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Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI), IEEE Conference on

作者： Aashdeep Singh Sakshi Dhawan Jyoti Verma Gurpreet Singh Amanpreet Kaur Dept. of Computer Science & Engineering Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Rajpura Punjab India Dept. of Computer Application Punjab Institute of Technology Rajpura (MRSPTU Bathinda) Rajpura Punjab India Dept. of Computer Science & Engineering Chitkara University Institute of Engineering & Technology Rajpura Punjab India

In the realm of robotics and autonomous systems, efficient path planning is a critical aspect for optimizing resource utilization and achieving mission objectives. This study explores the application of Ant Colony Optimized (ACO) algorithms to enhance the efficiency of robots in navigating and exploring unknown environments. Inspired by the foraging behavior of ants, ACO algorithms are employed to find the shortest paths by leveraging pheromone communication and decentralized decision-making. Ant Colony Optimization is then introduced as a bio-inspired solution to address these challenges. The experimental results demonstrate the capability of the ACO algorithm to efficiently explore complex environments, adapt to changes in the surroundings, and converge to optimal paths. Insights gained from these analyses contribute to the refinement of the ACO algorithm for diverse robotic applications. The findings of this research have practical implications for the development of autonomous robots operating in real-world scenarios, such as search and rescue missions, environmental monitoring, and surveillance. This study provides a valuable foundation for the integration of bio-inspired algorithms in robotics, advancing the state-of-the-art in autonomous exploration and path planning.

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Intelligent Framework for Task Placement and Resource Allocation for IoT in the Fog-Cloud Scenario

Intelligent Framework for Task Placement and Resource Alloca...

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Current Development in engineering and Technology (CCET), IEEE International Conference on

作者： Shifa Manihar Ravindra Patel Sanjay Agrawal Computer Science Engineering University Institute of Technology RGPV Bhopal India Computer Application University Institute of Technology RGPV Bhopal India Computer Engineering and Application NITTTR Bhopal India

In order to identify the latency-critical applications, this research develops an intelligent task placement and resource allocation framework (ITPRAF) that makes use of supervised feed forward neural networks. This algorithm runs through two stages. To decide whether to schedule a task for execution in the fog or send it to the cloud in the first part, this machine learning-based system uses the task's attributes as input. To ensure the best resource usage, jobs that are put to fog are reshuffled keeping into account the priority at the fog queue in the second phase. Four mechanisms for resource allocation in fog are presented in this research. Through the simulation results from various iterations, the reaction time, resource consumption, and delay satisfaction rate of each scenario were compared.

关键词： Schedules Machine learning algorithms Simulation Neural networks Delays Resource management Feeds

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A Comparative Analysis of Machine Learning Models for Colon Cancer Classification

A Comparative Analysis of Machine Learning Models for Colon ...

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Advances in Power, Signal, and Information Technology (APSIT), International Conference in

作者： Rashmi Ranjan Swain Debasish Swapnesh Kumar Nayak Tripti Swarnkar Department of Computer Application Siksha ‘O’ Anusandhan University Bhubaneswar India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan University Bhubaneswar India

One of the trickiest machine learning challenges is the automatic classification of cancer cells. Colon Cancer can strike anyone at any age, but it often strikes older persons. In recent years, several investigations have focused on developing machine learning algorithms that can automatically classify genes of various types of a particular disease, especially colon cancer. The classification of cancer has improved due to improvements in computational methods like machine learning (ML), which required little human interaction and inefficient computers. Since the classification of gene expression data has been extensively studied in conjunction with the advancement of gene technology, a wide variety of techniques, primarily neural network-related, have been applied to the analysis of medical data, which is primarily concerned with the large dimension and very small in quantity. In this work, five different ML techniques were implemented for the classification of colon cancer in different age groups. The implemented ML techniques are Random Forest (RF), Decision Tree (DT), Support vector Machine (SVM), Naive Bayes (NB), and K-Nearest Neighbour (KNN). It is observed that most of the implemented model has an average accuracy of more than 95%.

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AI-Powered Job Market Analysis: Transforming Workforce Trends and Employment Predictions

AI-Powered Job Market Analysis: Transforming Workforce Trend...

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Global Conference for Advancement in Technology (GCAT)

作者： Sanjana Purohit Mirza Shuja Sahil Walia Mukesh Kumar Karan Bajaj Vivek Bhardwaj School of Computer Applications JECRC University Jaipur Rajasthan India School of Computer Application Lovely Professional University Phagwara Punjab India Solutions Architect Snowflake Inc. Atlanta Georgia USA Department of Computer Application Chandigarh School of Business Chandigarh Group of Colleges Mohali Punjab India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India Department of Computer Science and Engineering Manipal University Jaipur Rajasthan India

ISBN: (数字)9798350376685

ISBN: (纸本)9798350376692

This analysis delves into the "AI-Powered Job Market Insights" dataset, offering a comprehensive exploration of how artificial intelligence (AI) and automation are influencing the modern job market. The dataset provides a snapshot of job listings across various industries, highlighting factors such as AI adoption levels, automation risk, and salary ranges. By examining these elements, we aim to uncover key trends and insights into how AI is reshaping employment opportunities and job market dynamics. The primary objective of this analysis is to identify and understand key patterns related to salaries, required skills, remote work, and job market trends influenced by AI. By employing various analytical techniques, we seek to reveal how AI adoption impacts compensation, skill requirements, and job growth, as well as how it correlates with the likelihood of remote work.

