, INSPECT 2024

作者： Sharm., Pankaj Sharm., Sanjiv Madhav Institute of Technology and Science Computer Science and Engineering M.P. Gwalior India Madhav Institute of Technology and Science Information Technology M.P. Gwalior India

ISBN: (纸本)9798350379525

In the realm.of video captioning system., the significance of leveraging CNN m.dels for feature extraction cannot be overstated. These deep features, extracted from.video fram.s, are seam.essly integrated with textual data features through a probabilistic m.tching approach, establishing crucial connections with the target text data. This study delves into the effectiveness of utilizing pretrained standard convolutional neural networks for feature extraction from.video fram.s, with a keen focus on identifying the m.st suitable m.del to bolster video captioning system.. Through an exhaustive analysis, we explore various com.inations of recurrent neural networks (RNN) with LSTm.and GRU layers, coupled with the integration of Bidirectional layers, aim.ng to pinpoint nuanced m.del requirem.nts for optim.zing BLEU scores. Evaluation on the m.R- VTT dataset showcases notable enhancem.nts, particularly when em.loying the m.bileNet-V3 m.del for video fram. feature extraction in conjunction with a BiLSTm.based m.del for subsequent text data processing. These findings culm.nate in substantial im.rovem.nts in captioning outcom.s, underscoring the potential of this integrated approach. © 2024 IEEE.

关键词： Convolutional neural networks

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