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3rd International Conference on Innovations in computer Science and Software engineering, ICONICS 2022

作者： Babar, M. Mubashir syed, Abeer Javed Asim, Shaarif Khan, Abdul Rafay Hai, M. Abdul Sir Syed University of Engineering and Technology Department of Software Engineering Karachi Pakistan

ISBN: (纸本)9781665461412

The warehouse is often the starting point in a supply chain. Ineffectiveness at a warehouse can cascade into the rest of the supply chain, hurting the customers' experience with the business in the long run. Warehouse Optimization System (WOS) play a crucial role in resolving a significant market issue by optimizing warehouse space and logistical procedures, documenting merchant supply and demand patterns, and making it simpler to handle goods that are about to expire, are out of stock, are broken, or have been abandoned by customers. This issue relates to space and storage in the warehouse, due to the fact that business intelligence, which may include algorithms for anticipating sales and managing warehouse capacity, may be connected to warehouse optimization systems. A case study is described in research of a warehouse optimization system, followed by a demonstration of the problem of gaps within the racks of warehouses. The algorithm solves the problem of fitting boxes with varying dimensions into racks. By using automation and careful design, warehouse optimization increases the effectiveness of how time, space, and resources are used in a warehouse, which enhances customer satisfaction. © 2022 IEEE.

关键词： Genetic algorithms

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Outlook 360 | A Practical Approach of Recruiting Talent Through Artificial Intelligence. 3

Outlook 360 | A Practical Approach of Recruiting Talent Thro...

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3rd International Conference on Innovations in computer Science and Software engineering, ICONICS 2022

作者： Zehra, Sani E Shakil, Areeba Fatima, Maryam Munawwar, M. Anas Khalil, Nida Sir Syed University of Engineering and Technology Software Engineering Department Karachi Pakistan

ISBN: (纸本)9781665461412

Intellectual capital acquired the Position of an asset in the fourth industrial revolution. An asset that would help companies to reach their targeted tier in the 'talent war' and help them maintain their bottom lines for significant setbacks. With the unforeseen consequences of Covid-19, this asset has become one benefit that needs to be retained. It implies that staying aware of the rapidly developing techniques for recruitment and implementing talented or skilled individual is one factor that assists companies in winning the talent war: The conversation objective, a potential employee to invest significant time and energy in a digitalized world. A better-performing e-recruitment application can bring employees to apply for the job. Thus, this paper sheds light on what upgrades and progressions are necessary for AI-based e-recruitment systems to draw and attain skilled individuals and help organizations achieve their target tier out in the corporate world. © 2022 IEEE.

关键词： Artificial intelligence

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An Automated Model for Fabric Weave Pattern Recognition and Classification Using Deep Learning Based Image Processing

An Automated Model for Fabric Weave Pattern Recognition and ...

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Quratulain Pasha Noreen Akram Ayesha Rafique Anam Akber Madiha Abbasi Telecommunication Engg. Department Sir Syed University of Engineering & Technology Karachi Pakistan Computer Science Department Sir Syed University of Engineering & Technology Karachi Pakistan

ISBN: (数字)9798331534271

ISBN: (纸本)9798331534288

Fabric weave type identification has many uses that adds to importance of advance classification methods; e.g fabric weave should be known in order to identify the most suitable use. But the manual inspection of fabric causes human errors such as, stressed, less focused, eye focus and mishandling of the apparatus. There are lighting effects which also degrades the performance of manual inspection. In recent years, computer vision based approaches have achieved remarkable performance in the fields of object recognition and image processing, thus opening a new door to develop an automated system for the recognition of woven fabric patterns for production of fine quality fabric according to customer needs. In this paper, we have devised an automatic weave pattern recognition mechanism based on convolutional neural networks(CNN). Initially three basic weave patterns were chosen i.e Twill, Plain and Satin. The results were analysed for its possible adaptability in the task of fabric weave pattern recognition. The classification results give the average accuracy of 72 % for all three samples whereas individually, Plain weave gives 100% accuracy, Twill gives 66% accuracy and satin weave gives 50% only, which indicates a room for improvement in future.

