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An Optimized Steganographic Technique for 24-Bit Digital Images using Slab Partitioning

An Optimized Steganographic Technique for 24-Bit Digital Ima...

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International Conference on ent Computing and Control Systems (ICICCS)

作者： Pinaki Pratim Acharjya Jayeeta Majumdar Subhabrata Barman Jayashri Deb Sinha Ramsundar Ghorai Arpan Ghosh Dept. of Computer Science and Engineering Haldia Institute of Technology Haldia West Bengal India Dept. of Computer Science and Engineering (Data Science) Haldia Institute of Technology Haldia West Bengal India

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

Based on image slabs and sheets, a novel kind of image steganography method for digital images has been proposed in this research work. Using the binary representation of the pixel, this approach splits the cover image into 24 fixed-size sheets. The secret message's binary counterpart is then incorporated into the cover image. It looks through each sheet's rows and columns to locate the one that most closely matches the secret message's binary counterpart. The row or column's position and how it differs from the message it stores in the LSB slab. Compared to previous ways, the proposed technique somewhat modifies the cover image.

关键词： Image quality Steganography Visualization Digital images Control systems Robustness Slabs

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Robust Multiclass Brain Tumor Classification: Leveraging Swin Transformer and Feature Optimization with Ensemble Learning

Robust Multiclass Brain Tumor Classification: Leveraging Swi...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Rahad Khan Rafiqul Islam Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Gazipur United International University Dhaka Bangladesh Dept. of Computer Science and Engineering Dhaka University of Engineering & Technology Dhaka Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Brain tumor is one of the most serious health problems, and early discovery is crucial to improving patient outcomes. Magnetic resonance imaging (MRI) is vital in early detection, diagnosis, and timely treatment. Due to the complexity and diversity of multiclass brain tumors, feature extraction becomes more difficult because it requires distinguishing between tumor categories, which typically overlap features in MRI. Traditional diagnostic approaches, while effective, frequently have limits in terms of accuracy, speed, and usability. This work presents a voting-based ensemble framework for multiclass brain tumor classification, which incorporates three variations of a pre-trained Swin Transformer for feature extraction. Among these features, we used only refined features to train machine learning models by applying several feature removal processes. Numerous experiments were conducted to evaluate the performance of the suggested model and compare the outcomes to existing transformer-based models, such as the Vision Transformer and the Swin Transformer. The experimental findings revealed that the model outperformed the other models, with a maximum accuracy of 97.56%.

关键词： Analytical models Accuracy Magnetic resonance imaging Computational modeling Brain tumors Predictive models Brain modeling Transformers Feature extraction Data models

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Credit Card Fraud Detection: Data Balancing Techniques and Model Interpretability

Credit Card Fraud Detection: Data Balancing Techniques and M...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Tanveer Kaiser Avishek Chowdhury Umme Humyra Nabila Pantha Singha Mohammad Hasan M. A. Rasel Dept. of Computer Science & Engineering Premier University Chattogram Bangladesh Dept. of Computer Science & Engineering Chittagong University of Engineering and Technology Chattogram Bangladesh Dept. of Artificial Intelligence Universiti Malaya Malaysia

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Credit card fraud detection is a crucial task in fi-nancial institutions to safeguard against fraudulent transactions. This study investigates the performance of Random Forest and Gradient Boosting models on a Nigerian credit card fraud detection dataset. Key features were selected using Chi-Square and Analysis of Variance (ANOVA) tests, and the models were evaluated across four data balancing techniques: no balancing (imbalanced), under-sampling, over-sampling, and hybrid sampling. The primary focus was on recall, as misclassifying fraudulent transactions can have significant financial implications. Gradient Boosting consistently outperformed Random Forest, achieving higher Recall, F1-Score, and AUC-ROC values across all experiments, particularly with over and hybrid sampling. To enhance interpretability, SHapley Additive exPlanations (SHAP) were applied to the Gradient Boosting model. SHAP analysis revealed that the amount of the transaction and average income expenditure were the most influential features in predicting fraud. Local and global explanations highlighted how specific feature values impacted the model’s decisions, while dependency plots uncovered interaction effects between key features. These findings contribute to the advancement of fraud detection methodologies, offering insights into effective strategies for mitigating financial risks associated with fraudulent transactions.

关键词： Analytical models Focusing Credit cards Boosting Prediction algorithms Data models Fraud Security Random forests Analysis of variance

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Cost-balanced Adaptive Replica Server Cache Management under Time-Varying User Request Density in CCDNs

Cost-balanced Adaptive Replica Server Cache Management under...

