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2nd International Conference on Advancements in Smart, Secure and Intelligent Computing, ASSIC 2024

作者： Allam, Sangeetha Manjusha, Bollimuntha Vuyyuru, Asha Veeranjaneyulu, P. Rani, K. Pushpa Mishra, Sushruta Mlr Institute of Technology Department of Computer Science & Engineering Hyderabad India Hyderabad India and Ai&ds Hyderabad India Mlr Institute of Technology Department of Computer Science and Engineering Hyderabad Dundigal500043 India Kalinga Institute of Industrial Technology Deemed to Be University India

ISBN: (纸本)9798350370188

India's socioeconomic system is mostly dependent on agriculture. Farmers used to rely on word-of-mouth, but the current climate prevents them from doing so. The IT industry has grown in order to provide farmers with precise agricultural information, which has resulted in several advancements in the field of agriculture sciences. In the current climate, having access to contemporary technologies for agriculture is desirable. [1] Using machine learning methods, it is feasible to make predictions and create a precise model from the data. There are solutions for agricultural problems such crop rotation, crop forecasts, water and fertilizer needs, and crop protection. Due to the environment's unpredictable climatic elements, an effective method is required to expedite crop cultivation and support farmers in their production and management. It's possible that aspiring farmers can now engage in improved agriculture. [2] A farmer can receive a number of recommendations to aid with crop yield. Based on the properties of the soil, the timing of the sowing, and the location, farmers occasionally choose the incorrect crops. [3]Many as a result commit suicide, stop farming, and go to cities in search of employment. By taking into account all relevant aspects, including the planting season, soil, and geographic location, this research effort has developed a method to assist farmers in choosing crops. Following a careful review of crop data gathering, crop recommendations were given. © 2024 IEEE.

关键词： Crops

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Optimizing Tomato Leaf Disease Detection through Transfer Learning and Model Compression: A Comprehensive Survey 4

Optimizing Tomato Leaf Disease Detection through Transfer Le...

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4th International Conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Devi, G.Uma Yamsani, Nagendar Perla, Someswari Maram, Balajee Maram, Rohan Raj Kumar, Pinnamraju Veeresh University of Engineering and Management Computer Science and Engineering Department Jaipur India Sr University School of Computer Science and Artificial Intelligence Warangal India Gmr Institute of Technology Department of Cseai&ml Andhra Pradesh Rajam India Sr University School of Computer Science and Artificial Intelligence Telangana Warangal506371 India Chitkara University Becse HP Baddi India K K R and K S R Institute of Technology and Sciences Computer Science Engineering India

ISBN: (纸本)9798350364729

Maximise Tomato Leaf Disease Detection by Synergistic Model Compression Techniques and Transfer Learning. Basically, it is true that as technology continues to be integrated into agriculture, methods for identifying diseases have become accurate and reliable for the management of crops. The two main such approaches that have evolved are model compression, which reduces the processing requirements of deep neural network models, and transfer learning, which reuses already pretrained models. This work investigates the impact of different transfer learning strategies on model performance and efficiency. It explains the key fundamentals of transfer learning with regard to the tomato leaf disease detection application domain and covers different kinds of pretrained models. It investigates the application of model compression techniques, such as quantization, pruning, and knowledge distillation, and provides their impact on agricultural image analysis optimization. This survey is going to put forward common challenges, clarify the pros and cons of various methods, and offer reallife examples or success stories through a comparative review of prior work. © 2024 IEEE.

关键词： Fruits

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Machine Learning and Artificial Intelligence for Predictive Modeling in Antimicrobial Resistance Data Sets, Challenges, and Future Directions

Machine Learning and Artificial Intelligence for Predictive ...

