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GWWAES: a hybrid optimization method to secure IoT networks

International Journal of Information Technology (Singapore) 2025年 1-7页

作者： Kumar, Sanjeev Deora, Sukhvinder Singh Department of Computer Science and Application MDU Rohtak India

Internet of Things (IoT) weaves together numerous material objects, reshaping industries such as healthcare, farming, and manufacturing. But there are some problems, such as their rates increasing with the growth of the network connected with the navigation of the data. Most traditional optimization approaches fail to efficiently strike the right chord between performance and security in IoT architectures, particularly when the network is low and computationally constrained. This paper presents GWWAES as a new hybrid optimization approach based on grey wolf optimization (GWO) and the whale optimization algorithm (WOA) for routing and task scheduling in conjunction with the advanced encryption standard (AES). This method tackles two problems related to the IoT wrapper, namely efficient communication within the network and data protection. MATLAB simulation proves that the proposed GWWAES method provides better results than existing methods and successfully minimizes the message delay, increases the throughput, and decreases the overhead in terms of key size and various traffic conditions. In addition to this, the proposed method attains the highest network efficiency (96.68%) by securing data transmission in IoT networks. © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： AES GWO IoT Routing and Encryption WOA

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Optimized Dense Neural Network Architecture for Bone Marrow Cancer Classification Using Custom Callbacks

Optimized Dense Neural Network Architecture for Bone Marrow ...

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2025 International Conference on Multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Deepa, G. Kalpana, Y. Department of Computer Science Chennai India Department of Computer Application and It Chennai India

ISBN: (纸本)9798331509828

Classification of bone marrow cancer is a crucial step in the analysis of several hematological cancers, such as multiple myeloma and leukemia. This study shows better dense neural network (DNN) architecture for significant the variation among the five main types of bone marrow cells includes lymphocytes, myelocytes, band neutrophils, and plasma cells. To find and diagnose bone marrow cancers like multiple myeloma, acute myeloid leukemia (AML), and acute lymphoblastic leukemia (ALL) early on, it is important to be able to accurately identify these cell types. The suggested DNN model uses unique callbacks to optimize training, avoid overfitting, and guarantee faster convergence. These callbacks include learning rate scheduling, early pausing, and dynamic weight modifications. The system preprocesses the high-resolution bone marrow image database to enhance contrast, adapt to fluctuations in illumination, and utilize dense layers for cell classification and convolutional layers for feature extraction. After extensive hyperparameter tuning, a model that classified these five categories of bone marrow cells obtained an overall accuracy rate of 95%. In terms of accuracy, recall, and F1 scores, the model performs better than conventional machine learning techniques while retaining computing economy. The model is able to increase training times without sacrificing classification performance by incorporating custom callbacks. With its enormous clinical application potential, our improved DNN architecture offers pathologists a dependable and effective tool for accurately classifying bone marrow cells. By improving bone marrow cancer diagnosis accuracy, this approach can improve patient care and treatment results. © 2025 IEEE.

关键词： Lung cancer

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Integrated fuzzy-based gamma adaptive for image enhancement with color attenuation prior

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Multimedia Tools and applications 2025年第12期84卷 11249-11267页

作者： Harne, Sachin Choubey, Siddhartha Choubey, Abha Department of Computer Application Shri Shankaracharya Group of Institution Chhattisgarh Bhilai India Department of Computer Science and Engineering Shri Shankaracharya Group of Institution Chhattisgarh Bhilai India

Images Quality in the Transportation industry is considered the most significant term and gains more attention among the higher authorities and researchers. Therefore, the accuracy level of the image has been evaluated with IQA (Image Quality Assessment). Most images are substantially affected by ambient lights that affect the quality of the image. In the existing research, the HVS (Human Visual System) has been utilized in order to identify the quality of images that occurred in both night and day time that affects the loss of luminance and information. In the current research, we proposed an integrated fuzzy-based Gamma Adaptive enhanced with Color Attenuation prior (IFGECAP) techniques that help capture the image's local characteristics. Eventually, the performance evaluation of the image whereas both input and output, has been evaluated with Natural image quality evaluator (NIQE), Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), structural similarity index measure (SSIM), Signal-to-noise ratio (SNR), and Peak signal-to-noise ratio (PSNR). Hence, the fog removal will be classified as seasonal, such as rainy and foggy, utilizing CNN—Convolutional Neural Network. The proposed hybrid technique—IFGECAP, procured an efficient result when compared with the existing techniques. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Signal to noise ratio

