Using Light Emitting Diodes (LEDs), Visible Light Communication (VLC) has two uses: lighting and sending data. However, fast on–off switching for data modulation causes flickering that can negatively impact human health even at imperceptible frequencies. Current flickering reduction strategies do not provide accurate measurement of flickering generated by modulation schemes such as PPM and DPPM. Worse channel conditions decrease data rates, thereby increasing flicker duration; nonetheless, earlier research disregards the trade-offs between flickering and communication performance. This study introduces a new flicker estimation technique for PPM and DPPM, examining how empty slots affect flicker. We investigate the balance among flickering rate, data rate, and BER to optimize VLC systems for both data transmission and human health. Using a threshold-based method derived from biological research, we simulate flickering. Theoretical analysis looks at the OFF cycles in PPM/DPPM symbols, and simulations compare the flickering rate, data rate, and BER for different modulation orders (4PPM to 8PPM) under different slot lengths. The findings reveal that shorter symbol durations (Ts = 2 ms), which correspond to higher data rates, generate insignificant LED flashing, whereas longer symbol durations (Ts = 10 ms), which correspond to lower data rates, increase flickering. But longer symbol durations provide better BER performance than shorter ones. DPPM also lowers the flickering rate by about 50% compared to PPM by removing unnecessary empty slots while still providing almost twice the data rate of PPM. Higher-order modulation (8PPM) lowers flickering but degrades BER (10-1 to 10-1.3), while lower-order modulation (4PPM) enhances BER (10-3 to 10-2.8) at the cost of more flickering. Moreover, to statistically validate the simulation results, each experiment was repeated 30 independent times using different random binary sequences. The mean and standard deviation of the flickering rate and BER were computed. This study offers a quantitative framework to create flicker-aware VLC systems, emphasizing DPPM for settings prone to flicker and directing modulation choice for applications where performance is critical.

Underwater Acoustic Sensor Networks (UASNs) play a critical role in underwater exploration, yet face challenges like high energy consumption, and uneven load distribution in multi-hop routing. While existing clustering protocols like Anchor Nodes assisted Cluster-based Routing Protocol (ANCRP) have improved energy-efficiency through uniform cluster formation, they still suffer from unequal energy depletion among cluster heads (CHs), particularly in shallower layers. This paper introduces key innovations that advance cluster-based routing in UASNs. We propose an Energy-Adaptive Non-uniform Clustering Protocol (EANCP) with three novel aspects: 1) depth-optimized cluster sizing where deeper CHs manage larger clusters with lower communication demands while shallower CHs handle smaller clusters to prevent early energy depletion; 2) an adaptive beaconing mechanism where transmission rate varies with depth to conserve energy in stable deep regions; and 3) a multi-criteria CH selection strategy considering residual energy, congestion, and distance to sink. The protocol was designed and simulated in Python, leveraging its scientific computing ecosystem (NumPy, SciPy, and Matplotlib) for flexible modeling of underwater acoustic channels and energy consumption. Unlike ANCRP’s uniform approach, EANCP’s depth-aware design addresses the uneven energy consumption in UASNs. Simulations demonstrate significant improvements: a 14% lower energy consumption, 11.5% longer network lifetime, $5-8\% $ higher packet delivery ratio (PDR) and 20% lower delays compared to ANCRP. The protocol particularly excels in large-scale deployment by preventing routing bottlenecks through congestion-aware forwarding. These advancements make EANCP a significant improvement in underwater routing protocols, offering a more robust solution to balancing energy efficiency with reliable communication in harsh underwater environments.