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Smart Systems for applications in Electrical sciences (ICSSES), International Conference on

作者： Ajeeb Sagar Vinothkumar Kolluru Ujjwal Jaiswal Gaurav Kumavat Shreyas Rajendra Hole Ankit Kumar Computer science Engineering (AI&ML) Dayananda Sagar University Bangalore India Department of Data science Stevens Institute of Technology Hoboken U.S.A. Computer Science and Engineering PES UNIVERSITY Bangalore India

ISBN: (数字)9798331507671

ISBN: (纸本)9798331507688

The phenomenon of Antimicrobial Resistance (AMR) is emerging strategically rather quickly and efficiently on a global scale. Because of due AMR Corona-virus outbreaks, previously effective antibiotics are becoming obsolete. The situation is only becoming dire as a result of chronic infections, increased deaths and subsequent healthcare expenditures. Increasingly, Artificial Intelligence solutions and Machine Learning strategies are contributing towards optimizing large volume data sets and offer introduction of new intervention methodologies. Within the scope of the current work, we aim to focus on data set amalgamation through Open-Source genomic data along with phenotype and environmental data sets within ml frameworks. While genomic datasets explain portion of resistance around genes, phenotype datasets gather ‘recorded’ datasets of resistance patterns and Environmental datasets monitor the distribution of AMR loops. Opportunities for improving model ethics, integration of data, and addressable generalization gaps to subsequently increase prediction accuracy are studied in focus. Predictability in AMR detection through leveraging ai increases accuracy and optimizes clinical decision-making alongside public health responsiveness. With the help of exact models, it is also possible to predict alterations in the resistance evolution timeline with new resistance genes and while clinically useful to determine right treatment options, they can aid clinical decisions. ai and ml mechanisms enable streamlined AMR surveillance, effectively guiding the direction for inline policy and containment control centers for antibiotic sustainability frameworks. ai, along with ml developments and emergence, is looked upon to take the lead in delays in AMR deployment. ml would intensively work in maintaining the effectiveness of antibiotics and redefining infection healthcare management.

关键词： Ethics Phenotypes Explainable ai Antibiotics Genomics Predictive models Data models Bioinformatics Public healthcare Immune system

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Sliced Wasserstein adversarial training for improving adversarial robustness

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Journal of Ambient Intelligence and Humanized Computing 2024年第8期15卷 3229-3242页

作者： Lee, Woojin Lee, Sungyoon Kim, Hoki Lee, Jaewook School of AI Convergence Dongguk University-Seoul Seoul Korea Republic of Department of Computer Science Hanyang University Seoul Korea Republic of Department of Industrial Security Chung-Ang University Seoul Korea Republic of Department of Industrial Engineering Seoul National University Seoul Korea Republic of

Recently, deep-learning-based models have achieved impressive performance on tasks that were previously considered to be extremely challenging. However, recent works have shown that various deep learning models are susceptible to adversarial data samples. In this paper, we propose the sliced Wasserstein adversarial training method to encourage the logit distributions of clean and adversarial data to be similar to each other. We capture the dissimilarity between two distributions using the Wasserstein metric and then align distributions using an end-to-end training process. We present the theoretical background of the motivation for our study by providing generalization error bounds for adversarial data samples. We performed experiments on three standard datasets and the results demonstrate that our method is more robust against white box attacks compared to previous methods. © The Author(s) 2024.

关键词： Deep learning

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MyRailGuide – Railway Enquiry System: An Artificial Intelligence Approach

MyRailGuide – Railway Enquiry System: An Artificial Intelli...

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Advancement in Technology (ICONAT), International Conference for

作者： Leena I. Sakri S. R. Biradar Viraj Barge Rohit Padaki Anish Hegde Department of AI/ML SDMCET Dharwad India Department of Information Science and Engineering SDMCET Dharwad India

ISBN: (数字)9798350354171

ISBN: (纸本)9798350354188

MyRailGuide is an interactive Android app designed to enhance train travel experiences. It offers easy access to PNR status, real-time alerts on train schedules and delays, train timetables, and emergency contact numbers. The app features an ai-based chatbot for personalized customer service and voice-based interactions for hands-free use. It also supports QR code ticketing, promoting paper-free travel and environmental conservation. Users can submit feedback and complaints directly through the app.

关键词： Schedules QR codes Chatbots Market research Real-time systems Rail transportation Safety Delays Environmental management Faces

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Lexical Simplification of Arabic Educational Texts Through a Classification Approach

Lexical Simplification of Arabic Educational Texts Through ...