关键词： Industries Correlation Automation Employment Companies Market research Remote working Remuneration Artificial intelligence Qualifications

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Modality-Independent Regression and Training for Improving Multispectral Pedestrian Detection 7

Modality-Independent Regression and Training for Improving M...

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7th International Conference on Image, Vision and Computing, ICIVC 2022

作者： Ni, Han Wang, Wenna Yun, Shuai Zhao, Zixu Zhang, Xiuwei Big Data Application Technology National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Xi'an710072 China Northwestern Polytechnical University School of Computer Science Xi'an710072 China

ISBN: (数字)9781665467346

ISBN: (纸本)9781665467346

Multispectral pedestrian detection enables an all-weather detector by fusing the complementary information of RGB and thermal images. However, due to the modality difference and the spatial misalignment between RGB and thermal sensors, the positions of the same pedestrian in two different sensors may be different and some pedestrians may only be visible in one sensor. The existing methods mostly adopt unified annotations to supervise different modalities, this is not conducive to the learning of different modal features. Therefore, we propose a multispectral pedestrian detector based on MBNet with modality-independent regression modules, named MBPNet. It adopts the corresponding modality-independent annotation to train the regression branch for different modalities, separately. Then, a decision-level fusion strategy is introduced to fuse the regression results from different branches. Experiments show that the proposed method outperforms the state-of-the-art methods on the KAIST pedestrian detection dataset. © 2022 IEEE.

关键词： Regression analysis

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Oddity Based Botnet Recognition Utilizing AI Model in the Internet of Things Framework

Oddity Based Botnet Recognition Utilizing AI Model in the In...

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International Conference on Computation, Automation and Knowledge Management (ICCAKM)

作者： Rupa Rani Sudeshna Chakraborty Pooja Singh Neha Varshney Computer Science & Engineering ABES Institute of Technology Ghaziabad U.P India Department of Computer Science & Engineering Banasthali Vidyapith Rajasthan School of Computer Application and Technology Galgotias University UP India Department of Computer Science & Engineering ABES Institute of Technology Ghaziabad U.P India

Advanced hooligans are grabbing the opportunity to mull over related contraptions with their IoT adware. The majority of digital attacks target these devices due to the rapid evolution of IoT advancements. With the diversity of modern advancements such as Industry 4.0, IoT, and Modern IoT, the linked gadgets are gradually expanding. The malware is zeroing in on frail devices. In this particular circumstance, we suggested an abnormality-based simulated intelligence model that can capably recognize the threatening practices in the association using change hyper-limit Light GBM computation. The proposed framework can be reached to identify new sorts of impending assaults and also, accomplish a lightweight nature. With the most recent Bot-IoT dataset, our model is prepared. Furthermore, by utilizing the Destroyed technique, the model similarly monitored the dataset's lopsidedness condition. With only one helping cycle, our suggested model demonstrates 99.99% accuracy with <0.01% actual regrettable.

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Adaptive Beamforming in MIMO Systems Using Deep Learning

Adaptive Beamforming in MIMO Systems Using Deep Learning

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Vinithra Banu, T. Hussein, Layth Grover, Varsha Kumar, T. Arun Sharma, Rupesh Sudha, I. Prince Shri Venkateshwara Padmavathy Engineering College Chennai India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Kongu Engineering College Department of Cse India Ies University Ies Institute of Technology and Management Department of Electronics & Communication Engineering M.P. Bhopal India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350390759

One of the most significant procedures in MIMO system is the adaptive beamforming which is the technique of adjusting the direction of signal transmission for better wireless communication. This paper aims to investigate the application of deep learning in combination with adaptive beamforming for the improvement of the existing technologies and techniques having some limitations which prove efficient when working in certain applications and scenarios only. Utilizing the CNNs and RNNs, the proposed approach then determines ideal beamforming patterns from various datasets, synthetic simulations, as well as measured ones. In this paper, various approaches are employed which include the Super & Reinforcement learning techniques, which makes the deep learning-based models capable of learning in real-time the change of channel characteristics. Simulations and practical adaptations of changing protocols have illustrated substantial enhancements on SINR, BER, and system capacity. The conclusion indicates that the proposed DL based technique is better than the existing standards providing more considerably higher data rates with reasonable less error rates and possessing highly adaptive capability. From the literature, it is clear that deep learning has a significant use in adaptive beam forming;although it has significantly increased the computational load. For future wireless communication technologies such as 5G and beyond MIMO systems this work has demonstrated the promises of deep learning to improve such systems. © 2024 IEEE.