Early and precise identification is essential for improving patient prognosis because melanoma is still one of the most aggressive and deadly dermatological cancers. This paper suggests an improved deep learning framework that uses a refined Convolutional Neural Network (CNN) based on InceptionV3 to automatically classify skin lesions. The model tackles the intrinsic complexity of dermoscopic images by combining transfer learning with sophisticated data augmentation and tailored training hyperparameters. Over 100 training epochs, the method was assessed using a binary dataset of benign and malignant tumors. Metrics including accuracy, precision, sensitivity, specificity, and Area Under the Curve (AUC) were used to thoroughly evaluate performance. According to experimental data, the suggested model obtains a high classification accuracy of around 94%, with sensitivity and F1-scores of 93% respectively. Additionally, even in the presence of class imbalance, the model's resilience and discriminative capacity are confirmed by ROC and Precision-Recall assessments. When compared to baseline designs and modern state-of-the-art CNN models like ResNet50, VGG16, and EfficientNet, the proposed approach demonstrates competitive performance with improved sensitivity and specificity balance while maintaining reduced false positive and false negative predictions. By merging an enhanced InceptionV3 architecture with effective transfer learning, adaptive hyperparameter tuning, and enhanced augmentation strategies, the proposed framework provides a more balanced and dependable melanoma classification performance than conventional CNN-based methods. These results highlight the potential of the improved InceptionV3 model as a trustworthy computer-aided diagnostic (CAD) tool, giving dermatologists vital decision help in the early identification of melanoma.

Among all stages of skin cancer, Melanoma, Basel Cell Carcinoma (BCC), and Squamous Cell Carcinoma (SCC) have a significant impact on world health. Although deep learning offers promising potential for dermatological categorization, only limited disease groups have benefited, since most studies focus on particular illnesses rather than covering comprehensive human skin problems. Computerized analysis has been used in the past to identify cancer in skin lesion images, but challenges still persist mainly due to the multiple forms, textures, and sizes of lesions that complicate skin cancer classification. This research paper presents a Convolutional Neural Network (CNN) model customized to meet our requirements by using a pre trained InceptionV3 model along with Bayesian hyperparameter tuning. Using the ISIC 2024 and HAM 10000 datasets, the main objective is to classify skin lesions and differentiate between malignant Melanoma, BCC, and SCC. By implementing this customized model, the issue caused by variations in lesion appearance is effectively addressed, leading to more accurate predictions. Using Bayesian hyperparameter tuning can increase identification while decreasing computational cost. The proposed model performed strongly on the combined datasets by achieving combined average accuracy of 95.1 %, a precision of 94.42 %, a sensitivity of 97.3 %, a specificity of 98.8 %, and an F1 score of 95.7 %. These results demonstrate that the model significantly outperformed existing techniques and provided more accurate and consistent diagnosis of pigmented skin lesions compared to current standards.

The Underwater Acoustic Sensor Networks have gained significant attention because of their wide range of applications in submerged environments. However, ensuring reliable and energy-efficient communication in the submerged environment is challenging due to their distinctive characteristics such as limited energy resources, dynamic topology, extended propagation delays, and node mobility. Additionally, the void hole problem in submerged environments arises due to randomized node deployment. To curtail these issues, this paper introduces a novel way of strategically deploying the nodes based on the underwater depth parameters, which can reduce the likelihood of void hole occurrence. An optimal number of clusters based on the fixed transmission range of cluster heads is used to cater to extensive energy usage. In the proposed routing protocol, the path selection is based on the residual energy, link quality, and proximity to a higher number of nodes. Extensive simulations have been conducted by varying network parameters to analyze the network performance in terms of energy expenditure, packet delivery ratio, network throughput, number of dead nodes, and end-to-end delays. Also, the proposed work provides a performance comparison with some state-of-the-art protocols and exhibits promising results.

The Underwater Acoustic Sensor Network (UASN) is based on the sensory elements that detect the environmental conditions of submerged regions and transmit the information through acoustic waves at the sink node placed on top of the water. All these sensory elements are battery-operated just like the Terrestrial Wireless Sensor Network (TWSN). However, these nodes are not stationary, unlike the nodes in TWSN. Thus, routing with limited energy and ever-changing topology decreases the reliability of the network and increases the chances of Void existence. This research paper focuses on the comparative analysis by critically reviewing some of the state-of-the-art routing protocols which are based on the effective usability of the limited energy resource, reliability of the network, and Void occurrence issues. An analytical method is employed to examine the strengths and shortcomings of existing routing protocols, with a specific emphasis on the identified difficulties. It provides a solid foundation related to UASN routing protocols which are primarily designed with a focus on resolving issues such as limited energy, low reliability, and high chances of void occurrence. The contribution of this comparative study helps the research partners from the academy and industry which are new to this field of study and would like to contribute.