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International Conference on Artificial Intelligence and Smart Environment, ICaiSE 2022

作者： Nassiri, Naoual Berrichi, Safae Lakhouaja, Abdelhak Cavalli-Sforza, Violetta Mazroui, Azzeddine Department of Computer Science Mohamed First University Oujda Morocco Oujda Morocco School of Science and Engineering AI Akhawayn University Ifrane Morocco

ISBN: (纸本)9783031262531

Readers wishing to learn a new language need texts adapted to their reading level. Indeed, readers with language or learning difficulties encounter challenges in understanding texts containing complex words. Therefore, identifying these words and generating easier synonyms and definitions can enhance text comprehension. In order to confront these problems, we introduce in this study a lexical simplification approach to educational Arabic texts based on readability measurements. This approach involves the task of identifying complex words and proposing substitution candidates followed by a process of selecting the best substitution and replacing it in the original text. The performance of the approach is evaluated on two different corpora dedicated to readers of Arabic as a first foreign language. The obtained results are very encouraging and constitute a first attempt to automate the simplification of Arabic texts in order to support new learners. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Arabic language Classification Education L2 Readability Simplification

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Efficient Temporal Data Mining Technique Using Dynamic Time Warped LSTM for E-Commerce Recommendation Systems 3rd

Efficient Temporal Data Mining Technique Using Dynamic Time ...

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3rd International Conference on Intelligent Systems and Sustainable Computing, ICISSC 2023

作者： Rajassekharan, Dinesh Subramanian, Jayasundar Lazar, Arokia Jesu Prabhu Pichuka Veera Venkata Satya, Srinivas Ray, Samrat Ammasai, Viswanathan Sengan, Sudhakar Faculty of AI Computing and Multimedia Lincoln University College Petaling Jaya47301 Malaysia Department of Computer Science and Engineering Idhaya Engineering College For Women Tamil Nadu Chinnasalem606201 India Department of Computer Science and Engineering CMR Institute of Technology Hyderabad501401 India Department of Computer Science and Engineering Koneru Lakshmaiah Education Foundation Andhra Pradesh Vaddeswaram522502 India Dean Head of International Relations IIMS Pune411033 India School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science and Engineering PSN College of Engineering and Technology Tamil Nadu Tirunelveli627451 India

ISBN: (纸本)9789819783540

The rapid surge of the electronic commerce (Ecomm) industry has ushered in intensified competition, with platforms vying for customer attention and loyalty. Many Ecomm sites have been employing various tactics to stand out, with Recommendation Systems (RS at the forefront of their strategies. However, traditional RS faces challenges, mainly when interactions occur at irregular intervals. This article introduces the DTW + LSTM framework, a methodology combining the merits of Long Short-Term Memory Networks and Dynamic Time Warping to improve this work. This method requires consideration of DTW's time series data alignment capabilities to account for interaction interval variability. The LSTM is then designed to accurately record the key elements of user interactions in sequential order. Combining the outcomes of these two methodologies in the DTW + LSTM model presents a more flexible and extensive approach to generating online shopping recommendations. The design guarantees that forecasts continue to be reliable and accurate despite the inherent temporal deviations that can be observed in user data. This paper investigates the context of this approach, its benefits, and prospective developments, demonstrating its possibilities in the dynamic Ecomm market. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Accuracy CNN E-commerce F1-score GRU LSTM Machine learning

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Heart Diseases Prediction Using Machine Learning Techniques: A Case Study from Jordan 25

Heart Diseases Prediction Using Machine Learning Techniques:...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alazaidah, Raed Safaqah, Mohammed Abu Alzoubi, Haneen Omar, Hani Al-Safarini, Maram Y. Elsoud, Esraa Abu Elrashidi, Ali Alluwaici, Mo'Ath Owaida, Suhaila Abu Zarqa University Faculty of It Department of Data Science and Ai Zarqa Jordan Department of Mechanical Engineering Muscat Oman Applied Science Private University Faculty of It Department of Computer Science Amman Jordan Zarqa University Faculty of It Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Al-al-Bayt University Faculty of Information Technology Department of Computer Science Mafraq Jordan

ISBN: (纸本)9798331540012

Heart disease is one of the most common diseases in Jordan. It is a major reason of death among Jordanian adult citizens. Worldwide, an average of 56,000 people dies each day or one death every 1.5 seconds. Hence, this research is interested in the early prediction of this disease among Jordanian people. To achieve this main objective, Machine Learning (ml) is utilized through a large number of classification models and considering five well-known evaluation metrics. These classification models have been trained on a primary dataset that has been collected for the purpose of the research. The results revealed that RandomForest and RandomCommittee are the best classification models to handle the task of the early prediction of Heart disease in Jordan. © 2024 IEEE.

