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Artificial super intelligence: Barriers and limitations

AIP Conference Proceedings 2024年第1期3209卷

作者： Rahul Goyal Neha Sharma Amanpreet Kaur Student Department of Computer Science and Engineering Chitkara University Institute of Engineering & technology Chitkara University Punjab India. Assistant Professor Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India Professor Department of Computer Science and Engineering Chitkara University Institute of Engineering & Technology Chitkara University Punjab India.

In urban studies, artificial intelligence systems are a widely recognized technology that provides an alternate approach to dealing with complicated and dynamic issues. Despite the risks of implementing more powerful artificial intelligence, many big tech companies are working towards it. So, this paper aims to show the barriers to artificial intelligence. Also, this paper illustrates barriers in machines that prevent artificial superintelligence from being achieved. Further, the paper discusses how machine neural networks differ from human neural networks and the existential threats posed by artificial intelligence and superintelligence.

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IoT for Smart City Planning and Development

IoT for Smart City Planning and Development

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International Conference on Computational and Characterization Techniques in engineering & sciences (CCTES)

作者： Raman Tirpude A. Vinitha Ahmed Kateb Jumaah Al-Nussairi Hawraa Ali Sabah Maki Mahdi Abdulhasan Manar Kareem Lafta Department of Management Research Scholar Ramdeobaba University and Assistant Professor at Maharashtra National Law University Nagpur Department of Computer Science Karpagam Academy of Higher Education Coimbatore Al-Manara College for Medical Sciences Amarah Iraq College of Technical Engineering National University of Science and Technology Dhi Qar Iraq Department of Technology Al-Nisour University College Baghdad Iraq Department of Pharmacy Mazaya University College Iraq

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

Urban planning and development have undergone a complete transformation with the advent of the Internet of Things (IoT), and groundbreaking technologies have been made available for smart city efforts. The inclusion of Internet of Things (IoT) technology in the design and development of smart cities is examined in this article. The significance that these technologies play in improving resource management, modernizing urban infrastructure, and elevating people's quality of life is given special consideration. This paper evaluates existing Internet of Things (IoT) applications, like intelligent transportation systems, smart grids, and environmental monitoring, to show how the Internet of Things (IoT) might address common urban issues like traffic congestion, energy consumption, and environmental pollution. Furthermore, the essay examines case studies submitted by well-known smart cities to show how the Internet of Things (IoT) may be successfully used and how it affects the sustainability and efficiency of metropolitan areas. The results suggest that the Internet of Things not only facilitates real-time data collection and analysis but also fosters the creation of creative urban development ideas. Thanks to the research that was done, this study shows how the Internet of Things has the potential to impact future cities and make them more resilient, adaptable, and effective in meeting the needs of growing populations.

关键词： Pollution Smart cities Urban planning Transportation Data collection Real-time systems Smart grids Internet of Things Environmental monitoring Sustainable development

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AI-Based Cybersecurity Frameworks for 7G-Enabled Virtual Therapy Platforms

Cyber Security and Applications

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Cyber Security and Applications 2025年

作者： A. Jayanthiladevi Jayapandian Natarajan KP Arjun L. Godlin Atlas M. Arvindhan Daniel Arockiam Professor Department of Computer Engineering Marwadi University Rajkot Gujarat India Associate Professor Department of Computer Science and Engineering School of Engineering and Technology Christ University Kengeri Campus Bangalore Karnataka India Department of Computer Science and Engineering GITAM University Bengaluru Campus Karnataka India Associate Professor Department of Computer Science and Engineering Bharath Institute of Higher Education and Research Chennai Tamil Nadu Assistant Professor Amity School of Engineering & Technology Computer Science and Engineering Gwalior Madhya Pradesh India Associate Professor CSE Amity University Madhya Pradesh Gwalior 474005 India

The modern technology network plays an important role. Artificial intelligence to provide a higher solution for network security. As 7G networks emerge, virtual therapy platforms have begun to take center stage, offering fast, real-time interaction and high-definition immersive experiences. While the convergence of security in health care and mobility is in many ways even more integrated into our lives, it is fraught with cybersecurity risks. This article proposes an AI-based cybersecurity framework for a 7 G-based virtual therapy platform. The framework employs advanced machine learning (ML) algorithms, predictive analytics, and adaptive threat intelligence to protect the confidentiality, integrity, and availability of data. The article covers the core building blocks like anomaly detection, secure 7 G-based virtual therapy platform authentication protocols, and privacy-preserving techniques. AI-GN gives an overview of attack mitigation using AI with a comparative analysis of existing 5G/6G frameworks and solutions and their unique aspects needed for 7G environments. This article mainly focuses on threat detection using a deep learning method, also it aims to provide a continuous authentication process. The proposed model response time is decreased by nearly 30 percent, at the same time detection rate is increased to 98.5 percent in a 7G network.