The novel coronavirus disease or COVID-19 pandemic, since its emergence in 2019, has led to severe disruptions in every field of life. The world has turned its focus toward digital technologies to fight against these socio-economic disruptions. The associated digital technology-based solutions predominantly rely on the advancements in wireless and communication technologies. These wireless technologies help in fighting the global crisis in various ways, including virus-spread monitoring, maximizing healthcare outreach, mitigating the physical barriers through automated visual inspection systems and online video conferencing solutions, and enabling distance and virtual education. However, the problem further intensifies in developing and underdeveloped countries, due to the lack of facilities, increased poverty ratio, and poor socio-economic situation. This paper reports various wireless technological solutions for the COVID-19 pandemic as experienced by one such developing country, Pakistan. The paper highlights the use of wireless technologies in various fields for fighting the COVID-19 pandemic, such as e-commerce, education, and the healthcare sector.

The Underwater Acoustic Sensor Networks has drawn the attention of researchers around the globe because of its diversified applications. The network is based on battery-operated acoustic sensor nodes therefore, it is of utmost significance to design energy efficient routing protocol. One of the major reasons for inefficient energy consumption is the imbalance utilization of energy in different areas of the underwater region which leads to hotspot issues. In this article, a novel localization-free Energy Efficient Clustering Routing Protocol based on Arithmetic Progression (EECRAP) is proposed. The underwater region logically segmented into asymmetric layers based on Arithmetic Progression. The proposed solution is based on the residual energy, layer division, and depth division of the node which can mitigate the hotspot issue and balance energy utilization in the network. For energy preservation and network longevity, an innovative energy-efficient routing path selection is presented in which the succeeding forwarder node is selected according to its residual energy, layer division, and the received signal strength. When compared with Low-Energy Adaptive Clustering Hierarchy (LEACH), Depth Based Routing (DBR), Energy Efficient Routing Protocol Based on Layers and unequal Clusters (EERBLC), and energy efficient clustering multi-hop routing protocol (EECMR), the simulated results illustrate improvements in energy usage of 40.7%, 29%, 20.25%, and 11% over LEACH, EERBLC, DBR, and EECMR respectively. Packet Delivery Ratio-wise, EECRAP is 58.44%, 55.77%, 49.87%, and 7.61% dependable than DBR, LEACH, EERBLC, and EECMR respectively. Dead nodes make EECRAP 68.7%, 23.1%, 8.7%, and 7.6% more efficient than LEACH, DBR, EERBLC, and EECMR respectively.

Due to unexplored but desirable undersea resources, Underwater Sensor Network, is developing technology and becomes a catchword for modern scientists. Underwater Acoustic Sensor Networks, also known as UASN, are employed for a broad range of underwater utilizations such as reconnaissance by the military, mineral ore detection, oil reservoir management, aquatic environment exploration, imaging marine life, and early warnings to prevent a natural disaster. Almost every application is triggering to monitor important characteristics such as the salinity concerning different levels of depth, temperature profile, pollutants, etc. However, the study of aquatic channel characteristics is required before implementing the UASN for any of the applications. This research paper presents the underwater channel characteristics by varying the acoustic signal frequency, underwater depth levels, and separation between nodes. The simulated result is generated using MATLAB. The findings in the form of graphical representations are satisfactory and may be considered for future research in UASN.