关键词： Wireless communication Accuracy Manuals Production Inspection Fabrics Weaving Pattern recognition Convolutional neural networks Optical reflection

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IoT-Based Soil and Weather Sensors for Crop Monitoring: A Step towards Smart Agriculture 7

IoT-Based Soil and Weather Sensors for Crop Monitoring: A St...

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7th Edition Global Conference on Wireless and Optical Technologies, GCWOT 2024

作者： Qamar, Nadia Ali, Mir Shabbar Siddique, Abdul Mohimeen Qureshi, Haris Uddin Ali, Uzair Khurram, Ayesha A. Sir Syed University of Engineering & Technology Civil Engineering Department Karachi Pakistan Limited Pakistan Karachi Pakistan Ned University Crop2x Pvt.Ltd National Incubation Center Pakistan

ISBN: (纸本)9798331534271

For a sustainable agricultural production, a thorough understanding of the interrelationship between the crop yield and soil properties, weather conditions, farming practice, and freshwater availability is essential. The conventional methods for monitoring the daily soil and weather conditions in a crop field have been found to be time-consuming and laborious, where a limited real-time field information leads to soil deterioration, excessive or deficient irrigation, and ultimately reduced crop water productivity and crop yield. Therefore, this study was conducted to investigate the effectiveness of IoT-based smart soil sensors and weather trackers for monitoring the soil and weather conditions as an alternative to the conventional methods. The initial crop growing phase was taken as the study period as 21st November 2022-10th December 2022. The results obtained for November showed the average daily soil moisure, soil temperature,, soil TDS, relative humidity, air temperature, and heat Index as 18.24 %, 17.7 °C, 200 ppm, 8126 %, 17.4 °C, and 21.6 °Crespectively, whilefor December, theaveragedaily soil moisture, soil temperature soil TDS, relative humidity, air temperature, and heat Indexwasfoundas 17.7%, 15.1 °C, 167 ppm, 75.8%, 14.53 °C, and 24.3 °C respectively. Thus, the IoT-based smart soil sensors and weather trackers can be adopted in place of the conventional methods for the real-time monitoring of soil and weather conditions that may assist the farm managers for a timely irrigation scheduling, better on-farm water management, soil conservation, and to achieve the desired crop water productivity and the per hectare crop yield. © 2024 IEEE.

关键词： Smart agriculture

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Selection of Metaheuristic Algorithm to Design Wireless Sensor Network

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Intelligent Automation & Soft Computing 2023年第7期37卷 985-1000页

作者： Rakhshan Zulfiqar Tariq Javed Zain Anwar Ali Eman H.Alkhammash Myriam Hadjouni Hamdard University Karachi75270Pakistan Electronic Engineering Department Sir Syed University of Engineering&TechnologyKarachiPakistan Department of Computer Science College of Computers and Information TechnologyTaif UniversityP.O.Box 11099Taif21944Saudi Arabia Department of Computer Sciences College of Computer and Information SciencePrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

The deployment of sensor nodes is an important aspect in mobile wireless sensor networks for increasing network *** longevity of the networks is mostly determined by the proportion of energy consumed and the sensor nodes’access *** optimal or ideal positioning of sensors improves the portable sensor networks *** and energy usage are mostly determined by successful sensor placement ***-inspired algorithms are the most effective solution for short sensor *** primary objective of work is to conduct a comparative analysis of nature-inspired optimization for wireless sensor networks(WSNs’)maximum network ***,it identifies quantity of installed sensor nodes for the given *** of algorithm has been identified based on value of optimized *** first half of the paper’s literature on nature-inspired algorithms is *** six metaheuristics algorithms(Grey wolf,Ant lion,Dragonfly,Whale,Moth flame,Sine cosine optimizer)are compared for optimal coverage of *** simulation outcomes confirm that whale opti-mization algorithm(WOA)gives optimized Energy with improved network coverage with the least number of *** comparison will be helpful for researchers who will use WSNs in their applications.