关键词： AI-based cyber security framework Adaptive threat intelligence Anomaly detection Predictive analytics Threat detection

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Machine learning models for evaluating the benefits of business intelligence systems

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Journal of High Technology Management Research 2023年第2期34卷

作者： Tripathi, Mano Ashish Madhavi, Kilaru Kandi, V.S. Prasad Nassa, Vinay Kumar Mallik, Banitamani Chakravarthi, M. Kalyan Department of Humanities and Social Sciences Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj India Department of Business Management Velagapudi Ramakrishna Siddhartha Engineering College Kanuru Andhra Pradesh Vijayawada India Greenfields Andhra Pradesh Vaddeswaram India Department of Computer Science Engineering Rajarambapu Institute of Technology Rajaramnagar Maharashtra Sangli India Department of Mathematics Centurion University of Technology and Management Paralakhemundi Odisha Gajapati India Senior Assistant Professor School of Electronics Engineering VIT-AP University Andhra Pradesh Amaravathi India

Due to the uncertainty of the market and the intensity of rivalry, business owners and managers are often compelled to experiment with a wide variety of strategies for enhancing their company's performance. By enhancing the timeliness and quality of inputs to the decision-making process, Business Intelligence (BI) is one such idea and tool that combines operational data with analytical tools to show complex and competitive information to planners and decision-makers. Business intelligence (BI) tools help companies rapidly generate insights that guide managers toward operational efficiencies, lead them to new opportunities, and set them apart from the competition. The literature study shows that there is a debate about whether BI tools have an effect on the quality of decisions and the development of businesses. The present research explores the varied empirical facets of BI application through ML models. This study concluded with a discussion of how Machine Learning models can be used to assess the value of BI tools. Machine learning models, fed with historical data and a wealth of input features, can foresee the effect of new systems on metrics like revenue development, customer behavior, and inventory management. Using these models, businesses will be able to better evaluate potential investments in new tools and systems. © 2023 Elsevier Inc.

关键词： Decision making

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The Terms Estimation of S Robotics using MTRS as well as FEM

The Terms Estimation of S Robotics using MTRS as well as FEM

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Advance Computing and Innovative Technologies in engineering (ICACITE), International Conference on

作者： R. Gopal Mustafa Mudhafar Amjad Hashem Venkadeshan Ramalingam Dibyahash Bordoloi Muhammad Numan Ali Khan Myasar Mundher Adnan Zahraa N. Abdulhussain Assistant Professor Information and communication Engineering College of Engineering University of Buraimi Sultanate of Oman Department of Medical Physics Faculty of Medical Applied Sciences University of Kerbala Karbala Iraq Computer Engineer Department Al-kunooze University College Basra Iraq Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences– Shinas Sultanate of Oman Dept of CSE Graphic Era Hill University Adjunct professor Graphic Era Deemed to be University Dehradun Uttarakhand Faculty – Network Computing College of Computing and Information Sciences University of Technology and Applied Sciences – Shinas Sultanate of Oman Department of computers Techniques engineering College of technical engineering The Islamic university Najaf Iraq College of Technical Engineering National University of Science and TechnologyDhi Qar Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

This look at addresses the critical project of reaching dependable and precise proprioception in soft robots, it really is essential for his or her effective deployment in complicated environments. By integrating multi-tap resistive sensors in the robot’s form, our technique lets in for the distinct sensing of nearby deformations at various factors throughout the robotics’ frame, moving beyond traditional techniques that anticipate steady curvature. We beautify the decision of these deformations and couple the sensory facts with a strong finite detail technique (FEM) model. This included framework allows the correct estimation of the smooth robot’s form and the significance of outside forces applied to it, attaining a mean relative inaccuracy of about 4%. Additionally, our technique proves its software program in tender manipulation structures, in which it achieves thirteen% relative mistakes in external force estimation inside diverse zero to five N. The approach gives a promising direction for enhancing robotic interactions in sensitive and dynamic responsibilities, providing massive enhancements in robotic proprioception without reliance on outdoor monitoring generation.

