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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gourshettiwar, Palash Thakare, Rajesh D. Duddalwar, Prapti Shete, Rushikesh Gundewar, Swapnil Verma, Prateek Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India Yashavantrao Chavan College of Engineering Department of Electronics Engineering Maharashtra Nagpur440002 India Vishwakarma Institute of Technology Department of Electronics Engineering Maharashtra Pune411037 India St. Vincent Pallotti College of Engineering and Technology Department of Computer Science and Business Systems Maharashtra Nagpur440002 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India

ISBN: (纸本)9798331528713

The SMS spam detection Project provides a comprehensive framework for reliably identifying and classifying spam messages sent over SMS communication channels by fusing ML algorithms with NLP techniques. Using EDA, a dataset of labelled SMS messages identified as spam or ham is thoroughly studied to reveal patterns in the word count, sentence count, and message length distributions. Then, unstructured text is transformed into structured data that may be utilized with ML models through tokenization, punctuation and stop word removal, stemming, and other text preprocessing techniques. Feature engineering, which numerically represents text data using vectorization techniques like BoW and TF-IDF, plays a significant role. Various ML algorithms, including Gaussian Naive Bayes, Multinomial Naive Bayes, SVM, and ensemble approaches, are trained and evaluated. Performance metrics, including accuracy and precision, serve as standards for model selection. © 2024 IEEE.

关键词： Text messaging

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Navigating the Nexus: Exploring the Fusion of AI and Nanotechnology for Cutting-Edge Advances 1

Navigating the Nexus: Exploring the Fusion of AI and Nanotec...

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2023 1st DMIHER International Conference on Artificial Intelligence in Education and Industry 4.0, IDICAIEI 2023

作者： Izanker, Sakshi V. Dhole, Aman Kumar, Praveen Faculty of Engineering and Technology Department of Computer and Design Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798350338423

The convergence of artificial intelligence (AI) and nanotechnology has initiated a transformative journey, introducing innovative possibilities across various fields. This review article explores the dynamic interaction between these two disciplines, emphasizing their collaborative potential, notable accomplishments, and prevailing challenges. The integration of AI algorithms with nanotechnology has expedited material discovery, nanoscale characterization, personalized medical applications, and nano- electronics. AI's predictive capabilities have revolutionized the discovery of novel nanomaterials with customized properties, the enhancement of drug delivery systems, and the design of intricate nano-electronic components. However, this synergy is accompanied by challenges such as data quality, multidisciplinary cooperation, ethical considerations, and computational complexity. The fusion of AI and nanotechnology is poised to transform industries, unlock quantum advancements, and influence sustainable materials. As AI's capacities evolve, scholars are prepared to address challenges and harness the transformative potential of AI- nanotechnology integration. © 2023 IEEE.

关键词： Medical applications

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A Review of Machine Learning Algorithms for Detection of Breast Cancer 4

A Review of Machine Learning Algorithms for Detection of Bre...

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4th International Conference on Ubiquitous Computing and Intelligent Information systems, ICUIS 2024

作者： Tale, Abhay Barhate, Aditya Verma, Prateek Gourshettiwar, Palash Faculty of Engineering and Technology Department of Artificial Intelligence and Data Science Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Artificial Intelligence and Machine Learning Maharashtra Wardha442001 India Faculty of Engineering and Technology Department of Computer Science and Medical Engineering Maharashtra Wardha442001 India

ISBN: (纸本)9798331529635

Breast cancer is still a global health concern, chiefly affecting women, as it is one of the major causes of cancer mortality. For the therapy to be effective, early diagnosis of cancer is critical to raising the survival level and increasing the effectiveness of the procedure. The advancement in the applied technique of Machine Learning (ML) has brought further opportunities for early and effective diagnosis of breast cancer. This review paper provides insights into how different ML algorithms including the Support Vector Machine (SVM), Convolutional Neural Network (CNN), Random Forests, and many more have contributed to the detection of breast cancer. These algorithms have proved effective in providing solutions to various medical data and imaging including early diagnosis and treatment. The paper also looks at the use of ML with precision medicine to show that these technologies will help minimize false diagnoses and offer better treatment options. This review consolidates recent works and advancements to emphasize the prospect of ML in breast cancer management and calls for more studies to enhance its implementation in practice © 2024 IEEE.