Since the beginning of the recent Covid-19 Pandemic, many countries around the globe are enforcing precautionary measures to reduce the threat that it poses to the masses. In this regard, most businesses and government offices have reduced the percentage of physical working personnel to less than half and they are allowed to work from home and communicate with each other via video conferencing. All multimedia content requires a good deal of bandwidth as the information they generate is immense, this may put a lot of strain on an available channel as its utilization has increased tenfold. It is possible to apply a sound and computationally inexpensive compression technique to reduce the size of the streaming video content. In this paper, a compression algorithm is proposed that incorporates Temporal Masking on the motion vectors extracted from the video stream. Two consecutive frames extracted from the same video sequence are used to acquire motion vectors and residual frames on which the Temporal Masking is applied using the masking parameter ( q ). Temporal Masking makes use of the temporal redundancy that every video content possesses and masks the motion that a human eye is unable to notice in real-time. If the information is over-compressed, it may induce distortion artifacts such as blockiness, blurriness, etc. To verify the integrity of the proposed method, a few samples of a lectured video sequence are compressed using both the conventional means of compression and by applying Temporal Masking. Structural similarity index (SSIM) and peak signal to noise ratio are the key quality measuring parameters used to evaluate the integrity of the proposed method.

The term Machine Learning is broadly used in the last two decades. It makes much rapid progress in the area of machine vision. Just because of the arrival of Convolution Neural Networks the computer vision gets much better accuracy as compared to classical Machine Learning algorithms. The arrival of Neural Nets helps in complex classification and detection from the image. In this work, we investigate the VGGNet model which was originally proposed by Oxford University. The VGG-Net architecture is very large and comprises a large space of memory and computational power and it has many layers. The reduced three layers are developed that comprise of 5 x 5 convolution filters which also decrease computational power and memory consumption. Our model is software-based in which we used NVidia GPU for the training of our model that achieve a great accuracy in between 98% to 99% for different types of leaves classes. Our proposed model helps in the field of botany to classify different species of plants leaf and helps in the study of plants leaf. If there were no specialists in the field of botanists then our software-based model helps to classify which type of leaf is this.

Underwater Acoustic Sensor Network (UASN) is a rapidly growing technology and a buzzword among recent researchers due to unreached, hidden, and desirable underwater resources. Like other technologies, UASN also has some challenges which include low propagation delay, free-floating underwater sensor nodes, low bandwidth, and reliability of data delivery. Among all these challenges, efficient utilization of energy from sensor nodes during data transmission and prolongation of network lifetime is the topmost challenge. Several researchers have made substantial contributions to address it by designing energy efficient routing protocols. This research paper focuses on presenting some of the significant work performed in this area. In this research paper, some of the recently developed cluster-based routing protocols are reviewed. A critical review approach is adopted to see existing routing protocols strengths and weaknesses and focus on the issues. The performance factors which are considered and ignored, reviewed in this research paper. In the end, some open areas are discussed for potential research work. This research paper is helpful for the researchers from academic circles and industries who are new in this research field and want to contribute by designing energy efficient clustered based routing protocol to mitigate excessive power consumption by Underwater Acoustic Sensor Networks.

Study of skin disease images through human effort, for detection of skin cancer, has always been a very difficult task. Distinguishing and manually analyzing skin lesions for detection of melanoma can be tedious as it is a lengthy process. Computational resources have advanced and are progressing in an innovative way, due to which the analysis of skin diseases, especially skin lesion, has been made easier by the help of innumerable AI strategies. The outcomes of such models, showed after implementation, are quite impressive but the drawbacks of these models include the failure in recognizing some of the skin lesion problems due to the complex skin lesion images. A complete study of procedures for distinguishing skin diseases from a healthy skin is presented in this work. This survey study will help examiners in creating effective models that automatically identify diseased skin from healthy skin images. Firstly, the difficulties in identifying skin tumor from skin images are recognized. Secondly, the pre-processing and segmentation techniques in determining various skin lesions are discussed. Thirdly, latest research comparisons are presented. Fourth, different methods for classification of skin lesion in various categories of skin tumor are examined. Lastly, the segmentation and classification process applying latest machine learning techniques utilized in well-known skin disease images examination are investigated and difficulties of skin disease analysis using ISIC 2018 and 2019 dataset are outlined.