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Proposing a digital twin-based sustainable water governance system for rural Indian villages

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International Journal of Information Technology (Singapore) 2025年第3期17卷 1777-1783页

作者： Singh, Dineshkumar Sharma, Vishnu Department of Computer Science and Engineering Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India Department of Computer Science and Application Faculty of Computer Science & Engineering (FCE) Poornima University Jaipur India

A sustainably governed water-ecosystem at village-level is crucial for the community's well-being. It requires understanding natures’ limits to store and yield water and balance it with the stakeholders’ needs, in the context of population growth, climate change, and unsustainable agricultural practices. Maharashtra villages like Randullabad and Karanjkhop are impacted by limited groundwater availability due to the geological structure of the Deccan Traps basaltic rock formation and are recharged by the most rainfall during the monsoon season. Traditional water management practices are inadequate, with old wells causing conflicts among villagers due to fluctuating water levels. In order to achieve a sustainable water system for rural Indian villages, this paper proposes a Digital Twin-Based Water Governance System (DToWGS) with the aim of balancing nature's boundaries with stakeholders' interests to provide real-time water insights, encourage community engagement, transparency in water allocation and decision-making, and promote sustainable practices. These measured indicators can potentially link local governance outcomes to global goals like Viksit Bharat 2047 and UN Sustainable Development Goal 6 (SDG 6). © Bharati Vidyapeeth's Institute of computer applications and Management 2025.

关键词： Digital twin Empathy map Remote sensing SDG6 Stakeholders Village water environment

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A deep learning approach using modified Xception net for oral malignancy detection using histopathological images of oral mucosa

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International Journal of Applied Decision sciences 2025年第2期18卷 168-188页

作者： Das, Madhusmita Dash, Rasmita Department of Computer Application Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan Deemed to be University Odisha Bhubaneswar India

The early detection of oral malignancy by physicians is a strenuous task. The analysis of histopathological oral malignancy images using image processing and deep learning techniques can be an add-on facility for doctors to diagnose oral cancer. In this work, a deep learning model is used, designing a modified Xception net with swish activation function and generalised mean pool for the detection of oral malignancy. To prove the superiority of the model, three stages of comparative analysis are carried out. In the first stage, the model is compared with a few advanced models explicitly Alexnet, Resnet50, Resnet101, VGG16, VGG19, Inception net and original Xception. In the second stage, loss and accuracy graphs analysis is done and in the third stage, the proposed model’s accuracy is compared with other model’s accuracy available in the literature. It is found that the modified Xception net got upgraded performance by an accuracy of 98.97%. Copyright © 2025 Inderscience Enterprises Ltd.

关键词： Lung cancer

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ADNeuroNet: a neuroevolution-based neural network algorithm for the diagnosis of neurodegenerative diseases

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Neural Computing and applications 2025年第14期37卷 8593-8627页

作者： Khan, Afreen Zubair, Swaleha Ali, Irfan Department of Computer Application Faculty of Engineering & IT Integral University Lucknow226026 India Department of Statistics and Operations Research Aligarh Muslim University Aligarh India Department of Computer Science Aligarh Muslim University Aligarh India