关键词： Convolutional neural networks

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Game Theory as a Method for Assessing the Security of Database systems 29th

Game Theory as a Method for Assessing the Security of Databa...

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29th International Conference on systems engineering, ICSEng 2022

作者： Warzyński, Arkadiusz Labuda, Katarzyna Falas, Lukasz Schauer, Patryk Department of Computer Science and Systems Engineering Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (纸本)9783031274695

This paper addresses the topic of database security, which is a critical component of many systems. The goal of the work is to investigate the effectiveness of methods for ensuring database security. The work introduces an innovative way of evaluating the effectiveness of the launched attacks. A literature review identifies current solutions in areas relevant to database security and identifies the state of the art and science. The final analysis introduced cost estimation and transformation of the results into a payoff matrix, which allowed the use of decision-making methods from the field of game theory. No party, defender or attacker, obtained a dominant strategy, but the application of the min-max criterion showed that the defenders’ best strategy is to implement all means of protection, given the assumptions introduced. The presented evaluation method can be applied to decision making in cyber security and contribute to cost optimization in the organization. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Database systems

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A Novel Architecture Based on a Pyrrole-Functionalized Dithieno[3,2-b:2′,3′-d]pyrrole (DTP)-Type Conducting Polymer and Thiol-Modified Calixarene Derivative for Biophotovoltaic Solar Cells: Photocurrent and Hydrogen Generations via Both Photosynthesis and Respiratory System

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ACS Omega 2025年第21期10卷 21450-21462页

作者： Buyukharman, Mustafa Yildiz, Huseyin Bekir Bakim, Sumeyye Marti, Mustafa Esen Department of Physics Institute of Graduate Studies in Science Istanbul University Istanbul 34134 Turkey Department of Electrical-Electronics Engineering Faculty of Engineering and Natural Sciences KTO Karatay University Konya 42020 Turkey Department of Computer Engineering Faculty of Engineering and Natural Sciences KTO Karatay University Konya 42020 Turkey Department of Chemical Engineering Faculty of Engineering and Natural Sciences Konya Technical University Konya 42250 Turkey

This review explores the potential of biophotovoltaic devices (BPVs) as a sustainable solution for addressing the global energy crisis and combating climate change. BPVs generate renewable electricity from sunlight and water through the photosynthetic activity of microorganisms such as cyanobacteria and algae, which act as living photocatalysts. The study essentially focuses on improving photocurrent outputs through developing efficient anode materials. An innovative photoanode design is introduced employing cyanobacteria immobilized on a P(DTP-Ph-Pyr)/Calixarene-AuNP-modified surface. This design features a porous structure conducive to cyanobacterial attachment and efficient electron transfer. As a first step, the conductive polymeric film of 4-(4-(1H-pyrrol-1-yl)phenyl)-4H-dithieno[3,2-b:2′,3′-d]pyrrole (DTP-Ph-Pyr) monomer was coated onto a gold electrode via electropolymerization method. Then, a mixture of thiol- and carboxylic group-modified calixarene and gold nanoparticles (AuNPs) was applied to enhance the photoelectrode’s performance. The surface of the modified electrode enabled the successful immobilization of Leptolyngbya sp. cyanobacterial cells, providing a reliable interface for efficient photocurrent and hydrogen generation. Calixarenes and their derivatives act as favorable agents for cyanobacterial immobilization due to their specific configurations. Moreover, the formation of covalent bonds between the carboxyl groups of calixarenes and the amino groups in cyanobacteria facilitates the robust immobilization of cyanobacterial cells while maintaining their well-ordered structural integrity and organized cellular architecture. A complementary cathode structure, employing aniline-modified Pt nanoparticles, facilitates the reduction of protons to generate hydrogen gas. Overall, this study underscores the promise of BPVs as feasible clean energy technologies and introduces innovative methods to improve their efficiency and sustainability. © 2025 The Au