关键词： Deformable models Deformation Force Estimation Soft robotics Robot sensing systems Software

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Advanced skin lesion segmentation and classification using adaptive contextual GLCM and deep learning hybrid models

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Egyptian Informatics Journal 2025年 30卷

作者： Vinit Kumar D.L. Shanthi Tummala Ranga Babu Narendra Kumar Rakesh Kumar Godi Dr Arun G Professor Galgotias College of Engineering and Technology Greater Noida India Assistant Professor Department of Computer Science and Engineering BMS Institute of Technology and Management Bangalore Karnataka India Professor Electronics & Communication Engineering R.V.R. & J.C.College of Engineering India Associate Professor Department of CSE Amity University Jharkhand Ranchi Jharkhand India Department of Computer Science Central University of Karnataka Kalaburagi 585367 India Assistant Professor Department of Computer Science and Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamilnadu India

One of the most common and dangerous types of cancer, skin cancer, especially melanoma, is distinguished by its quick spread and high death rate if left untreated. It is impossible to overestimate the significance of early detection and precise diagnosis as they are essential for successful treatment and greatly raise patient survival rates. To improve skin lesion segmentation and classification, this work presents a state-of-the-art image analysis technique that uses hybrid deep learning models in conjunction with the Adaptive Contextual Gray Level Co-occurrence Matrix (GLCM). DeepLabV3 + is used to segment the skin lesion from pre-processed input images with high accuracy. A Long Short-Term Memory (LSTM) network trained on characteristics collected by the Adaptive Contextual GLCM is included to further evaluate the segmented images. Because of this special combination, the model can accurately capture contextual information and tiny texture differences, both of which are essential for differentiating between benign and malignant skin lesions. Our suggested approach performs very well in tasks involving both segmentation and classification. In particular, it attains a remarkable 98.34 % accuracy, 99.13 % specificity, 97.25 % sensitivity, 97.15 % Dice coefficient, and 98.6 % F1 score. These findings demonstrate the method’s stability and dependability, which makes it a potentially useful instrument for raising the detection accuracy of skin cancer. This approach has the potential to transform early detection procedures by providing improved diagnostic accuracy and resilience, eventually contributing to better patient outcomes and more effective management of skin cancer cases.

关键词： Skin cancer Machine Learning Deeplabv3 Adaptive Contextual Gray Level Co-occurrence Matrix Long Short-Term Memory

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Introducing Service Learning To Under Graduate engineering Students Through EPICS

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Procedia computer science 2020年 172卷 688-695页

作者： Santosh Madeva Naik Surendra Bandi Hema Mahajan Assistant Professor Department of Mechanical Engineering HITAM Hyderabad Telangana 501401 India Associate Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India Assistant Professor Department of Computer Science Engineering HITAM Hyderabad Telangana 501401 India

This paper describes the introduction of doing engineering through the "engineering Project in Community Service (EPICS)" and the impact of a community-based learning approach on student motivation. Students get practical hands-on experience while working with non-profit organizations. Now a day’s students find it difficult to learn in the traditional way of classroom teaching chalk and board. They want to work on real-time problems with community partners by applying engineering knowledge. EPICS program helps in grooming the students’ skills in design, communication, teamwork, and develop leadership skills, facing the challenges, social awareness, multidisciplinary knowledge, solution-driven approach towards a problem and maintaining social relationships. EPICS is one of the IUCEE (Indo Universal Collaboration for engineering Education) initiative where students are exposed to study the community needs and frame an engineering problem to work on further.