关键词： Bio-inspired computing evolutionary computation greedy algorithms wireless sensor network computational intelligence

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Wind's Data Analysis for its Accurate Prediction in Smart Grid Systems 3

Wind's Data Analysis for its Accurate Prediction in Smart Gr...

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3rd International Conference on Latest Trends in Electrical engineering and Computing Technologies, INTELLECT 2022

作者： Ashraf, Maria Gulraiz, Asif Zaidi, syed Sajjad Haider Ashraf, Farhana Khan, Bilal Muhammad Electronic and Power Engineering Pnec Nust Karachi Pakistan Sir Syed University of Engineering and Technology Department of Computer Science Karachi Pakistan

ISBN: (数字)9781665487061

ISBN: (纸本)9781665487061

There already existed the problem of unit commitment that became further complicated and accelerated by the use of renewable energy in smart grid systems. One of the major contributor of renewable energy is wind, because of the unpredictable nature of the wind, at different places the demand is variable at different times of the day. Therefore, it is crucial to predict wind as accurately as possible, as this will not only help researchers to optimize grid models but will also play a key role in managing demand and supply (ED)issues. This paper investigates real time wind's data for the city of karachi Pakistan in detail, and finds approximation of polynomial (polynomial regression) that can be further used in prediction algorithms. Furthermore, R2 and norm of residual associated with each degree of polynomial is also shown for clear *** of polynomial regression, if all alone applied in the proposed scenario is also discussed. © 2022 IEEE.

关键词： Smart power grids

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Recursive Neural Network Based Degradation Trend Estimation for Efficient Maintenance of Aerial Bundled Cables 6

Recursive Neural Network Based Degradation Trend Estimation ...

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Talha, syed Muhammad Umar Ali, syed Muhammad Ahmed, syed Sheraz Ali, Asif Mairaj, Tariq Sir Syed University of Engineering & Technology Department of Telecommunication Engineering Karachi Pakistan National University of Sciences and Technology Pakistan Navy Engineering College Department of Electrical Engineering IslamabadH-12 Pakistan

ISBN: (纸本)9798350333718

Aerial Bundle Cables (ABCs) have been used for overhead power distribution in many parts of the world. ABCs are of great interest to metropolitan areas with electrical pilferage problems, as their inherent insulation offers better protection. One such instance is M/s KE deploying ABCs in the coastal city of karachi. The ABCs experienced rapid degradation due to the moist, humid, and harsh environment of karachi. However, the degradation is not visually observable due to insulation, and in turn, compromises early detection. Non-Destructive Testing (NDT) techniques offer a solution for condition monitoring of the ABCs for degradation detection. A historical database of appropriate NDT and environmental data enables degradation trend prediction. This research work reports a degradation trend prediction scheme for ABCs using Recursive Neural Network (RNN) based Artificial Intelligence (AI) model. The reported work uses custom-Acquired real NDT data of in-service ABCs. Promising results show the efficacy of the AI-based prediction scheme. © 2023 IEEE.

关键词： Condition monitoring

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The Impact of Fog computing in the IoT World

The Impact of Fog computing in the IoT World

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Wireless and Optical Technologies (GCWOT), 2020 Global Conference on

作者： Fizzah Sohail Hira Haider Najma Ismat Software Engineering Department Sir Syed University of Engineering & Technology Karachi Pakistan Computer Engineering Department Sir Syed University of Engineering & Technology Karachi Pakistan