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Lightweight Privacy Preserving Framework at Edge Layer in IoT 1

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10th International Conference on Frontiers of Intelligent Computing: Theory and Applications, FICTA 2022

作者： Agrawal, Kavita Chittineni, Suresh Reddy, P. V. G. D. Prasad Subhadra, K. Diaz, Elizabeth D. Department of Computer Science and Engineering Chaitanya Bharathi Institute of Technology Hyderabad India Department of Computer Science and Engineering GITAM Deemed to be University Vishakhapatnam India Department of Computer Science and Systems Engineering Andhra University Vishakhapatnam India Department of Computer Science and Engineering University of Texas Arlington United States

ISBN: (数字)9789811975240

ISBN: (纸本)9789811975233

The Internet of Things is helping us to make our lives comfortable. IoT devices used here are resource-constrained in terms of memory and CPU as well as they lack basic security design. When everyone is using IoT, there are high chances of their personal data getting exposed to the outside world. For real-time application, data need to be processed at the edge layer directly instead of doing the entire processing and analysis at cloud. Privacy preserving at the edge layer in the Internet of Things is a challenging task. We proposed a lightweight privacy preserving framework that uses the concept of blockchain, zero knowledge proof algorithm, principal component analysis, and digital signatures. Here for the mining process in blockchain, Trust concept is being used instead of Proof of Work (POW) and Proof of Stake (POS) as it consumes fewer resources and our edge layer is resource constrained. This could be a feasible solution and can be deployed at the edge layer in the Internet of Things for preserving privacy. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Internet of things

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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Enhancing crop yield prediction in Senegal using advanced machine learning techniques and synthetic data

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Artificial Intelligence in Agriculture 2024年第4期14卷 99-114页

作者： Mohammad Amin Razavi A.Pouyan Nejadhashemi Babak Majidi Hoda S.Razavi Josue Kpodo Rasu Eeswaran Ignacio Ciampitti P.V.Vara Prasad School of Electrical and Computer Engineering University of TehranTehranIran Department of Biosystems and Agricuttural Engineering Michigan State UniversityMIUSA Department of Computer Engineering Khatam UniversityTehranIran Department of Computer Science and Engineering Michigan State UniversityEast LansingMIUSA Department of Agronomy Faculty of AgricultureUniversity of JafinaKilinochchSri Lanka Department of Agronomy Kansas State UniversityManhattanKSUA Feed the Future Sustainable Intensification Innovation Lab Kansas State UniversityManhattanKSUA

In this study,we employ advanced data-driven techniques to investigate the complex relationships between the yields of five major crops and various geographical and spatiotemporal features in *** analyze how these features influence crop yields by utilizing remotely sensed *** methodology incorporates clustering algorithms and correlation matrix analysis to identify significant patterns and dependencies,offering a comprehensive understanding of the factors affecting agricultural productivity in *** optimize the model's performance and identify the optimal hyperparameters,we implemented a comprehensive grid search across four distinct machine learning regressors:Random Forest,Extreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Light Gradient-Boosting Machine(LightGBM).Each regressor offers unique functionalities,enhancing our exploration of potential model *** top-performing models were selected based on evaluating multiple performance metrics,ensuring robust and accurate predictive *** results demonstrated that XGBoost and CatBoost perform better than the other *** introduce synthetic crop data generated using a Variational Auto Encoder to address the challenges posed by limited agricultural *** achieving high similarity scores with real-world data,our synthetic samples enhance model robustness,mitigate overfitting,and provide a viable solution for small dataset issues in *** approach distinguishes itself by creating a flexible model applicable to various crops *** integrating five crop datasets and generating high-quality synthetic data,we improve model performance,reduce overfitting,and enhance *** findings provide crucial insights for productivity drivers in key cropping systems,enabling robust recommendations and strengthening the decision-making capabilities of policymakers and farmers in datascarce regions.