Wireless Communication Technologies has completely revolutionized the world. Wireless Communication Technologies provide ease to the users such as portability of the devices and mobile access to the internet. These portable wireless devices include PDAs, laptops, smart phones etc. offers some valuable features. These features include accessing the e-mails, SMS, MMS, calendars, addresses, phone numbers list and the internet. These entire devices store large amount of data and their wireless connection to network spectrum exhibit them as important source of computing. These devices are always vulnerable to attacks. Mobile devices are the new frontier for viruses, spam and other potential security threats. All these viruses, spam, Trojans and worms are out there in our vicinity to gain access to our personal data. This research aims to analyze the threats of viruses in the wireless communication systems and security including their role in the service outbreak, laying down the possible scenarios and also identifying possible remedies.

This energy ramp is an extensive approach in the field of alternativerenewableenergy.It is a mechanismtoproduce electricity by harnessing the kinetic energy of vehicles that drives over the ramp.The objective is to design a system that decreases the energy crisis in Pakistan by utilizing the vehicles kinetic energy.The system can be implemented just before or just after the entrance of e.g.Tool Plazas, Hospitals, U-turns, Airports etc.

To evaluate the feasibility of using recombinant human TSH (rhTSH) in conjunction with 131I to treat patients with differentiated thyroid carcinoma.

Juvenile differentiated carcinoma thyroid is a rare entity. It differs from adult differentiated thyroid carcinoma in a variety of ways, including large tumor volume at presentation with early involvement of the capsule, more frequent nodal and distant metastases, greater expression of sodium-iodide symporter and early recurrence. The overall survival seems to be better than for adult patients; however, due to high and early recurrence rates, prompt and adequate treatment is advocated. The mainstay of treatment includes total thyroidectomy, central lymphadenectomy with modified radical lateral lymphadenectomy, followed by ablation with radioactive iodine (RAI). Both modalities improve the final outcome, but RAI ablation decreases cause-specific death risk independent of the extent of surgery. We present the case of a 5-year-old girl, the youngest ever treated in our country with surgery and RAI therapy successfully after being diagnosed as papillary carcinoma of the thyroid, follicular variant.