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International Conference on Communication Systems and Network Technologies (CSNT)

作者： Rohit Kumar Gupta Arka Prokash Mazumdar Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Manipal University Jaipur India Dept. of Computer Science & Engineering Malaviya National Institute of Technology Jaipur Jaipur India

ISBN: (数字)9798331531935

ISBN: (纸本)9798331531942

In today’s growing Internet, cost-effective on-demand provisioning of caching resources in Cloud-based Content Delivery Networks (CCDNs) is essential to preserve the cache hit ratio while reducing storage requirements. However, during the high and fluctuating request rates at different locations, the sudden increase in cache to mitigate the miss rate requires a balance between storage cost (from dynamic cache provisioning) and miss cost (from latency in retrieving content from the Origin Server (OS) or other Replica Servers (RSs)). In such an environment, some RS instances remain underutilized while others are overburdened across different locations and time intervals, which requires effective RS management to activate only sufficient RSs by adapting to the user demands. To overcome these challenges, a cost-balanced adaptive cache management approach is proposed for dynamic cache provisioning in CCDN. This method aims to dynamically manage the activation of RS to optimize resource utilization, and cache instance provisioning and content caching operations in these RSs are managed by introducing an Adaptive Cache Manager that retrieves content from the OS or other RSs by effectively balancing costs in different time intervals. The experimental results indicate that the proposed method reduces the number of active replica servers in CCDN across different time intervals while decreasing the average total cost by about 56% relative to the LRU caching policy on fixed cache and about 10% compared to the dynamic selective caching policy on RCW cache.

关键词： Cloud computing Adaptation models Costs Content distribution networks Machine learning Predictive models Servers Resource management Time-varying systems

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Industrial IoT Smart Manufacturing Systems based on Cloud Computing Energy Efficiency Analysis using Machine Learning Algorithms 3

Industrial IoT Smart Manufacturing Systems based on Cloud Co...

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3rd International Conference on Electronics and Renewable Systems, ICEARS 2025

作者： Chinnasamy, P. Khan, Patan Feridoz Sasikala, C. Swathi, Mavalluru Kumar, M. Prema Yadav, Ajay Kumar Kalasalingam Academy of Research and Education School of Computing Department of Computer Science and Engineering Tamilnadu India Mohan Babu University School of Computing Department of Computer Science and Engineering Andhra Pradesh Tirupati India Dept of Cse Andhra Pradesh Ananthapuramu India Audisankara College of Engineering and Technology Andhra Pradesh India Dept. of Ece Andhra Pradesh Bhimavaram India United Institute of Management Department of Computer Application Prayagraj India

ISBN: (纸本)9798331509675

With open connectivity and emergent computing, the Industrial Internet of Things (IIoT) combined with cloud computing resources provides significant breakthroughs in the field of industrial automation. These developments lead to the evolution of supplemental services, which improve automation in the production and industrial sectors. This research proposes novel technique in smart manufacturing system based on cloud computing with IIoT in energy efficiency using machine learning. Here the smart manufacturing process has been analysed based on IIoT with cloud computing network, where the energy efficiency is analysed using virtual machine-based reinforcement markov chain based linear regression. Experimental analysis has been carried out in terms of latency, energy efficiency, task response time, accuracy. proposed technique obtained 97% of Latency, 95% of Energy efficiency, 98% of Accuracy, 94% of Task response time. © 2025 IEEE.

关键词： Smart manufacturing

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Self Organized Anchor Group based Routing Protocol for IoT Initial Setup 17

Self Organized Anchor Group based Routing Protocol for IoT I...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Gupta, Naveen Kumar Aggarwal, Apeksha Yadav, Rama Shankar Kumar Nagaria, Rajendra Dr B R Ambedkar National Institute of Technology Jalandhar Center of Energy and Environment Dept. of Information Technology Jalandhar India Jaypee Institute of Information Technology Dept. of CSE and IT Noida India Motilal Nehru National Institute of Technology Allahabad Department of Computer Science and Engineering Prayagraj India Motilal Nehru National Institute of Technology Allahabad Department of Electronics and Comm. Engineering Prayagraj India

ISBN: (纸本)9798331531195

Geographical routing approach relies on local geographic information and performs multi-hop routing with the help of local decisions. The nodes are represented by 3D coordinates as some real IoT networks may be deployed in high buildings, hilly areas, underground or underwater. The geographical routing protocols follow the greedy path and suffer from void node problem (VNP). The traditional recovery policies follow a longer path around the boundary nodes of the void region which may go out of energy quickly. Thus, this paper proposes the concept of anchor group and presents an efficient anchor group routing protocol (EAGR) for IoT networks. The anchor group is treated as a sub-destination region where a group of nodes behaves like sub-destinations, which bypass the boundary nodes of the void to minimize the path length and divide the network traffic, in case of void in the network. The paper is also focused on considering the residual energy in the contention-based next-hop selection process. The experimental work has been carried out in the OMNET++ simulator's INET framework and observed that the proposed method is outperforming existing protocols. Moreover, it has reported a longer network lifetime and is able to survive longer than other existing geographical routing protocols on high load. © 2025 IEEE.