关键词： Crop yield prediction Variational auto encoder Pattern recognition on spatiotemporal and physiographical variables Synthetic tabular data generation Ensemble learning

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Numerical simulation and optimization of a circular open channel for fish farming using Computational Fluid Dynamics (CFD)

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Water-Energy Nexus 2023年第1期6卷 96-111页

作者： Jebelli, Ali Zare, Mohammad Saeid Lotfi, Nafiseh Yagoub, Mustapha C.E. Department of Mechanical Engineering University of Alberta Edmonton Canada Faculty of New Science and Technologies Tehran University Tehran Iran Department of Engineering and Design Robotic Century Inc Alberta Canada School of Electrical Engineering and Computer Science University of Ottawa ON Canada

Open canals are one of the most common and cost-effective methods in water supply networks;they are widely used in different industries such as agriculture, water and sewage treatment, urban drainage, pisciculture and water parks. Due to global warming, the demand in clean energy to replace fossil fuels has caused using water turbines in riverbeds and aqueducts to be important. On this basis, optimizing key parameters such as flow motion and canal turbulence is crucial to reduce breakdowns while designing canals. In fact, the inappropriate adjustment of velocity and pressure within the canals may lead to the system failure: imbalance between inlet and outlet will increase the water level and the pressure on canal walls thus, leading to breakdown. In this paper, a circular open canal was designed for a pisciculture system. The velocity and height of the water canal is controlled by the canal inlet and outlet system and the water flows continuously inside the canal. By keeping the canal water volume constant in any time and the flow motion with constant velocity, the system makes the fishes feel infinite movement. Furthermore, the water particles and impurities (e.g., food and fish feces) are removed by the outlet from the canal bottom, transferred to the filtration system, and returned to the fish farm by the canal inlet after the filtration procedure;the mentioned technique causes the water canal to be kept at its optimal level. Computational Fluid Dynamics (CFD) has been used to simulate the canal flow. Solving the Navier-Stokes equations numerically and assuming incompressible, unsteady, and two-phase flow, the parameters of the canal flow were extracted. Also, by mounting the system outlet along the path of water movement, greatly reduces the adverse effects of the outlet suction force on the canal main flow. Moreover, by dividing the canal inlet with guide vanes, the inlet has been modified for the entrance of the clean water simultaneously with the distribution

关键词： Computational fluid dynamics

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CRISP: Cloud resilient infrastructure for self-healing platforms in dynamic adaptation

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International Journal of Information Technology (Singapore) 2024年 1-14页

作者： Rawas, Soha Samala, Agariadne Dwinggo Fortuna, Aprilla Faculty of Science Department of Mathematics and Computer science Beirut Arab University Soha Beirut Lebanon Faculty of Engineering Universitas Negeri Padang West Sumatera Indonesia

As cloud computing evolves, traditional resilience strategies must adapt to new challenges. This paper introduces CRISP, Cloud Resilient Infrastructure for Self-Healing Platforms, a pioneering framework designed to enhance cloud resilience through autonomous fault management. By leveraging advanced machine learning, predictive analytics, and dynamic adaptation, CRISP Clouds provides a robust solution for real-time fault detection, diagnosis, and automated recovery. Our findings highlight CRISP Clouds’ ability to significantly improve system agility, minimize downtime, and optimize resource usage. This work presents a critical advancement in resilience engineering, setting the stage for future innovations in cloud technology. © Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Autonomous systems Cloud Computing Dynamic adaptation Fault Management Machine learning Predictive analytics Resilience enhancement Self-healing

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