RAT BITE FEVER is a rare systemic febrile illness caused by Streptobacillus moniliformis that can be transmitted by the bite of a rat or a small rodent or ingestion of food or water contaminated with rat feces [1]. The diagnosis can often be missed if health care providers fail to obtain a careful and extensive patient history. We report an unusual case of rat bite fever in a 19-year-old college student who presented with a fever and a rash. Case report A 19-year-old female college student presented to the emergency department in July because of fever, rash, and bilateral ankle and shoulder pain of approximately 6 days’ duration. Her medical history was unremarkable. She was living alone in an apartment in Syracuse, New York, and denied any recent travel outside the area over the previous several months. She was not sexually active and denied using drugs. She was taking no medications. Her immunizations were up to date. At the time of physical examination her temperature was 103°F. Examination of the head, eyes, ears, and throat revealed nothing remarkable. The neck was supple. There were no enlarged lymph nodes palpable. The lungs were clear and there was no heart murmur. Examination of the extremities revealed symmetrical swelling of her hands, shoulders, and ankles. The skin revealed a petechial rash over the hands, palms, ankles, and soles of her feet (Figs. 1, 2). A few of the lesions on her hands and feet were papulopustular. Pelvic examination findings were normal.FIGURE 1.: Petechial lesions located on the palms of the hands.FIGURE 2.: Petechial lesions located on the ankles and feet.Laboratory studies revealed a normal complete blood cell count, platelet count, and differential, and findings of chest radiography and serum chemistry were normal. A throat culture was negative. Vaginal culture for gonorrhea was negative, and a GenProbe assay (GenProbe, San Diego, CA) of an endocervical specimen was negative for chlamydia. Urinalysis was negative. Antinuclear antibody, rapid plasma reagin, and rheumatoid factor testing were negative, and the erythrocyte sedimentation rate was normal. Two sets of blood cultures were performed, and ampicillin–sulbactam (3 g intravenously every 6 hours) was administered. Over the next 2 days her fever resolved. A skin biopsy was performed, which revealed leukocytoclastic vasculitis with focal epidermal necrosis (Fig. 3). Immunofluorescent staining revealed C3 deposition along papillary dermal vessels and along the dermal–epidermal junction (Fig. 4). There was no immunoglobulin deposition. Five days after admission the blood cultures yielded a gram-negative rod that was later identified as Streptobacillus moniliformis.FIGURE 3.: Hematoxylin–eosin staining of skin biopsy specimen showing leukocytoclastic vasculitis with focal epidermal necrosis.FIGURE 4.: Immunofluorescence staining of skin biopsy specimen showing C3 deposition along papillary dermal vessels and along the dermal-epidermal junction, without evidence of immunoglobulin deposition.Further questioning of the patient’s mother revealed she lived with 2 cats, 2 hissing cockroaches, 3 African frogs, 2 lizards, 1 mouse, and 10 rats. One of the rats bit her approximately 1 week prior to the onset of her symptoms. The rash improved, and after 7 days of intravenous antibiotic therapy the patient was discharged to her mother’s care with oral amoxicillin, to complete a 14-day course. A health department referral was initiated. She was seen for follow-up 2 weeks after discharge and was well. Discussion Rat bite fever is rare in the United States, and accurate data about incidence rates are unavailable because the disease is not reportable in any state [1]. Most cases in the United States are caused by S. moniliformis acquired through rat bites or scratches [2]. The rate of nasopharyngeal carriage in healthy laboratory rats ranges from 10% to 100%, and that among wild rats is 50% to 100% [1,2]. Bites from mice, squirrels, and gerbils and exposure to animals that prey on these rodents (e.g., cats and dogs) have been associated with cases of rat bite fever [2]. There have also been cases reported in which indirect contact, such as living in dwellings that are rat-infested or ingestion of water or food contaminated with rat feces, resulted in S. moniliformis bacteremia [3]. Contamination of drinking water and raw milk has been linked to other cases [4]. In 1996 two cases were linked to common exposures to the same dog and to consumption of surface water that could have been contaminated with rat feces [5]. The incubation period of rat bite fever caused by S. moniliformis can range from 1 to 22 days. Onset usually occurs 2 to 10 days after the rat bite. Clinical presentation is characterized by relapsing fever and asymmetric polyarthritis, followed by a maculopapular rash within 2 to 4 days on the extremities, palms, and soles. Other manifestations reported include headache, nausea, vomiting, myalgias, lymphadenopathy, endocarditis, meningitis, pneumonia, and focal abscess [5]. Thirteen percent of untreated cases are fatal, but most cases resolve spontaneously within 2 weeks [5]. Rat bite fever due to Spirillum minus occurs most commonly in Asia and has a longer incubation period, of about 1 to 3 weeks [5]. Diagnosis is made by blood culture only. The organism has strict growth requirements. It is slow-growing, and unless the laboratory is notified that Streptobacillus moniliformis is suspected, it is difficult for most laboratories to culture it [6]. Serologic testing is no longer available. Recommended treatment is with intravenous penicillin for 5 to 7 days, followed by treatment with oral penicillin for an additional 7 days. Tetracycline is an alternative agent that can be used when there is a history of penicillin allergy. There is limited experience with erythromycin, clindamycin, and ceftriaxone. The pet history was not obtained until late in the course of this patient’s hospital stay. Initially, the patient denied having any pets at home because she was fearful they would be confiscated. The importance of obtaining a thorough history for a patient who presents with a fever and rash cannot be overemphasized.