The rise in Internet of Things (IoT) devices with inadequate resources resulted in the emergence of the fog-computing, an extension of the cloud computing paradigm. In cloud computing, the network edge contains all fog cells that cause a reduction in the latency. It enables the implementation of IoT applications on the resources of computational Fog. IoT implementation aims to optimize the need for quality service (QoS) while maximizing fog resource utilization. Fog computing also enables a new generation of services and applications by extending the Cloud Computing paradigm to the network edge. Fog is defined with the help of the characteristics such as Mobility, location awareness and low latency, a significant number of nodes, major wireless access role, extensive geographical, prevalent real-time applications, and streaming. In this study, comprehensive taxonomy is proposed, a comparison between cloud and fog computing has been highlighted, the key features of Fog Computing, also Fog-based applications is presented in detail. Moreover, it is argued that Fog is a suitable platform for various IoT applications. These characteristics include Actuators Networks, Wireless sensors, smart cities, smart Grid, and connected vehicles. At last, the paper elaborates the potential trends of Fog Computing.

关键词： Wireless communication Cloud computing Wireless sensor networks Privacy Computational modeling Market research Internet of Things

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Implementation of Convolutional Neural Networks deep learning approach to Classify Melanoma Skin Cancer 6

Implementation of Convolutional Neural Networks deep learnin...

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6th Global Conference on Wireless and Optical Technologies, GCWOT 2023

作者： Afroz, Adnan Zia, Razia Noor, Shaheena Garcia, Andres Ortiz Shams, Sarman Mughal, Shoaib Sir Syed University Engineering and Technology Software Enginneering Department Karachi Pakistan Universidad de Malaga E.T.S Ingenieria de Telecomunicacion Malaga Spain Bahria University Computer Engineering Department Campus Karachi Pakistan

ISBN: (纸本)9798350333718

A kind of deadly skin tumor called melanoma is arguably very lethal since it accounts for the majority of skin cancer fatalities. Melanomas are often brown or black in color because melanocyte cells, which produce melanin, are the source of the disease. The DNA of skin cells is damaged by UV light, which is the main cause of melanomas. The analysis of the dermoscopy examination's findings and comparison with medical sciences are frequently used in the manual diagnosis of melanoma cancer. Human subjectivity has a strong impact on manual detection, which renders it unreliable in some circumstances. In order to classify the outcomes of the dermoscopy test and to determine the results more precisely with a comparatively shorter amount of time, computer-Assisted technology is required. Problem statement, planning, execution, and testing are the first steps in the creation of this application. To identify picture data, the research study combines deep machine learning approach using Convolutional Neural Network technique along with LeNet-5 architectural model. With a variable number of training and testing epochs and 44 photos from the training results, the experiment with the best success rate (93% in training and 100% in testing) required 176 images and 100 epochs of training data. Programming language such as Python with utilization of Keras library, which serves as the Tensorflow back-end, were used to construct this application. © 2023 IEEE.

关键词： Python

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Comparative Analysis of BERT and TF-IDF for Textual Semantic Similarity Assessment

Comparative Analysis of BERT and TF-IDF for Textual Semantic...

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IEEE International Conference on Multi Topic

作者： syed Muhammad Hasan Danish syed Muhammad Ejaz Hasnain Hamza Ashraf Rukaiya Rukaiya Computer Engineering Department Sir Syed University of Engg. & Technology Karachi Pakistan

ISBN: (数字)9798331507213

ISBN: (纸本)9798331507220

The paper presents an investigative analysis between two techniques BERT (Bidirectional Encoder Representations from Transformers) and TF-IDF (Term Frequency-Inverse Document Frequency) combined with cosine similarity. Text comparison, in general, is a fundamental aspect of NLP, plays a crucial role in a variety of applications, such as search engines, plagiarism detection, and question-answering systems. The objective is to evaluate efficiency of techniques in comparing similarity between text and to identify which one performs better. The experiments demonstrate that while BERT excels in understanding context-dependent meanings and subtle linguistic nuances, TF-IDF with cosine similarity offers computational efficiency and interpretability for specific task. Findings provide insights into the strengths and limitations of each approach, offering guidance for researchers and practitioners in selecting appropriate methodologies for content relevance assessment in diverse scenarios.

关键词： Plagiarism Semantics Bidirectional control Linguistics Search engines Transformers Encoding Computational efficiency Object recognition

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