Neurodegenerative disorders such as dementia and Alzheimer’s disease (AD) have adversely devastated the health and well-being of the older community. Given that early detection might help prevent or delay cognitive disorders associated with AD, developing an effective diagnostic technique is deemed appropriate to control the disease. Despite advances in clinical diagnostic standards and treatment techniques, the global prevalence of cognitive disturbance and behavioral problems remains steep. Besides the above challenge, the scarcity of AD-related open-source raw data prompted us to tailor ADNeuroNet, a neuroevolution-based neural network (NN) designed for predicting cognitively normal, mild cognitive impairment, and AD instances. To construct a robust NN algorithm that can predict the disease with high accuracy and improved efficiency, we worked on the combined cognitive and demographic clinical data to create a comprehensive model. Primarily, the Alzheimer’s Disease Neuroimaging Initiative (ADNI) repository was used to develop this program. We employed three distinct datasets and developed a predictive model that achieved the maximum performance accuracy of 93.42%. Utilizing only baseline details, the as-developed model successfully diagnosed and predicted neurodegenerative disorders and is likely to emerge as an effective clinical tool in future endeavors. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Disease control

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Utilization of Two Cross Two Switch for Bitonic Sorting Circuit

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Micro and Nanosystems 2025年第1期17卷 45-57页

作者： Bhattacharjee, Subhasree Sarkar, Soumyadip Das, Kunal Sadhu, Arindam Sarkar, Bikramjit Department of Computer Application Narula Institute of Technology Kolkata India Department of Computer Science Acharya Prafulla Chandra College Kolkata India Department of Electronics & Communication Engineering Greater Kolkata College of Engineering and Management West Bengal India Department of Computer Science & Engineering JIS College of Engineering West Bengal India

Background: The synthesis of reversible logic has gained prominence as a crucial research area, particularly in the context of post-CMOS computing devices, notably quantum computing. Objective: To implement the bitonic sorting circuit, 2x2 switch, is used. The overarching objective of implementing a reversible sorting circuit is to mitigate power consumption while promising secure communication. The successful implementation of the bitonic sorting circuit utilizing the proposed 2x2 switch is validated through simulations conducted in IBM Qiskit. Methods: This study begins by employing the control swap gate to implement a 2x2 switch, aimed at enabling concurrent computing operations within multiprocessor systems to enhance throughput. Subsequently, following the successful implementation of the 2x2 switch, the study proceeds to design a bitonic sorting circuit. Results: Bitonic sort using a 2x2 switch on the IBM Quantum Experience (IBMQ) platform via the QISKit SDK in Python 3.6. Specifically, Qiskit v0.29.0 is utilized, with OpenQASM. OpenQASM serves as the quantum assembly and instruction language. Conclusion: This work presents a methodology for constructing a 2x2 switch, utilizing a reversible control swap gate as its core component. Additionally, it demonstrates the intricate development and implementation of a bitonic sorting circuit, capitalizing on this innovative switch architecture. © 2025 Bentham science Publishers.

关键词： Quantum computers

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Framework for classifying gait disorders and fall prevention

Framework for classifying gait disorders and fall prevention

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2025 International Conference on computer, Electrical and Communication Engineering, ICCECE 2025

作者： Saha, Rupa Karmakar, Koushik Biswas, Suparna Narula Institute of Technology Department of Computer Application India Narula Institute of Technology Department of Computer Science and Technology India Department of Computer Science & Engineering Makaut W B India

ISBN: (纸本)9798331513894

One of the crucial approaches that use tracking and analyzing human movement for the identification of functional disorders and illnesses of the human body is known as the gait analysis method. This method helps diagnose neurological and musculoskeletal conditions. It also tracks the effectiveness of rehabilitation. However, conventional techniques for analyzing gait frequently depend on costly items and methods like fixed lab apparatus, force plates, and motion capture devices. But new developments in wearable sensor technology and machine learning provide new methods that are cheaper, more affordable, portable, and easily accessible. It continuously tracks gait in real-world settings. In this work, the use of wearable sensors for gait analysis, which includes devices like magnetometers, gyroscopes, and accelerometers, is examined. They are integrated into gadgets like smartphones, insoles, and smartwatches and offer real-time kinematic data that machine learning algorithms can analyze. Some of the important gait parameters 1that are extracted from sensor data are stride length, cadence, and gait symmetry. Machine learning is used to process this data and use it for categorization and gait analysis. The suggested method improves patient monitoring outside of clinical settings. It supports individualized rehabilitation plans and also identifies early indicators of gait abnormalities. The use of the wearable sensor-based gait analysis may emerge as a game-changing medical tool. It offers constant, discrete monitoring and prompts intervention in gait-related disorders. This works explains it thoroughly. This report offers insights for upcoming researchers in the field by surveying the state of the art in wearable sensor-based gait analysis. © 2025 IEEE.