关键词： Routing protocols

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Gait Recognition in Forensic: Analyzing CCTV Footage for Criminal Profiling

Gait Recognition in Forensic: Analyzing CCTV Footage for Cri...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Shifana M Sona P N. Nanthini Dept. Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

The gait analysis helps to identify the features of individual through gait patterns which will assist the investigation. The closed-circuit television (CCTV) footage from the scene of the offense, can be collected through surveillance camera footage, traffic camera footage, dash- cam footage from the dashboard-mounted cameras of the vehicles, mobile phone recording by the victim, is collected for the purpose of gathering digital evidence. Image segmentation of the particular individual from the background scene is done through the collected CCTV footage. By observing the way of walking, movement of lower limbs during locomotion, and walking pattern such as step length, step with, step angle and foot angle which is somehow individualistic to particular person, helps of approximately find the of stature, gender, age, and the bodyweight of a suspect. The observed data is compared with the criminal database to identify the similarities of the suspect. This study helps in the further contribution to establish the physical and biological profile of the suspect for identification. Hence many forensic cases can be solved and criminals can be convicted using this gait analysis.

关键词： Legged locomotion Training Technological innovation TV Forensics Traffic control Cameras Reliability Gait recognition Testing

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Hardware Acceleration of Scalable Artificial Neural Network Inference on FPGA

Hardware Acceleration of Scalable Artificial Neural Network ...

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International Conference on Electrical, computer and Communication Engineering (ECCE)

作者： Nahin Ul Sadad Md. Nazrul Islam Mondal Dept. of Computer Science & Engineering Rajshahi University of Engineering & Technology Rajshahi Bangladesh

ISBN: (数字)9798350357509

ISBN: (纸本)9798350357516

Artificial intelligence (AI) relies on tasks like pattern recognition and object detection, but software-based implementations often struggle to meet real-time requirements. Field-Programmable Gate Arrays (FPGAs) have become a promising solution for hardware acceleration, offering parallelism, energy efficiency, and adaptability, making them ideal for accelerating Artificial Neural Networks (ANNs) in edge and low-power environments. This paper implements a scalable and efficient FPGA-based ANN inference model, incorporating a fully connected layer with sigmoid activation for non-linear processing. The design is evaluated for FPGA resource utilization and benchmarked against CPU and GPU implementations for computational time. Results show that the FPGA implementation achieves a 34,000x speedup over CPU-based inference and a 2,000x improvement over GPUs on computational time while consuming significantly fewer resources on FPGA (except IO).

关键词： Computational modeling Scalability graphics processing units computer architecture Parallel processing Real-time systems Resource management Artificial intelligence Field programmable gate arrays Hardware acceleration

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Fine-Tuning of Large Language Models for Solving DSA Problems Using QLoRA

Fine-Tuning of Large Language Models for Solving DSA Problem...

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Computing for Sustainability and Intelligent Future (COMP-SIF), International Conference on

作者： Pavan Kalyan S Arun Kumar B R Satish Kumar T Prem Raj S Pratik B Patil Pothireddy Harshini Dept. Computer Science and Engineering BMS Institute of Technology and Management Bangalore Karnataka

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

Fine-tuning large language models (LLMs) for domain specific tasks is often an expensive resource intensive procedure requiring large computing and memory resources. In this paper, We introduce finetuned-leetcode-CodeLlama-7b, a version of CodeLlama that was fine tuned for solving LeetCode problems, specifically. Using QLoRA (Quantized Low Rank Adaptation) techniques, we show that smaller models achieve high performance when solving Data Structure and Algorithm (DSA) problems, providing a cost effective alternative to larger models. The proposed incremental approach reduces memory and computational needs, keeping high accuracy while providing advanced problem-solving in resource constrained environments.

关键词： Adaptation models Computational modeling Large language models Memory management Data structures Data models Computational efficiency Problem-solving Sustainable development Optimization

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technology and Community Interventions for food Security and Waste Reduction

Technology and Community Interventions for food Security and...

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Data Science, Agents & Artificial Intelligence (ICDSAAI), International Conference on

作者： Arunkumar K Muthupandi G S. Pavithra Dept. of Computer Science and Engineering Chennai Institute of Technology Chennai Tamilnadu India

ISBN: (数字)9798331537555

ISBN: (纸本)9798331537562

Food waste and insecurity using a multifaceted approach that includes nutrition-focused interventions in supportive housing, culturally appropriate food programs for Indigenous communities, and mobile applications to cut down on food waste. Geographical barriers, cultural significance, and logistical inefficiency are among the major barriers. This study assesses the impacts of digital food-waste apps, targeted nutritional interventions, and Indigenous food sovereignty initiatives using a mixed-methods approach. The integration of community-based and technology-driven solutions is unprecedented. The results highlight the worth of cooperative, sustainable approaches toward improving community well-being, environmental resilience, and food access.

关键词： Food waste Waste reduction Economics Food security Collaboration Medical services Mobile applications Global communication Cultural differences Resilience

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