关键词： Convolutional neural networks

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Prevent Network-Related Attacks with Load Balancing in Cloud Services Providers

SSRN

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SSRN 2024年

作者： Khan, Ghufran Ahmad Olivier, Ametovi Koffi Jacques Anwar, Taushif Khan, Jalaluddin Ashraf, Zubair Department of Computer Science & Application Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh 522502 India Department of Computer Engineering and Applications GLA University Uttar Pradesh Mathura281406 India

The Cloud refers to the provision of computing resources over the Internet in an on-demand manner, allowing for the storage, processing, and distribution of data. it helps to run applications and software through interconnected computer networks that provide services to individuals and organizations. The use of cloud services is growing exponentially, which gives rise to hackers who scan and analyze potential network vulnerabilities in the cloud architecture daily. To focus on the above-mentioned issue, we propose a novel idea to prevent large-scale DDoS attacks on the cloud through the adaption of load-balancing technology. To fulfill this task, we establish a virtual network with enabled Bastion service and DDoS protection. Further, it generates a standard SKU public load balancer, including a front-end IP address, integrity probe, back-end configuration, and load balancing rule. Afterward, the virtual machine is set up, and simulation is conducted through the attack on the network, which is described in the experiment section. © 2024, The Authors. All rights reserved.

关键词： Digital storage

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Behavioral Time Management Analysis: Clustering Productivity Patterns using K-Means

Behavioral Time Management Analysis: Clustering Productivity...

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2024 IEEE International Conference on Communication, Computing and Signal Processing, IICCCS 2024

作者： Winster Praveenraj, D David Habelalmateen, Mohammed I. Shrivastava, Akrati Kaur, Amarpreet Valarmathy, A.S. Patnaik, Chintamani Prasad Christ University School of Business and Management Bangalore India The Islamic University College Of Technical Engineering Department Of Computers Techniques Engineering Najaf Iraq The Islamic University Of Al Diwaniyah College Of Technical Engineering Department Of Computers Techniques Engineering Al Diwaniyah Iraq Ies University Ies Institute of Technology and Management Department of Computer Science & Engineering M.P. Bhopal India Chandigarh Group of Colleges Chandigarh Engineering College Department of Computer Application Punjab Jhanjeri Mohali140307 India Prince Shri Venkateshwara Padmavathy Engineering College Chennai India Aditya Institute of Technology and Management Department of Mba Andhra Pradesh Tekkali India

ISBN: (纸本)9798350390759

This paper focuses on investigating the efficiency profile through the three-time management behaviors using the K-Means clustering method. In the case of the study, the data gathered from digital time management tools for 100 participants for one month was preprocessed to distil features surrounding productivity, including daily working hours, focus time, break duration and frequency, and task completion ratios. The four groups that were agreed upon through K-Means clustering differed in terms of time management behaviours and productivity. Insert table 6 IT cluster 1 worked long hours with high productivity owing to the fact that they are IT professionals but had a tendency of multitasking. Employment Cluster 2 (marketing and sales professionals) achieved both personal and work-related self-care but identified the need for more concentrated time per task. As for the differences in the breaks, it can be noted that cluster 3 (management and administration personnel) had significantly higher task completion times and focus times, but their break intervals needed to be optimized. Hypothesis 2 stated that there will be many hours of leisure for Cluster 4 (students and interns) imply that their work hours should be adjusted to several small tasks a day, and their rates of task completion should be increased. From the study, it is possible to stress that time management should be considered as an individual activity that requires specific approaches to the given subject area and to the learner in particular. Specifically, demographic profiling identified the roles that age and occupational status may play in averting or exacerbating productivity deficiencies: insights that could be actionable in specific scenarios. The implications of this research offer practical insights into individual and organizational time management, as the usability aspects of machine learning techniques were considered and their applicability established, which further extends the scope of time

关键词： Information management

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WOBTree:a write-optimized B+-tree for non-volatile memory

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Frontiers of computer science 2021年第5期15卷 37-51页

作者： Haitao WANG Zhanhuai LI Xiao ZHANG Xiaonan ZHAO Song JIANG School of Computer Science and Engineering Northwestern Polytechnical UniversityXi'An 710072China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical UniversityMinistry of Industry and Information TechnologyXi'An 710072China National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology Xi’An 710072China Department of Computer Science and Engineering University of Texas at ArlingtonArlingtonTX 76010USA

The emergence of non-volatile memory(NVM)has introduced new opportunities for performance optimizations in existing storage *** better utilize its byte-addressability and near-DRAM performance,NVM can be attached on the memory bus and accessed via load/store memory instructions rather than the conventional block *** this scenario,a cache line(usually 64 bytes)becomes the data transfer unit between volatile and non-volatile ***,the failure-atomicity of write on NVM is the memory bit width(usually 8 bytes).This mismatch between the data transfer unit and the atomicity unit may introduce write amplification and compromise data consistency of node-based data structures such as B+-*** this paper,we propose WOBTree,a Write-Optimized B+-Tree for NVM to address the mismatch problem without expensive *** minimizes the update granularity from a tree node to a much smaller subnode and carefully arranges the write operations in it to ensure crash consistency and reduce write *** results show that compared with previous persistent B+-tree solutions,WOBTree reduces the write amplification by up to 86× and improves write performance by up to 61× while maintaining similar search performance.

关键词： non-volatile memory B+-tree atomic granularity mismatch write amplification performance optimization

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