关键词： Adversarial machine learning

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Design and Development of Chip Design for Digital Electronic System: Evolution and Trends 1

Design and Development of Chip Design for Digital Electronic...

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1st International Conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Deep, Pragya Anand, John Rawat, Himani Kumar, Pankaj Kubba, Akshaya Karn, Ankriti IIMT College of Engineering Department of AI & DS Greater Noida India UP Greater Noida India Tula's Institute Department of Electronics and Communication Engineering Dehradun India Amity University Department of Statistics Kolkata India Chandigarh University Deparment of Computer Science Punjab Mohali India Tula's Institute Department of Computer Science & Engineering Dehradun India

ISBN: (纸本)9798331518578

System on-a-chip (SoC) achievement relies upon a very much planned, incorporated come nearer from a few disciplines, including application, plan, and gadget. From the viewpoint of the gadget, quick progressing VLSI innovation makes it conceivable to incorporate billions of semiconductors on a solitary chip, empowering the mix of various capabilities on a solitary chip. Since they rely upon these electrical interconnects at the actual level, on-chip correspondence models like transports and networks on-chip (NoC), which are utilized to empower between part correspondence in multi-processor systems on-chip (MPSoC) plans, are dependent upon the full scope of troubles and impediments that influence copper-based electrical interconnects. Various examination projects have begun looking at imaginative association choices, for example, on-chip optical interconnects, remote interconnects, and carbon nanotube-based interconnects, trying to beat the downsides of traditional copper-based electrical interconnects. An outline and flow research status for these three promising association advancements are introduced in this review. It additionally discusses the issues that should be fixed for every one of these advances before they can ultimately be utilized to supplant copper-based electrical interconnects. © 2025 IEEE.

关键词： Network-on-chip

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Transforming Agricultural Supply Chains: The Power of Blockchain Technology 3

Transforming Agricultural Supply Chains: The Power of Blockc...

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3rd International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, IITCEE 2025

作者： Mohammed, Shehu Singh, Arun Kumar, Gotte Ranjith Lovely Professional University School of Computer Applications Phagwara India Lovely Professional University Department of Computer Science and Engineering Phagwara India Sr University School of Cs & Ai Department of Cse Telangana Warangal506371 India

ISBN: (纸本)9798331515911

Implementing blockchain technology in agricultural supply chains has the potential to revolutionize and increase transparency, efficiency and trust between all parties. A variation of the white paper discussed how blockchain can be a transformative tech in addressing major challenges especially on traceability, fraud prevention and sustainability on agriculture. Blockchain (for those hearing about this for the first time) can provide a system where every step of even complex supply chains is perfectly recorded, enabling transactions to take place in a secure way and offering transparency, immutability, accountability. Case studies and real-world examples showcased throughout the paper depict how blockchain could enhance operational efficiencies, boost food safety, authenticate products, and cut waste across agriculture. While very promising, the implementation of blockchain technology in agriculture is not without its own issues;this includes obligatory industry standards as well as highly costly technical implementations. The paper also highlights the potential for blockchain to contribute to sustainable agriculture by allowing better tracking of environmental footprints and ethical sourcing. In summary, we propose that the flexibility of blockchain technology renders it an ideal approach to revolutionizing supply chains in the agriculture sector, as we continue down a trajectory towards increased levels of visibility and productivity with improved environmental outcomes. This paper contributes towards a more comprehensive understanding of the role of blockchain in scenario-based selection as it addresses both opportunities and challenges due to adoption of block chain which gives technology and agriculture a new future(directions). © 2025 IEEE.

关键词： Sustainable agriculture

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