关键词： Wearable sensors

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Fall Detection and Alerts in Hearing Aids for Deaf using Generative AI 2

Fall Detection and Alerts in Hearing Aids for Deaf using Gen...

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2nd International Conference on Computational Intelligence, Communication Technology and Networking, CICTN 2025

作者： Tripathi, Bharat Lal Saini, Madan Jain, Manish Saxena, Apoorva Pal, Narendra Department of Computer Application Allenhouse Institute of Technology Kanpur India Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab India Department of Computer Application Allenhouse Business School Kanpur India Department of Computer Science & Engineering Axis Institute of Technology & Management Kanpur India Department of Computer Science & Engineering Invertis University Bareilly India

ISBN: (纸本)9798331530389

Individuals with sensorineural hearing loss often experience difficulty comprehending speech when background noise is present. This paper investigates the extent of this problem in various listening scenarios and with different types of background sounds. Additionally, the section explores the contributing factors to this challenge such as reduced audibility, reduced frequency selectivity, loudness recruitment, and dead regions in the cochlea where there are no surviving inner hair cells and or neurons. The sections evaluate different techniques for compensating for the effects of these factors. While signal-processing methods using the output of a single microphone to compensate for reduced frequency selectivity have had limited success, techniques utilizing multiple microphones have proven effective. Amplitude compression has also been effective in compensating for the effects of loudness recruitment, enabling individuals to understand speech across a wide range of sound levels. Next, the paper delves into the technical aspects of implementing fall detection systems in hearing aids, learning algorithms are also investigated for their potential in enhancing fall detection accuracy and minimizing false alarms. Furthermore, the importance of user-friendly interfaces and customization features in ensuring the effectiveness and acceptance of fall detection alerts is also highlighted. The paper also addresses the ethical and privacy considerations associated with fall detection in hearing aids. It emphasizes the need for transparent data handling practices and user consent, while also addressing potential concerns related to user autonomy and intrusion. © 2025 IEEE.

关键词： Data privacy

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Security-Aware Optimal Cluster Head-Based Energy-Efficient Data Transmission on Wireless Sensor Network

Security-Aware Optimal Cluster Head-Based Energy-Efficient D...

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International Conference on Business Intelligence and Data Analytics, BIDA 2024

作者： Bharath, A.D. Revathy, N. Ph.D Research scholar Department of Computer Science Hindusthan College of Arts and Science Coimbatore India Professor Department of Computer Application Hindusthan College of Arts and Science Coimbatore India

ISBN: (纸本)9789819777167

Wireless sensor networks, or WLANs, are the networks most essential to the development of many Internet of Things (IoT)-based applications. These intelligent gadgets do, however, have limitations in processing, memory, energy, and calculation. Moreover, attaining Quality of Service (QoS) with secure data transfer is one of the main obstacles for WSNs. To improve security and quality of service while using less energy in wireless sensor networks (WSNs), this paper suggests effective energy-aware secure data transmission models. Initially, the network randomly deploys the sensor nodes (SNs), and they register with the base station (BS). Next, the Low-energy Adaptive Clustering Hierarchy (LEACH) process is applied to choose the cluster head. Once the cluster head has been selected, the clusters are organised according to the cluster head. After that, the Enhanced Coronavirus Mask Protection Algorithm (ECMPA) is used to enable the best routing procedure. The Oppositional Function (OF) and Coronavirus Mask Protection Algorithm (CMPA) are combined in the suggested technique. This hybrid algorithm takes into account multiple measurements and the secure trust parameter to offer the best possible routing process. The data are then sent using the shortest path possible. The model’s performance efficiency above current methods for security, quality of service, and energy efficiency (EE) are demonstrated by the findings. NS2 is used in the implementation of the suggested method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Coronavirus

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