关键词： EPICS Community partner Design process Team work Products

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Site reliability engineering for IOS mobile application in small-medium scale industries

Global Transitions Proceedings

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Global Transitions Proceedings 2021年第2期2卷 137-144页

作者： N Kavyashree Dr. Supriya M C Dr. Lokesh M R SSAHE Tumkur Assistant Professor Dr.AIT Bangalore Karnataka India Department of MCA SSIT Tumkur Karnataka India Department of computer Science and Engineering MITM Mandya Karnataka India

Software reliability is one and only the standard used to calculate the excellence of a software system. In this situation, we suggest a method to forecast software reliability in the primary creation of any process growth phases since from the designing stage only. Software mobile application has remained a significant tool in our lifetime, and several small-medium scale software progress companies capitalize the substantial resources to mark accessible prospects. The landscape of mobile atmospheres varies as of server environments owing to various issues, by means of the net, liveliness, battery-operated, and compatibility. Estimating and forecasting mobile application reliability remain the actual contests as of the variety of the mobile situations in which the applications are used, and the absence of widely available fault data. In addition, error information is optionally given in to the end-users. Hence, in what way to estimate reliability of mobile software application has remained more and more significant. In this paper, site reliability engineering (SRE) for mobile application is proposed for the reliability of software and is taken as a case study. Their failure is copied back to the exact features of mobile application is useful and powerful.

关键词： Fault report Mobile application Site reliability engineering Software forecast Software reliability SRGM Model

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Reinforcement learning: A novel approach towards drug discovery

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AIP Conference Proceedings 2023年第1期2477卷

作者： Nishath Ansari Amjan Shaik Patluri Harika Boga Varasree K. Purnachand Saikumar Tara Assistant Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Assistant Professor Department of Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Assistant Professor Department of Information Technology Institute of Aeronautical Engineering Dundigal Hyderabad Telangana India Associate Professor Department of Computer Science and Engineering B V Raju Institute of Technology Narsapur Telangana India Associate Professor Department of Electronics and Communication Engineering CMR Technical campus Hyderabad Telangana India

Reinforcement learning has been giving new strategies to ad-lib in the fields of medication and the drug industry. It is probably the best strategy among all the AI strategies. Reinforcement learning has been compelling to discover strategies that could treat the immense number of deadly infections. In this paper, a detailed new methodology about what Reinforcement Leaning and what is drug revelation and what are various techniques utilized in Reinforcement learning, how Reinforcement learning could be utilized in different utilization of medication plan such as target identification, hit discovery, hit to lead, lead optimization. The consequence of the paper illustrates the summarized data of different approaches for the development and discovery of drugs. A clear illustration of various work done in the field of drug discovery, discuss different pros and cons of Reinforcement learning as well emphasize primary challenges need to handle and how to defeat them in the future.

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ADVANCED ATTACK MITIGATION IN IOT GATEWAY PROTOCOLS

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computers & Security 2025年 157卷

作者： K. Praveen Kumar Dr. N. Suresh Kumar Assistant Professor Department of Information Technology Kakatiya Institute of Technology and Science Warangal (KITSW) Telangana India Research Scholar Department of Computer Science and Engineering GITAM University Visakhapatnam Andhra Pradesh India Associate Professor Department of Computer Science Engineering GITAM Institute of Technology GITAM (Deemed to be University) Visakhapatnam India

With the increasing number of users on the internet, numerous cyberattacks are becoming more and more common. Proper detection of these attacks by Intrusion Detection Systems (IDS) is extremely important, particularly for IoT networks. Deep learning methods have proved to be very promising for enhancing IDS performance. This paper presents an end-to-end system for attack detection and prevention in IoT networks with the use of data augmentation, preprocessing, feature extraction, and deep machine learning algorithms. The class imbalance is resolved using the Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE), and preprocessing operations normalize and clean the data for improved model performance. Feature extraction involves statistical features and Shannon entropy-based features, which are fused and sent through a feature selection process. A new 2D-LICM hyper-chaotic map combined with Walrus Optimization (2D-LICMHy-CM_WO) is used to enhance feature selection through enhanced search diversity, convergence rate, and eliminating redundancy. The Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) effectively extracts attack pattern dependencies for precise detection, and an Enhanced Double Deep Q-Learning Network (DoubleDQN) offers dynamic adaptive real-time countermeasures. Experimental findings prove that the proposed solution can obtain a 99.6% detection accuracy with an F1-score of 0.98 and outperforms current IDS models in false positive rate and detection time.

关键词： 2D-LICM hyper-chaotic map with Walrus Optimization (2D-LICMHy-CM_WO) Dense Convolutional Spatial Attention-based Enhanced Bi-GRU (DCSAtten_EBi-GRU) Double Deep Q-Learning Network (DoubleDQN) Enhanced Synthetic Minority Over-Sampling Technique (ESMOTE)

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